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10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses.  We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.

Walmart Sales Forecasting Data Science Project

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Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.

So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science  at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

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iii) Packing Optimization 

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering. 

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

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Let us explore data analytics case study examples in the entertainment indusry.

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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience. 

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics. 

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn 

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

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9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used. 

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato. 

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time. 

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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Data Analytics Case Study: Complete Guide in 2024

What are data analytics case study interviews.

When you’re trying to land a data analyst job, the last thing to stand in your way is the data analytics case study interview.

One reason they’re so challenging is that case studies don’t typically have a right or wrong answer.

Instead, case study interviews require you to come up with a hypothesis for an analytics question and then produce data to support or validate your hypothesis. In other words, it’s not just about your technical skills; you’re also being tested on creative problem-solving and your ability to communicate with stakeholders.

This article provides an overview of how to answer data analytics case study interview questions. You can find an in-depth course in the data analytics learning path .

How to Solve Data Analytics Case Questions

Check out our video below on How to solve a Data Analytics case study problem:

With data analyst case questions, you will need to answer two key questions:

• What metrics should I propose?
• How do I write a SQL query to get the metrics I need?

In short, to ace a data analytics case interview, you not only need to brush up on case questions, but you also should be adept at writing all types of SQL queries and have strong data sense.

These questions are especially challenging to answer if you don’t have a framework or know how to answer them. To help you prepare, we created this step-by-step guide to answering data analytics case questions.

We show you how to use a framework to answer case questions, provide example analytics questions, and help you understand the difference between analytics case studies and product metrics case studies .

Data Analytics Cases vs Product Metrics Questions

Product case questions sometimes get lumped in with data analytics cases.

Ultimately, the type of case question you are asked will depend on the role. For example, product analysts will likely face more product-oriented questions.

Product metrics cases tend to focus on a hypothetical situation. You might be asked to:

Investigate Metrics - One of the most common types will ask you to investigate a metric, usually one that’s going up or down. For example, “Why are Facebook friend requests falling by 10 percent?”

Measure Product/Feature Success - A lot of analytics cases revolve around the measurement of product success and feature changes. For example, “We want to add X feature to product Y. What metrics would you track to make sure that’s a good idea?”

With product data cases, the key difference is that you may or may not be required to write the SQL query to find the metric.

Instead, these interviews are more theoretical and are designed to assess your product sense and ability to think about analytics problems from a product perspective. Product metrics questions may also show up in the data analyst interview , but likely only for product data analyst roles.

TRY CHECKING: Marketing Analytics Case Study Guide

Data Analytics Case Study Question: Sample Solution

Let’s start with an example data analytics case question :

You’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5, where 5 is high relevance, and 1 is low relevance.

Each row in the search_events table represents a single search, with the has_clicked column representing if a user clicked on a result or not. We have a hypothesis that the CTR is dependent on the search result rating.

Write a query to return data to support or disprove this hypothesis.

search_results table:

search_events table

Step 1: With Data Analytics Case Studies, Start by Making Assumptions

Hint: Start by making assumptions and thinking out loud. With this question, focus on coming up with a metric to support the hypothesis. If the question is unclear or if you think you need more information, be sure to ask.

Answer. The hypothesis is that CTR is dependent on search result rating. Therefore, we want to focus on the CTR metric, and we can assume:

• If CTR is high when search result ratings are high, and CTR is low when the search result ratings are low, then the hypothesis is correct.
• If CTR is low when the search ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.

Step 2: Provide a Solution for the Case Question

Hint: Walk the interviewer through your reasoning. Talking about the decisions you make and why you’re making them shows off your problem-solving approach.

Answer. One way we can investigate the hypothesis is to look at the results split into different search rating buckets. For example, if we measure the CTR for results rated at 1, then those rated at 2, and so on, we can identify if an increase in rating is correlated with an increase in CTR.

First, I’d write a query to get the number of results for each query in each bucket. We want to look at the distribution of results that are less than a rating threshold, which will help us see the relationship between search rating and CTR.

This CTE aggregates the number of results that are less than a certain rating threshold. Later, we can use this to see the percentage that are in each bucket. If we re-join to the search_events table, we can calculate the CTR by then grouping by each bucket.

Step 3: Use Analysis to Backup Your Solution

Hint: Be prepared to justify your solution. Interviewers will follow up with questions about your reasoning, and ask why you make certain assumptions.

Answer. By using the CASE WHEN statement, I calculated each ratings bucket by checking to see if all the search results were less than 1, 2, or 3 by subtracting the total from the number within the bucket and seeing if it equates to 0.

I did that to get away from averages in our bucketing system. Outliers would make it more difficult to measure the effect of bad ratings. For example, if a query had a 1 rating and another had a 5 rating, that would equate to an average of 3. Whereas in my solution, a query with all of the results under 1, 2, or 3 lets us know that it actually has bad ratings.

Product Data Case Question: Sample Solution

In product metrics interviews, you’ll likely be asked about analytics, but the discussion will be more theoretical. You’ll propose a solution to a problem, and supply the metrics you’ll use to investigate or solve it. You may or may not be required to write a SQL query to get those metrics.

We’ll start with an example product metrics case study question :

Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.

The company has been consistently growing new users in the city from January to March.

What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?

Step 1: Ask Clarifying Questions Specific to the Case

Hint: This question is very vague. It’s all hypothetical, so we don’t know very much about users, what the product is, and how people might be interacting. Be sure you ask questions upfront about the product.

Answer: Before I jump into an answer, I’d like to ask a few questions:

• Who uses this social network? How do they interact with each other?
• Has there been any performance issues that might be causing the problem?
• What are the goals of this particular launch?
• Has there been any changes to the comment features in recent weeks?

For the sake of this example, let’s say we learn that it’s a social network similar to Facebook with a young audience, and the goals of the launch are to grow the user base. Also, there have been no performance issues and the commenting feature hasn’t been changed since launch.

Step 2: Use the Case Question to Make Assumptions

Hint: Look for clues in the question. For example, this case gives you a metric, “average number of comments per user.” Consider if the clue might be helpful in your solution. But be careful, sometimes questions are designed to throw you off track.

Answer: From the question, we can hypothesize a little bit. For example, we know that user count is increasing linearly. That means two things:

• The decreasing comments issue isn’t a result of a declining user base.
• The cause isn’t loss of platform.

We can also model out the data to help us get a better picture of the average number of comments per user metric:

• January: 10000 users, 30000 comments, 3 comments/user
• February: 20000 users, 50000 comments, 2.5 comments/user
• March: 30000 users, 60000 comments, 2 comments/user

One thing to note: Although this is an interesting metric, I’m not sure if it will help us solve this question. For one, average comments per user doesn’t account for churn. We might assume that during the three-month period users are churning off the platform. Let’s say the churn rate is 25% in January, 20% in February and 15% in March.

Step 3: Make a Hypothesis About the Data

Hint: Don’t worry too much about making a correct hypothesis. Instead, interviewers want to get a sense of your product initiation and that you’re on the right track. Also, be prepared to measure your hypothesis.

Answer. I would say that average comments per user isn’t a great metric to use, because it doesn’t reveal insights into what’s really causing this issue.

That’s because it doesn’t account for active users, which are the users who are actually commenting. A better metric to investigate would be retained users and monthly active users.

What I suspect is causing the issue is that active users are commenting frequently and are responsible for the increase in comments month-to-month. New users, on the other hand, aren’t as engaged and aren’t commenting as often.

Step 4: Provide Metrics and Data Analysis

Hint: Within your solution, include key metrics that you’d like to investigate that will help you measure success.

Answer: I’d say there are a few ways we could investigate the cause of this problem, but the one I’d be most interested in would be the engagement of monthly active users.

If the growth in comments is coming from active users, that would help us understand how we’re doing at retaining users. Plus, it will also show if new users are less engaged and commenting less frequently.

One way that we could dig into this would be to segment users by their onboarding date, which would help us to visualize engagement and see how engaged some of our longest-retained users are.

If engagement of new users is the issue, that will give us some options in terms of strategies for addressing the problem. For example, we could test new onboarding or commenting features designed to generate engagement.

Step 5: Propose a Solution for the Case Question

Hint: In the majority of cases, your initial assumptions might be incorrect, or the interviewer might throw you a curveball. Be prepared to make new hypotheses or discuss the pitfalls of your analysis.

Answer. If the cause wasn’t due to a lack of engagement among new users, then I’d want to investigate active users. One potential cause would be active users commenting less. In that case, we’d know that our earliest users were churning out, and that engagement among new users was potentially growing.

Again, I think we’d want to focus on user engagement since the onboarding date. That would help us understand if we were seeing higher levels of churn among active users, and we could start to identify some solutions there.

Tip: Use a Framework to Solve Data Analytics Case Questions

Analytics case questions can be challenging, but they’re much more challenging if you don’t use a framework. Without a framework, it’s easier to get lost in your answer, to get stuck, and really lose the confidence of your interviewer. Find helpful frameworks for data analytics questions in our data analytics learning path and our product metrics learning path .

Once you have the framework down, what’s the best way to practice? Mock interviews with our coaches are very effective, as you’ll get feedback and helpful tips as you answer. You can also learn a lot by practicing P2P mock interviews with other Interview Query students. No data analytics background? Check out how to become a data analyst without a degree .

Finally, if you’re looking for sample data analytics case questions and other types of interview questions, see our guide on the top data analyst interview questions .

FOR EMPLOYERS

Top 10 real-world data science case studies.

Aditya Sharma

Aditya is a content writer with 5+ years of experience writing for various industries including Marketing, SaaS, B2B, IT, and Edtech among others. You can find him watching anime or playing games when he’s not writing.

Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

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Data Analytics Case Study Guide 2024

by Sam McKay, CFA | Data Analytics

Data analytics case studies reveal how businesses harness data for informed decisions and growth.

For aspiring data professionals, mastering the case study process will enhance your skills and increase your career prospects.

So, how do you approach a case study?

Use these steps to process a data analytics case study:

Understand the Problem: Grasp the core problem or question addressed in the case study.

Collect Relevant Data: Gather data from diverse sources, ensuring accuracy and completeness.

Apply Analytical Techniques: Use appropriate methods aligned with the problem statement.

Visualize Insights: Utilize visual aids to showcase patterns and key findings.

Derive Actionable Insights: Focus on deriving meaningful actions from the analysis.

This article will give you detailed steps to navigate a case study effectively and understand how it works in real-world situations.

By the end of the article, you will be better equipped to approach a data analytics case study, strengthening your analytical prowess and practical application skills.

Let’s dive in!

Table of Contents

What is a Data Analytics Case Study?

A data analytics case study is a real or hypothetical scenario where analytics techniques are applied to solve a specific problem or explore a particular question.

It’s a practical approach that uses data analytics methods, assisting in deciphering data for meaningful insights. This structured method helps individuals or organizations make sense of data effectively.

Additionally, it’s a way to learn by doing, where there’s no single right or wrong answer in how you analyze the data.

So, what are the components of a case study?

Key Components of a Data Analytics Case Study

A data analytics case study comprises essential elements that structure the analytical journey:

Problem Context: A case study begins with a defined problem or question. It provides the context for the data analysis , setting the stage for exploration and investigation.

Data Collection and Sources: It involves gathering relevant data from various sources , ensuring data accuracy, completeness, and relevance to the problem at hand.

Analysis Techniques: Case studies employ different analytical methods, such as statistical analysis, machine learning algorithms, or visualization tools, to derive meaningful conclusions from the collected data.

Insights and Recommendations: The ultimate goal is to extract actionable insights from the analyzed data, offering recommendations or solutions that address the initial problem or question.

Now that you have a better understanding of what a data analytics case study is, let’s talk about why we need and use them.

Why Case Studies are Integral to Data Analytics

Case studies serve as invaluable tools in the realm of data analytics, offering multifaceted benefits that bolster an analyst’s proficiency and impact:

Real-Life Insights and Skill Enhancement: Examining case studies provides practical, real-life examples that expand knowledge and refine skills. These examples offer insights into diverse scenarios, aiding in a data analyst’s growth and expertise development.

Validation and Refinement of Analyses: Case studies demonstrate the effectiveness of data-driven decisions across industries, providing validation for analytical approaches. They showcase how organizations benefit from data analytics. Also, this helps in refining one’s own methodologies

Showcasing Data Impact on Business Outcomes: These studies show how data analytics directly affects business results, like increasing revenue, reducing costs, or delivering other measurable advantages. Understanding these impacts helps articulate the value of data analytics to stakeholders and decision-makers.

Learning from Successes and Failures: By exploring a case study, analysts glean insights from others’ successes and failures, acquiring new strategies and best practices. This learning experience facilitates professional growth and the adoption of innovative approaches within their own data analytics work.

Including case studies in a data analyst’s toolkit helps gain more knowledge, improve skills, and understand how data analytics affects different industries.

Using these real-life examples boosts confidence and success, guiding analysts to make better and more impactful decisions in their organizations.

But not all case studies are the same.

Let’s talk about the different types.

Types of Data Analytics Case Studies

Data analytics encompasses various approaches tailored to different analytical goals:

Exploratory Case Study: These involve delving into new datasets to uncover hidden patterns and relationships, often without a predefined hypothesis. They aim to gain insights and generate hypotheses for further investigation.

Predictive Case Study: These utilize historical data to forecast future trends, behaviors, or outcomes. By applying predictive models, they help anticipate potential scenarios or developments.

Diagnostic Case Study: This type focuses on understanding the root causes or reasons behind specific events or trends observed in the data. It digs deep into the data to provide explanations for occurrences.

Prescriptive Case Study: This case study goes beyond analytics; it provides actionable recommendations or strategies derived from the analyzed data. They guide decision-making processes by suggesting optimal courses of action based on insights gained.

Each type has a specific role in using data to find important insights, helping in decision-making, and solving problems in various situations.

Regardless of the type of case study you encounter, here are some steps to help you process them.

Roadmap to Handling a Data Analysis Case Study

Embarking on a data analytics case study requires a systematic approach, step-by-step, to derive valuable insights effectively.

Here are the steps to help you through the process:

Step 1: Understanding the Case Study Context: Immerse yourself in the intricacies of the case study. Delve into the industry context, understanding its nuances, challenges, and opportunities.

Identify the central problem or question the study aims to address. Clarify the objectives and expected outcomes, ensuring a clear understanding before diving into data analytics.

Step 2: Data Collection and Validation: Gather data from diverse sources relevant to the case study. Prioritize accuracy, completeness, and reliability during data collection. Conduct thorough validation processes to rectify inconsistencies, ensuring high-quality and trustworthy data for subsequent analysis.

Step 3: Problem Definition and Scope: Define the problem statement precisely. Articulate the objectives and limitations that shape the scope of your analysis. Identify influential variables and constraints, providing a focused framework to guide your exploration.

Step 4: Exploratory Data Analysis (EDA): Leverage exploratory techniques to gain initial insights. Visualize data distributions, patterns, and correlations, fostering a deeper understanding of the dataset. These explorations serve as a foundation for more nuanced analysis.

Step 5: Data Preprocessing and Transformation: Cleanse and preprocess the data to eliminate noise, handle missing values, and ensure consistency. Transform data formats or scales as required, preparing the dataset for further analysis.

Step 6: Data Modeling and Method Selection: Select analytical models aligning with the case study’s problem, employing statistical techniques, machine learning algorithms, or tailored predictive models.

In this phase, it’s important to develop data modeling skills. This helps create visuals of complex systems using organized data, which helps solve business problems more effectively.

Understand key data modeling concepts, utilize essential tools like SQL for database interaction, and practice building models from real-world scenarios.

Furthermore, strengthen data cleaning skills for accurate datasets, and stay updated with industry trends to ensure relevance.

Step 7: Model Evaluation and Refinement: Evaluate the performance of applied models rigorously. Iterate and refine models to enhance accuracy and reliability, ensuring alignment with the objectives and expected outcomes.

Step 8: Deriving Insights and Recommendations: Extract actionable insights from the analyzed data. Develop well-structured recommendations or solutions based on the insights uncovered, addressing the core problem or question effectively.

Step 9: Communicating Results Effectively: Present findings, insights, and recommendations clearly and concisely. Utilize visualizations and storytelling techniques to convey complex information compellingly, ensuring comprehension by stakeholders.

Step 10: Reflection and Iteration: Reflect on the entire analysis process and outcomes. Identify potential improvements and lessons learned. Embrace an iterative approach, refining methodologies for continuous enhancement and future analyses.

This step-by-step roadmap provides a structured framework for thorough and effective handling of a data analytics case study.

Now, after handling data analytics comes a crucial step; presenting the case study.

Presenting Your Data Analytics Case Study

Presenting a data analytics case study is a vital part of the process. When presenting your case study, clarity and organization are paramount.

To achieve this, follow these key steps:

Structuring Your Case Study: Start by outlining relevant and accurate main points. Ensure these points align with the problem addressed and the methodologies used in your analysis.

Crafting a Narrative with Data: Start with a brief overview of the issue, then explain your method and steps, covering data collection, cleaning, stats, and advanced modeling.

Visual Representation for Clarity: Utilize various visual aids—tables, graphs, and charts—to illustrate patterns, trends, and insights. Ensure these visuals are easy to comprehend and seamlessly support your narrative.

Highlighting Key Information: Use bullet points to emphasize essential information, maintaining clarity and allowing the audience to grasp key takeaways effortlessly. Bold key terms or phrases to draw attention and reinforce important points.

Addressing Audience Queries: Anticipate and be ready to answer audience questions regarding methods, assumptions, and results. Demonstrating a profound understanding of your analysis instills confidence in your work.

Integrity and Confidence in Delivery: Maintain a neutral tone and avoid exaggerated claims about findings. Present your case study with integrity, clarity, and confidence to ensure the audience appreciates and comprehends the significance of your work.

By organizing your presentation well, telling a clear story through your analysis, and using visuals wisely, you can effectively share your data analytics case study.

This method helps people understand better, stay engaged, and draw valuable conclusions from your work.

We hope by now, you are feeling very confident processing a case study. But with any process, there are challenges you may encounter.

Key Challenges in Data Analytics Case Studies

A data analytics case study can present various hurdles that necessitate strategic approaches for successful navigation:

Challenge 1: Data Quality and Consistency

Challenge: Inconsistent or poor-quality data can impede analysis, leading to erroneous insights and flawed conclusions.

Solution: Implement rigorous data validation processes, ensuring accuracy, completeness, and reliability. Employ data cleansing techniques to rectify inconsistencies and enhance overall data quality.

Challenge 2: Complexity and Scale of Data

Challenge: Managing vast volumes of data with diverse formats and complexities poses analytical challenges.

Solution: Utilize scalable data processing frameworks and tools capable of handling diverse data types. Implement efficient data storage and retrieval systems to manage large-scale datasets effectively.

Challenge 3: Interpretation and Contextual Understanding

Challenge: Interpreting data without contextual understanding or domain expertise can lead to misinterpretations.

Solution: Collaborate with domain experts to contextualize data and derive relevant insights. Invest in understanding the nuances of the industry or domain under analysis to ensure accurate interpretations.

Challenge 4: Privacy and Ethical Concerns

Challenge: Balancing data access for analysis while respecting privacy and ethical boundaries poses a challenge.

Solution: Implement robust data governance frameworks that prioritize data privacy and ethical considerations. Ensure compliance with regulatory standards and ethical guidelines throughout the analysis process.

Challenge 5: Resource Limitations and Time Constraints

Challenge: Limited resources and time constraints hinder comprehensive analysis and exhaustive data exploration.

Solution: Prioritize key objectives and allocate resources efficiently. Employ agile methodologies to iteratively analyze and derive insights, focusing on the most impactful aspects within the given timeframe.

Recognizing these challenges is key; it helps data analysts adopt proactive strategies to mitigate obstacles. This enhances the effectiveness and reliability of insights derived from a data analytics case study.

Now, let’s talk about the best software tools you should use when working with case studies.

Top 5 Software Tools for Case Studies

In the realm of case studies within data analytics, leveraging the right software tools is essential.

Here are some top-notch options:

Tableau : Renowned for its data visualization prowess, Tableau transforms raw data into interactive, visually compelling representations, ideal for presenting insights within a case study.

Python and R Libraries: These flexible programming languages provide many tools for handling data, doing statistics, and working with machine learning, meeting various needs in case studies.

Microsoft Excel : A staple tool for data analytics, Excel provides a user-friendly interface for basic analytics, making it useful for initial data exploration in a case study.

SQL Databases : Structured Query Language (SQL) databases assist in managing and querying large datasets, essential for organizing case study data effectively.

Statistical Software (e.g., SPSS , SAS ): Specialized statistical software enables in-depth statistical analysis, aiding in deriving precise insights from case study data.

Choosing the best mix of these tools, tailored to each case study’s needs, greatly boosts analytical abilities and results in data analytics.

Final Thoughts

Case studies in data analytics are helpful guides. They give real-world insights, improve skills, and show how data-driven decisions work.

Using case studies helps analysts learn, be creative, and make essential decisions confidently in their data work.

Check out our latest clip below to further your learning!

Frequently Asked Questions

What are the key steps to analyzing a data analytics case study.

When analyzing a case study, you should follow these steps:

Clarify the problem : Ensure you thoroughly understand the problem statement and the scope of the analysis.

Make assumptions : Define your assumptions to establish a feasible framework for analyzing the case.

Gather context : Acquire relevant information and context to support your analysis.

Analyze the data : Perform calculations, create visualizations, and conduct statistical analysis on the data.

Provide insights : Draw conclusions and develop actionable insights based on your analysis.

How can you effectively interpret results during a data scientist case study job interview?

During your next data science interview, interpret case study results succinctly and clearly. Utilize visual aids and numerical data to bolster your explanations, ensuring comprehension.

Frame the results in an audience-friendly manner, emphasizing relevance. Concentrate on deriving insights and actionable steps from the outcomes.

How do you showcase your data analyst skills in a project?

To demonstrate your skills effectively, consider these essential steps. Begin by selecting a problem that allows you to exhibit your capacity to handle real-world challenges through analysis.

Methodically document each phase, encompassing data cleaning, visualization, statistical analysis, and the interpretation of findings.

Utilize descriptive analysis techniques and effectively communicate your insights using clear visual aids and straightforward language. Ensure your project code is well-structured, with detailed comments and documentation, showcasing your proficiency in handling data in an organized manner.

Lastly, emphasize your expertise in SQL queries, programming languages, and various analytics tools throughout the project. These steps collectively highlight your competence and proficiency as a skilled data analyst, demonstrating your capabilities within the project.

Can you provide an example of a successful data analytics project using key metrics?

A prime illustration is utilizing analytics in healthcare to forecast hospital readmissions. Analysts leverage electronic health records, patient demographics, and clinical data to identify high-risk individuals.

Implementing preventive measures based on these key metrics helps curtail readmission rates, enhancing patient outcomes and cutting healthcare expenses.

This demonstrates how data analytics, driven by metrics, effectively tackles real-world challenges, yielding impactful solutions.

Why would a company invest in data analytics?

Companies invest in data analytics to gain valuable insights, enabling informed decision-making and strategic planning. This investment helps optimize operations, understand customer behavior, and stay competitive in their industry.

Ultimately, leveraging data analytics empowers companies to make smarter, data-driven choices, leading to enhanced efficiency, innovation, and growth.

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Top 25 Data Science Case Studies [2024]

In an era where data is the new gold, harnessing its power through data science has led to groundbreaking advancements across industries. From personalized marketing to predictive maintenance, the applications of data science are not only diverse but transformative. This compilation of the top 25 data science case studies showcases the profound impact of intelligent data utilization in solving real-world problems. These examples span various sectors, including healthcare, finance, transportation, and manufacturing, illustrating how data-driven decisions shape business operations’ future, enhance efficiency, and optimize user experiences. As we delve into these case studies, we witness the incredible potential of data science to innovate and drive success in today’s data-centric world.

Related: Interesting Data Science Facts

Top 25 Data Science Case Studies [2024]

Case study 1 – personalized marketing (amazon).

Challenge:  Amazon aimed to enhance user engagement by tailoring product recommendations to individual preferences, requiring the real-time processing of vast data volumes.

Solution:  Amazon implemented a sophisticated machine learning algorithm known as collaborative filtering, which analyzes users’ purchase history, cart contents, product ratings, and browsing history, along with the behavior of similar users. This approach enables Amazon to offer highly personalized product suggestions.

Overall Impact:

• Increased Customer Satisfaction:  Tailored recommendations improved the shopping experience.
• Higher Sales Conversions:  Relevant product suggestions boosted sales.

Key Takeaways:

• Personalized Marketing Significantly Enhances User Engagement:  Demonstrating how tailored interactions can deepen user involvement and satisfaction.
• Effective Use of Big Data and Machine Learning Can Transform Customer Experiences:  These technologies redefine the consumer landscape by continuously adapting recommendations to changing user preferences and behaviors.

This strategy has proven pivotal in increasing Amazon’s customer loyalty and sales by making the shopping experience more relevant and engaging.

Case Study 2 – Real-Time Pricing Strategy (Uber)

Challenge:  Uber needed to adjust its pricing dynamically to reflect real-time demand and supply variations across different locations and times, aiming to optimize driver incentives and customer satisfaction without manual intervention.

Solution:  Uber introduced a dynamic pricing model called “surge pricing.” This system uses data science to automatically calculate fares in real time based on current demand and supply data. The model incorporates traffic conditions, weather forecasts, and local events to adjust prices appropriately.

• Optimized Ride Availability:  The model reduced customer wait times by incentivizing more drivers to be available during high-demand periods.
• Increased Driver Earnings:  Drivers benefitted from higher earnings during surge periods, aligning their incentives with customer demand.
• Efficient Balance of Supply and Demand:  Dynamic pricing matches ride availability with customer needs.
• Importance of Real-Time Data Processing:  The real-time processing of data is crucial for responsive and adaptive service delivery.

Uber’s implementation of surge pricing illustrates the power of using real-time data analytics to create a flexible and responsive pricing system that benefits both consumers and service providers, enhancing overall service efficiency and satisfaction.

Case Study 3 – Fraud Detection in Banking (JPMorgan Chase)

Challenge:  JPMorgan Chase faced the critical need to enhance its fraud detection capabilities to safeguard the institution and its customers from financial losses. The primary challenge was detecting fraudulent transactions swiftly and accurately in a vast stream of legitimate banking activities.

Solution:  The bank implemented advanced machine learning models that analyze real-time transaction patterns and customer behaviors. These models are continuously trained on vast amounts of historical fraud data, enabling them to identify and flag transactions that significantly deviate from established patterns, which may indicate potential fraud.

• Substantial Reduction in Fraudulent Transactions:  The advanced detection capabilities led to a marked decrease in fraud occurrences.
• Enhanced Security for Customer Accounts:  Customers experienced greater security and trust in their transactions.
• Effectiveness of Machine Learning in Fraud Detection:  Machine learning models are greatly effective at identifying fraud activities within large datasets.
• Importance of Ongoing Training and Updates:  Continuous training and updating of models are crucial to adapt to evolving fraudulent techniques and maintain detection efficacy.

JPMorgan Chase’s use of machine learning for fraud detection demonstrates how financial institutions can leverage advanced analytics to enhance security measures, protect financial assets, and build customer trust in their banking services.

Case Study 4 – Optimizing Healthcare Outcomes (Mayo Clinic)

Challenge:  The Mayo Clinic aimed to enhance patient outcomes by predicting diseases before they reach critical stages. This involved analyzing large volumes of diverse data, including historical patient records and real-time health metrics from various sources like lab results and patient monitors.

Solution:  The Mayo Clinic employed predictive analytics to integrate and analyze this data to build models that predict patient risk for diseases such as diabetes and heart disease, enabling earlier and more targeted interventions.

• Improved Patient Outcomes:  Early identification of at-risk patients allowed for timely medical intervention.
• Reduction in Healthcare Costs:  Preventing disease progression reduces the need for more extensive and costly treatments later.
• Early Identification of Health Risks:  Predictive models are essential for identifying at-risk patients early, improving the chances of successful interventions.
• Integration of Multiple Data Sources:  Combining historical and real-time data provides a comprehensive view that enhances the accuracy of predictions.

Case Study 5 – Streamlining Operations in Manufacturing (General Electric)

Challenge:  General Electric needed to optimize its manufacturing processes to reduce costs and downtime by predicting when machines would likely require maintenance to prevent breakdowns.

Solution:  GE leveraged data from sensors embedded in machinery to monitor their condition continuously. Data science algorithms analyze this sensor data to predict when a machine is likely to disappoint, facilitating preemptive maintenance and scheduling.

• Reduction in Unplanned Machine Downtime:  Predictive maintenance helped avoid unexpected breakdowns.
• Lower Maintenance Costs and Improved Machine Lifespan:  Regular maintenance based on predictive data reduced overall costs and extended the life of machinery.
• Predictive Maintenance Enhances Operational Efficiency:  Using data-driven predictions for maintenance can significantly reduce downtime and operational costs.
• Value of Sensor Data:  Continuous monitoring and data analysis are crucial for forecasting equipment health and preventing failures.

Related: Data Engineering vs. Data Science

Case Study 6 – Enhancing Supply Chain Management (DHL)

Challenge:  DHL sought to optimize its global logistics and supply chain operations to decreases expenses and enhance delivery efficiency. It required handling complex data from various sources for better route planning and inventory management.

Solution:  DHL implemented advanced analytics to process and analyze data from its extensive logistics network. This included real-time tracking of shipments, analysis of weather conditions, traffic patterns, and inventory levels to optimize route planning and warehouse operations.

• Enhanced Efficiency in Logistics Operations:  More precise route planning and inventory management improved delivery times and reduced resource wastage.
• Reduced Operational Costs:  Streamlined operations led to significant cost savings across the supply chain.
• Critical Role of Comprehensive Data Analysis:  Effective supply chain management depends on integrating and analyzing data from multiple sources.
• Benefits of Real-Time Data Integration:  Real-time data enhances logistical decision-making, leading to more efficient and cost-effective operations.

Case Study 7 – Predictive Maintenance in Aerospace (Airbus)

Challenge:  Airbus faced the challenge of predicting potential failures in aircraft components to enhance safety and reduce maintenance costs. The key was to accurately forecast the lifespan of parts under varying conditions and usage patterns, which is critical in the aerospace industry where safety is paramount.

Solution:  Airbus tackled this challenge by developing predictive models that utilize data collected from sensors installed on aircraft. These sensors continuously monitor the condition of various components, providing real-time data that the models analyze. The predictive algorithms assess the likelihood of component failure, enabling maintenance teams to schedule repairs or replacements proactively before actual failures occur.

• Increased Safety:  The ability to predict and prevent potential in-flight failures has significantly improved the safety of Airbus aircraft.
• Reduced Costs:  By optimizing maintenance schedules and minimizing unnecessary checks, Airbus has been able to cut down on maintenance expenses and reduce aircraft downtime.
• Enhanced Safety through Predictive Analytics:  The use of predictive analytics in monitoring aircraft components plays a crucial role in preventing failures, thereby enhancing the overall safety of aviation operations.
• Valuable Insights from Sensor Data:  Real-time data from operational use is critical for developing effective predictive maintenance strategies. This data provides insights for understanding component behavior under various conditions, allowing for more accurate predictions.

This case study demonstrates how Airbus leverages advanced data science techniques in predictive maintenance to ensure higher safety standards and more efficient operations, setting an industry benchmark in the aerospace sector.

Case Study 8 – Enhancing Film Recommendations (Netflix)

Challenge:  Netflix aimed to improve customer retention and engagement by enhancing the accuracy of its recommendation system. This task involved processing and analyzing vast amounts of data to understand diverse user preferences and viewing habits.

Solution:  Netflix employed collaborative filtering techniques, analyzing user behaviors (like watching, liking, or disliking content) and similarities between content items. This data-driven approach allows Netflix to refine and personalize recommendations continuously based on real-time user interactions.

• Increased Viewer Engagement:  Personalized recommendations led to longer viewing sessions.
• Higher Customer Satisfaction and Retention Rates:  Tailored viewing experiences improved overall customer satisfaction, enhancing loyalty.
• Tailoring User Experiences:  Machine learning is pivotal in personalizing media content, significantly impacting viewer engagement and satisfaction.
• Importance of Continuous Updates:  Regularly updating recommendation algorithms is essential to maintain relevance and effectiveness in user engagement.

Case Study 9 – Traffic Flow Optimization (Google)

Challenge:  Google needed to optimize traffic flow within its Google Maps service to reduce congestion and improve routing decisions. This required real-time analysis of extensive traffic data to predict and manage traffic conditions accurately.

Solution:  Google Maps integrates data from multiple sources, including satellite imagery, sensor data, and real-time user location data. These data points are used to model traffic patterns and predict future conditions dynamically, which informs updated routing advice.

• Reduced Traffic Congestion:  More efficient routing reduced overall traffic buildup.
• Enhanced Accuracy of Traffic Predictions and Routing:  Improved predictions led to better user navigation experiences.
• Integration of Multiple Data Sources:  Combining various data streams enhances the accuracy of traffic management systems.
• Advanced Modeling Techniques:  Sophisticated models are crucial for accurately predicting traffic patterns and optimizing routes.

Case Study 10 – Risk Assessment in Insurance (Allstate)

Challenge:  Allstate sought to refine its risk assessment processes to offer more accurately priced insurance products, challenging the limitations of traditional actuarial models through more nuanced data interpretations.

Solution:  Allstate enhanced its risk assessment framework by integrating machine learning, allowing for granular risk factor analysis. This approach utilizes individual customer data such as driving records, home location specifics, and historical claim data to tailor insurance offerings more accurately.

• More Precise Risk Assessment:  Improved risk evaluation led to more tailored insurance offerings.
• Increased Market Competitiveness:  Enhanced pricing accuracy boosted Allstate’s competitive edge in the insurance market.
• Nuanced Understanding of Risk:  Machine learning provides a deeper, more nuanced understanding of risk than traditional models, leading to better risk pricing.
• Personalized Pricing Strategies:  Leveraging detailed customer data in pricing strategies enhances customer satisfaction and business performance.

Related: Can you move from Cybersecurity to Data Science?

Case Study 11 – Energy Consumption Reduction (Google DeepMind)

Challenge:  Google DeepMind aimed to significantly reduce the high energy consumption required for cooling Google’s data centers, which are crucial for maintaining server performance but also represent a major operational cost.

Solution:  DeepMind implemented advanced AI algorithms to optimize the data center cooling systems. These algorithms predict temperature fluctuations and adjust cooling processes accordingly, saving energy and reducing equipment wear and tear.

• Reduction in Energy Consumption:  Achieved a 40% reduction in energy used for cooling.
• Decrease in Operational Costs and Environmental Impact:  Lower energy usage resulted in cost savings and reduced environmental footprint.
• AI-Driven Optimization:  AI can significantly decrease energy usage in large-scale infrastructure.
• Operational Efficiency Gains:  Efficiency improvements in operational processes lead to cost savings and environmental benefits.

Case Study 12 – Improving Public Safety (New York City Police Department)

Challenge:  The NYPD needed to enhance its crime prevention strategies by better predicting where and when crimes were most likely to occur, requiring sophisticated analysis of historical crime data and environmental factors.

Solution:  The NYPD implemented a predictive policing system that utilizes data analytics to identify potential crime hotspots based on trends and patterns in past crime data. Officers are preemptively dispatched to these areas to deter criminal activities.

• Reduction in Crime Rates:  There is a notable decrease in crime in areas targeted by predictive policing.
• More Efficient Use of Police Resources:  Enhanced allocation of resources where needed.
• Effectiveness of Data-Driven Crime Prevention:  Targeting resources based on data analytics can significantly reduce crime.
• Proactive Law Enforcement:  Predictive analytics enable a shift from reactive to proactive law enforcement strategies.

Case Study 13 – Enhancing Agricultural Yields (John Deere)

Challenge:  John Deere aimed to help farmers increase agricultural productivity and sustainability by optimizing various farming operations from planting to harvesting.

Solution:  Utilizing data from sensors on equipment and satellite imagery, John Deere developed algorithms that provide actionable insights for farmers on optimal planting times, water usage, and harvest schedules.

• Increased Crop Yields:  More efficient farming methods led to higher yields.
• Enhanced Sustainability of Farming Practices:  Improved resource management contributed to more sustainable agriculture.
• Precision Agriculture:  Significantly improves productivity and resource efficiency.
• Data-Driven Decision-Making:  Enables better farming decisions through timely and accurate data.

Case Study 14 – Streamlining Drug Discovery (Pfizer)

Challenge:  Pfizer faced the need to accelerate the process of discoverying drug and improve the success rates of clinical trials.

Solution:  Pfizer employed data science to simulate and predict outcomes of drug trials using historical data and predictive models, optimizing trial parameters and improving the selection of drug candidates.

• Accelerated Drug Development:  Reduced time to market for new drugs.
• Increased Efficiency and Efficacy in Clinical Trials:  More targeted trials led to better outcomes.
• Reduction in Drug Development Time and Costs:  Data science streamlines the R&D process.
• Improved Clinical Trial Success Rates:  Predictive modeling enhances the accuracy of trial outcomes.

Case Study 15 – Media Buying Optimization (Procter & Gamble)

Challenge:  Procter & Gamble aimed to maximize the ROI of their extensive advertising budget by optimizing their media buying strategy across various channels.

Solution:  P&G analyzed extensive data on consumer behavior and media consumption to identify the most effective times and channels for advertising, allowing for highly targeted ads that reach the intended audience at optimal times.

• Improved Effectiveness of Advertising Campaigns:  More effective ads increased campaign impact.
• Increased Sales and Better Budget Allocation:  Enhanced ROI from more strategic media spending.
• Enhanced Media Buying Strategies:  Data analytics significantly improves media buying effectiveness.
• Insights into Consumer Behavior:  Understanding consumer behavior is crucial for optimizing advertising ROI.

Related: Is Data Science Certificate beneficial for your career?

Case Study 16 – Reducing Patient Readmission Rates with Predictive Analytics (Mount Sinai Health System)

Challenge:  Mount Sinai Health System sought to reduce patient readmission rates, a significant indicator of healthcare quality and a major cost factor. The challenge involved identifying patients at high risk of being readmitted within 30 days of discharge.

Solution:  The health system implemented a predictive analytics platform that analyzes real-time patient data and historical health records. The system detects patterns and risk factors contributing to high readmission rates by utilizing machine learning algorithms. Factors such as past medical history, discharge conditions, and post-discharge care plans were integrated into the predictive model.

• Reduced Readmission Rates:  Early identification of at-risk patients allowed for targeted post-discharge interventions, significantly reducing readmission rates.
• Enhanced Patient Outcomes: Patients received better follow-up care tailored to their health risks.
• Predictive Analytics in Healthcare:  Effective for managing patient care post-discharge.
• Holistic Patient Data Utilization: Integrating various data points provides a more accurate prediction and better healthcare outcomes.

Case Study 17 – Enhancing E-commerce Customer Experience with AI (Zalando)

Challenge:  Zalando aimed to enhance the online shopping experience by improving the accuracy of size recommendations, a common issue that leads to high return rates in online apparel shopping.

Solution:  Zalando developed an AI-driven size recommendation engine that analyzes past purchase and return data in combination with customer feedback and preferences. This system utilizes machine learning to predict the best-fit size for customers based on their unique body measurements and purchase history.

• Reduced Return Rates:  More accurate size recommendations decreased the returns due to poor fit.
• Improved Customer Satisfaction: Customers experienced a more personalized shopping journey, enhancing overall satisfaction.
• Customization Through AI:  Personalizing customer experience can significantly impact satisfaction and business metrics.
• Data-Driven Decision-Making: Utilizing customer data effectively can improve business outcomes by reducing costs and enhancing the user experience.

Case Study 18 – Optimizing Energy Grid Performance with Machine Learning (Enel Group)

Challenge:  Enel Group, one of the largest power companies, faced challenges in managing and optimizing the performance of its vast energy grids. The primary goal was to increase the efficiency of energy distribution and reduce operational costs while maintaining reliability in the face of fluctuating supply and demand.

Solution:  Enel Group implemented a machine learning-based system that analyzes real-time data from smart meters, weather stations, and IoT devices across the grid. This system is designed to predict peak demand times, potential outages, and equipment failures before they occur. By integrating these predictions with automated grid management tools, Enel can dynamically adjust energy flows, allocate resources more efficiently, and schedule maintenance proactively.

• Enhanced Grid Efficiency:  Improved distribution management, reduced energy wastage, and optimized resource allocation.
• Reduced Operational Costs: Predictive maintenance and better grid management decreased the frequency and cost of repairs and outages.
• Predictive Maintenance in Utility Networks:  Advanced analytics can preemptively identify issues, saving costs and enhancing service reliability.
• Real-Time Data Integration: Leveraging data from various sources in real-time enables more agile and informed decision-making in energy management.

Case Study 19 – Personalizing Movie Streaming Experience (WarnerMedia)

Challenge:  WarnerMedia sought to enhance viewer engagement and subscription retention rates on its streaming platforms by providing more personalized content recommendations.

Solution:  WarnerMedia deployed a sophisticated data science strategy, utilizing deep learning algorithms to analyze viewer behaviors, including viewing history, ratings given to shows and movies, search patterns, and demographic data. This analysis helped create highly personalized viewer profiles, which were then used to tailor content recommendations, homepage layouts, and promotional offers specifically to individual preferences.

• Increased Viewer Engagement:  Personalized recommendations resulted in extended viewing times and increased interactions with the platform.
• Higher Subscription Retention: Tailored user experiences improved overall satisfaction, leading to lower churn rates.
• Deep Learning Enhances Personalization:  Deep learning algorithms allow a more nuanced knowledge of consumer preferences and behavior.
• Data-Driven Customization is Key to User Retention: Providing a customized experience based on data analytics is critical for maintaining and growing a subscriber base in the competitive streaming market.

Case Study 20 – Improving Online Retail Sales through Customer Sentiment Analysis (Zappos)

Challenge:  Zappos, an online shoe and clothing retailer, aimed to enhance customer satisfaction and boost sales by better understanding customer sentiments and preferences across various platforms.

Solution:  Zappos implemented a comprehensive sentiment analysis program that utilized natural language processing (NLP) techniques to gather and analyze customer feedback from social media, product reviews, and customer support interactions. This data was used to identify emerging trends, customer pain points, and overall sentiment towards products and services. The insights derived from this analysis were subsequently used to customize marketing strategies, enhance product offerings, and improve customer service practices.

• Enhanced Product Selection and Marketing:  Insight-driven adjustments to inventory and marketing strategies increased relevancy and customer satisfaction.
• Improved Customer Experience: By addressing customer concerns and preferences identified through sentiment analysis, Zappos enhanced its overall customer service, increasing loyalty and repeat business.
• Power of Sentiment Analysis in Retail:  Understanding and reacting to customer emotions and opinions can significantly impact sales and customer satisfaction.
• Strategic Use of Customer Feedback: Leveraging customer feedback to drive business decisions helps align product offerings and services with customer expectations, fostering a positive brand image.

Related: Data Science Industry in the US

Case Study 21 – Streamlining Airline Operations with Predictive Analytics (Delta Airlines)

Challenge:  Delta Airlines faced operational challenges, including flight delays, maintenance scheduling inefficiencies, and customer service issues, which impacted passenger satisfaction and operational costs.

Solution:  Delta implemented a predictive analytics system that integrates data from flight operations, weather reports, aircraft sensor data, and historical maintenance records. The system predicts potential delays using machine learning models and suggests optimal maintenance scheduling. Additionally, it forecasts passenger load to optimize staffing and resource allocation at airports.

• Reduced Flight Delays:  Predictive insights allowed for better planning and reduced unexpected delays.
• Enhanced Maintenance Efficiency:  Maintenance could be scheduled proactively, decreasing the time planes spend out of service.
• Improved Passenger Experience: With better resource management, passenger handling became more efficient, enhancing overall customer satisfaction.
• Operational Efficiency Through Predictive Analytics:  Leveraging data for predictive purposes significantly improves operational decision-making.
• Data Integration Across Departments: Coordinating data from different sources provides a holistic view crucial for effective airline management.

Case Study 22 – Enhancing Financial Advisory Services with AI (Morgan Stanley)

Challenge:  Morgan Stanley sought to offer clients more personalized and effective financial guidance. The challenge was seamlessly integrating vast financial data with individual client profiles to deliver tailored investment recommendations.

Solution:  Morgan Stanley developed an AI-powered platform that utilizes natural language processing and ML to analyze financial markets, client portfolios, and historical investment performance. The system identifies patterns and predicts market trends while considering each client’s financial goals, risk tolerance, and investment history. This integrated approach enables financial advisors to offer highly customized advice and proactive investment strategies.

• Improved Client Satisfaction:  Clients received more relevant and timely investment recommendations, enhancing their overall satisfaction and trust in the advisory services.
• Increased Efficiency: Advisors were able to manage client portfolios more effectively, using AI-driven insights to make faster and more informed decisions.
• Personalization through AI:  Advanced analytics and AI can significantly enhance the personalization of financial services, leading to better client engagement.
• Data-Driven Decision Making: Leveraging diverse data sets provides a comprehensive understanding crucial for tailored financial advising.

Case Study 23 – Optimizing Inventory Management in Retail (Walmart)

Challenge:  Walmart sought to improve inventory management across its vast network of stores and warehouses to reduce overstock and stockouts, which affect customer satisfaction and operational efficiency.

Solution:  Walmart implemented a robust data analytics system that integrates real-time sales data, supply chain information, and predictive analytics. This system uses machine learning algorithms to forecast demand for thousands of products at a granular level, considering factors such as seasonality, local events, and economic trends. The predictive insights allow Walmart to dynamically adjust inventory levels, optimize restocking schedules, and manage distribution logistics more effectively.

• Reduced Inventory Costs:  More accurate demand forecasts helped minimize overstock and reduce waste.
• Enhanced Customer Satisfaction: Improved stock availability led to better in-store experiences and higher customer satisfaction.
• Precision in Demand Forecasting:  Advanced data analytics and machine learning significantly enhance demand forecasting accuracy in retail.
• Integrated Data Systems:  Combining various data sources provides a comprehensive view of inventory needs, improving overall supply chain efficiency.

Case Study 24: Enhancing Network Security with Predictive Analytics (Cisco)

Challenge:  Cisco encountered difficulties protecting its extensive network infrastructure from increasingly complex cyber threats. The objective was to bolster their security protocols by anticipating potential breaches before they happen.

Solution:  Cisco developed a predictive analytics solution that leverages ML algorithms to analyze patterns in network traffic and identify anomalies that could suggest a security threat. By integrating this system with their existing security protocols, Cisco can dynamically adjust defenses and alert system administrators about potential vulnerabilities in real-time.

• Improved Security Posture:  The predictive system enabled proactive responses to potential threats, significantly reducing the incidence of successful cyber attacks.
• Enhanced Operational Efficiency: Automating threat detection and response processes allowed Cisco to manage network security more efficiently, with fewer resources dedicated to manual monitoring.
• Proactive Security Measures:  Employing predictive cybersecurity analytics helps organizations avoid potential threats.
• Integration of Machine Learning: Machine learning is crucial for effectively detecting patterns and anomalies that human analysts might overlook, leading to stronger security measures.

Case Study 25 – Improving Agricultural Efficiency with IoT and AI (Bayer Crop Science)

Challenge:  Bayer Crop Science aimed to enhance agricultural efficiency and crop yields for farmers worldwide, facing the challenge of varying climatic conditions and soil types that affect crop growth differently.

Solution:  Bayer deployed an integrated platform that merges IoT sensors, satellite imagery, and AI-driven analytics. This platform gathers real-time weather conditions, soil quality, and crop health data. Utilizing machine learning models, the system processes this data to deliver precise agricultural recommendations to farmers, including optimal planting times, watering schedules, and pest management strategies.

• Increased Crop Yields:  Tailored agricultural practices led to higher productivity per hectare.
• Reduced Resource Waste: Efficient water use, fertilizers, and pesticides minimized environmental impact and operational costs.
• Precision Agriculture:  Leveraging IoT and AI enables more precise and data-driven agricultural practices, enhancing yield and efficiency.
• Sustainability in Farming:  Advanced data analytics enhance the sustainability of farming by optimizing resource utilization and minimizing waste.

Related: Is Data Science Overhyped?

The power of data science in transforming industries is undeniable, as demonstrated by these 25 compelling case studies. Through the strategic application of machine learning, predictive analytics, and AI, companies are solving complex challenges and gaining a competitive edge. The insights gleaned from these cases highlight the critical role of data science in enhancing decision-making processes, improving operational efficiency, and elevating customer satisfaction. As we look to the future, the role of data science is set to grow, promising even more innovative solutions and smarter strategies across all sectors. These case studies inspire and serve as a roadmap for harnessing the transformative power of data science in the journey toward digital transformation.

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Data Analysis Case Study: Learn From Humana’s Automated Data Analysis Project

Lillian Pierson, P.E.

Playback speed:

Got data? Great! Looking for that perfect data analysis case study to help you get started using it? You’re in the right place.

If you’ve ever struggled to decide what to do next with your data projects, to actually find meaning in the data, or even to decide what kind of data to collect, then KEEP READING…

Deep down, you know what needs to happen. You need to initiate and execute a data strategy that really moves the needle for your organization. One that produces seriously awesome business results.

But how you’re in the right place to find out..

As a data strategist who has worked with 10 percent of Fortune 100 companies, today I’m sharing with you a case study that demonstrates just how real businesses are making real wins with data analysis. 

In the post below, we’ll look at:

• A shining data success story;
• What went on ‘under-the-hood’ to support that successful data project; and
• The exact data technologies used by the vendor, to take this project from pure strategy to pure success

If you prefer to watch this information rather than read it, it’s captured in the video below:

Here’s the url too: https://youtu.be/xMwZObIqvLQ

3 Action Items You Need To Take

To actually use the data analysis case study you’re about to get – you need to take 3 main steps. Those are:

• Reflect upon your organization as it is today (I left you some prompts below – to help you get started)
• Review winning data case collections (starting with the one I’m sharing here) and identify 5 that seem the most promising for your organization given it’s current set-up
• Assess your organization AND those 5 winning case collections. Based on that assessment, select the “QUICK WIN” data use case that offers your organization the most bang for it’s buck

Step 1: Reflect Upon Your Organization

Whenever you evaluate data case collections to decide if they’re a good fit for your organization, the first thing you need to do is organize your thoughts with respect to your organization as it is today.

Before moving into the data analysis case study, STOP and ANSWER THE FOLLOWING QUESTIONS – just to remind yourself:

• What is the business vision for our organization?
• What industries do we primarily support?
• What data technologies do we already have up and running, that we could use to generate even more value?
• What team members do we have to support a new data project? And what are their data skillsets like?
• What type of data are we mostly looking to generate value from? Structured? Semi-Structured? Un-structured? Real-time data? Huge data sets? What are our data resources like?

Jot down some notes while you’re here. Then keep them in mind as you read on to find out how one company, Humana, used its data to achieve a 28 percent increase in customer satisfaction. Also include its 63 percent increase in employee engagement! (That’s such a seriously impressive outcome, right?!)

Step 2: Review Data Case Studies

Here we are, already at step 2. It’s time for you to start reviewing data analysis case studies  (starting with the one I’m sharing below). I dentify 5 that seem the most promising for your organization given its current set-up.

Humana’s Automated Data Analysis Case Study

The key thing to note here is that the approach to creating a successful data program varies from industry to industry .

Let’s start with one to demonstrate the kind of value you can glean from these kinds of success stories.

Humana has provided health insurance to Americans for over 50 years. It is a service company focused on fulfilling the needs of its customers. A great deal of Humana’s success as a company rides on customer satisfaction, and the frontline of that battle for customers’ hearts and minds is Humana’s customer service center.

Call centers are hard to get right. A lot of emotions can arise during a customer service call, especially one relating to health and health insurance. Sometimes people are frustrated. At times, they’re upset. Also, there are times the customer service representative becomes aggravated, and the overall tone and progression of the phone call goes downhill. This is of course very bad for customer satisfaction.

Humana wanted to use artificial intelligence to improve customer satisfaction (and thus, customer retention rates & profits per customer).

Humana wanted to find a way to use artificial intelligence to monitor their phone calls and help their agents do a better job connecting with their customers in order to improve customer satisfaction (and thus, customer retention rates & profits per customer ).

In light of their business need, Humana worked with a company called Cogito, which specializes in voice analytics technology.

Cogito offers a piece of AI technology called Cogito Dialogue. It’s been trained to identify certain conversational cues as a way of helping call center representatives and supervisors stay actively engaged in a call with a customer.

The AI listens to cues like the customer’s voice pitch.

If it’s rising, or if the call representative and the customer talk over each other, then the dialogue tool will send out electronic alerts to the agent during the call.

Humana fed the dialogue tool customer service data from 10,000 calls and allowed it to analyze cues such as keywords, interruptions, and pauses, and these cues were then linked with specific outcomes. For example, if the representative is receiving a particular type of cues, they are likely to get a specific customer satisfaction result.

The Outcome

Customers were happier, and customer service representatives were more engaged..

This automated solution for data analysis has now been deployed in 200 Humana call centers and the company plans to roll it out to 100 percent of its centers in the future.

The initiative was so successful, Humana has been able to focus on next steps in its data program. The company now plans to begin predicting the type of calls that are likely to go unresolved, so they can send those calls over to management before they become frustrating to the customer and customer service representative alike.

What does this mean for you and your business?

Well, if you’re looking for new ways to generate value by improving the quantity and quality of the decision support that you’re providing to your customer service personnel, then this may be a perfect example of how you can do so.

Humana’s Business Use Cases

Humana’s data analysis case study includes two key business use cases:

• Analyzing customer sentiment; and
• Suggesting actions to customer service representatives.

Analyzing Customer Sentiment

First things first, before you go ahead and collect data, you need to ask yourself who and what is involved in making things happen within the business.

In the case of Humana, the actors were:

• The health insurance system itself
• The customer, and
• The customer service representative

As you can see in the use case diagram above, the relational aspect is pretty simple. You have a customer service representative and a customer. They are both producing audio data, and that audio data is being fed into the system.

Humana focused on collecting the key data points, shown in the image below, from their customer service operations.

By collecting data about speech style, pitch, silence, stress in customers’ voices, length of call, speed of customers’ speech, intonation, articulation, silence, and representatives’  manner of speaking, Humana was able to analyze customer sentiment and introduce techniques for improved customer satisfaction.

Having strategically defined these data points, the Cogito technology was able to generate reports about customer sentiment during the calls.

Suggesting actions to customer service representatives.

The second use case for the Humana data program follows on from the data gathered in the first case.

In Humana’s case, Cogito generated a host of call analyses and reports about key call issues.

In the second business use case, Cogito was able to suggest actions to customer service representatives, in real-time , to make use of incoming data and help improve customer satisfaction on the spot.

The technology Humana used provided suggestions via text message to the customer service representative, offering the following types of feedback:

• The tone of voice is too tense
• The speed of speaking is high
• The customer representative and customer are speaking at the same time

These alerts allowed the Humana customer service representatives to alter their approach immediately , improving the quality of the interaction and, subsequently, the customer satisfaction.

The preconditions for success in this use case were:

• The call-related data must be collected and stored
• The AI models must be in place to generate analysis on the data points that are recorded during the calls

Evidence of success can subsequently be found in a system that offers real-time suggestions for courses of action that the customer service representative can take to improve customer satisfaction.

Thanks to this data-intensive business use case, Humana was able to increase customer satisfaction, improve customer retention rates, and drive profits per customer.

The Technology That Supports This Data Analysis Case Study

I promised to dip into the tech side of things. This is especially for those of you who are interested in the ins and outs of how projects like this one are actually rolled out.

Here’s a little rundown of the main technologies we discovered when we investigated how Cogito runs in support of its clients like Humana.

• For cloud data management Cogito uses AWS, specifically the Athena product
• For on-premise big data management, the company used Apache HDFS – the distributed file system for storing big data
• They utilize MapReduce, for processing their data
• And Cogito also has traditional systems and relational database management systems such as PostgreSQL
• In terms of analytics and data visualization tools, Cogito makes use of Tableau
• And for its machine learning technology, these use cases required people with knowledge in Python, R, and SQL, as well as deep learning (Cogito uses the PyTorch library and the TensorFlow library)

These data science skill sets support the effective computing, deep learning , and natural language processing applications employed by Humana for this use case.

If you’re looking to hire people to help with your own data initiative, then people with those skills listed above, and with experience in these specific technologies, would be a huge help.

Step 3: S elect The “Quick Win” Data Use Case

Still there? Great!

It’s time to close the loop.

Remember those notes you took before you reviewed the study? I want you to STOP here and assess. Does this Humana case study seem applicable and promising as a solution, given your organization’s current set-up…

YES ▶ Excellent!

Earmark it and continue exploring other winning data use cases until you’ve identified 5 that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that.

NO , Lillian – It’s not applicable. ▶  No problem.

Discard the information and continue exploring the winning data use cases we’ve categorized for you according to business function and industry. Save time by dialing down into the business function you know your business really needs help with now. Identify 5 winning data use cases that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that data use case.

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12 Data Science Case Studies: Across Various Industries

Home Blog Data Science 12 Data Science Case Studies: Across Various Industries

Data science has become popular in the last few years due to its successful application in making business decisions. Data scientists have been using data science techniques to solve challenging real-world issues in healthcare, agriculture, manufacturing, automotive, and many more. For this purpose, a data enthusiast needs to stay updated with the latest technological advancements in AI. An excellent way to achieve this is through reading industry data science case studies. I recommend checking out Data Science With Python course syllabus to start your data science journey.   In this discussion, I will present some case studies to you that contain detailed and systematic data analysis of people, objects, or entities focusing on multiple factors present in the dataset. Almost every industry uses data science in some way. You can learn more about data science fundamentals in this Data Science course content .

Let’s look at the top data science case studies in this article so you can understand how businesses from many sectors have benefitted from data science to boost productivity, revenues, and more.

List of Data Science Case Studies 2024

• Hospitality:  Airbnb focuses on growth by  analyzing  customer voice using data science.  Qantas uses predictive analytics to mitigate losses
• Healthcare:  Novo Nordisk  is  Driving innovation with NLP.  AstraZeneca harnesses data for innovation in medicine  
• Covid 19:  Johnson and Johnson use s  d ata science  to fight the Pandemic  
• E-commerce:  Amazon uses data science to personalize shop p ing experiences and improve customer satisfaction  
• Supply chain management:  UPS optimizes supp l y chain with big data analytics
• Meteorology:  IMD leveraged data science to achieve a rec o rd 1.2m evacuation before cyclone ''Fani''  
• Entertainment Industry:  Netflix  u ses data science to personalize the content and improve recommendations.  Spotify uses big   data to deliver a rich user experience for online music streaming  
• Banking and Finance:  HDFC utilizes Big  D ata Analytics to increase income and enhance  the  banking experience
• Urban Planning and Smart Cities:  Traffic management in smart cities such as Pune and Bhubaneswar
• Agricultural Yield Prediction:  Farmers Edge in Canada uses Data science to help farmers improve their produce
• Transportation Industry:  Uber optimizes their ride-sharing feature and track the delivery routes through data analysis
• Environmental Industry:  NASA utilizes Data science to predict potential natural disasters, World Wildlife analyzes deforestation to protect the environment

Top 12 Data Science Case Studies

1. data science in hospitality industry.

In the hospitality sector, data analytics assists hotels in better pricing strategies, customer analysis, brand marketing, tracking market trends, and many more.

Airbnb focuses on growth by analyzing customer voice using data science.  A famous example in this sector is the unicorn '' Airbnb '', a startup that focussed on data science early to grow and adapt to the market faster. This company witnessed a 43000 percent hypergrowth in as little as five years using data science. They included data science techniques to process the data, translate this data for better understanding the voice of the customer, and use the insights for decision making. They also scaled the approach to cover all aspects of the organization. Airbnb uses statistics to analyze and aggregate individual experiences to establish trends throughout the community. These analyzed trends using data science techniques impact their business choices while helping them grow further.  

Travel industry and data science

Predictive analytics benefits many parameters in the travel industry. These companies can use recommendation engines with data science to achieve higher personalization and improved user interactions. They can study and cross-sell products by recommending relevant products to drive sales and increase revenue. Data science is also employed in analyzing social media posts for sentiment analysis, bringing invaluable travel-related insights. Whether these views are positive, negative, or neutral can help these agencies understand the user demographics, the expected experiences by their target audiences, and so on. These insights are essential for developing aggressive pricing strategies to draw customers and provide better customization to customers in the travel packages and allied services. Travel agencies like Expedia and Booking.com use predictive analytics to create personalized recommendations, product development, and effective marketing of their products. Not just travel agencies but airlines also benefit from the same approach. Airlines frequently face losses due to flight cancellations, disruptions, and delays. Data science helps them identify patterns and predict possible bottlenecks, thereby effectively mitigating the losses and improving the overall customer traveling experience.  

How Qantas uses predictive analytics to mitigate losses  

Qantas , one of Australia's largest airlines, leverages data science to reduce losses caused due to flight delays, disruptions, and cancellations. They also use it to provide a better traveling experience for their customers by reducing the number and length of delays caused due to huge air traffic, weather conditions, or difficulties arising in operations. Back in 2016, when heavy storms badly struck Australia's east coast, only 15 out of 436 Qantas flights were cancelled due to their predictive analytics-based system against their competitor Virgin Australia, which witnessed 70 cancelled flights out of 320.  

2. Data Science in Healthcare

The  Healthcare sector  is immensely benefiting from the advancements in AI. Data science, especially in medical imaging, has been helping healthcare professionals come up with better diagnoses and effective treatments for patients. Similarly, several advanced healthcare analytics tools have been developed to generate clinical insights for improving patient care. These tools also assist in defining personalized medications for patients reducing operating costs for clinics and hospitals. Apart from medical imaging or computer vision,  Natural Language Processing (NLP)  is frequently used in the healthcare domain to study the published textual research data.     

A. Pharmaceutical

Driving innovation with NLP: Novo Nordisk.  Novo Nordisk  uses the Linguamatics NLP platform from internal and external data sources for text mining purposes that include scientific abstracts, patents, grants, news, tech transfer offices from universities worldwide, and more. These NLP queries run across sources for the key therapeutic areas of interest to the Novo Nordisk R&D community. Several NLP algorithms have been developed for the topics of safety, efficacy, randomized controlled trials, patient populations, dosing, and devices. Novo Nordisk employs a data pipeline to capitalize the tools' success on real-world data and uses interactive dashboards and cloud services to visualize this standardized structured information from the queries for exploring commercial effectiveness, market situations, potential, and gaps in the product documentation. Through data science, they are able to automate the process of generating insights, save time and provide better insights for evidence-based decision making.  

How AstraZeneca harnesses data for innovation in medicine.  AstraZeneca  is a globally known biotech company that leverages data using AI technology to discover and deliver newer effective medicines faster. Within their R&D teams, they are using AI to decode the big data to understand better diseases like cancer, respiratory disease, and heart, kidney, and metabolic diseases to be effectively treated. Using data science, they can identify new targets for innovative medications. In 2021, they selected the first two AI-generated drug targets collaborating with BenevolentAI in Chronic Kidney Disease and Idiopathic Pulmonary Fibrosis.   

Data science is also helping AstraZeneca redesign better clinical trials, achieve personalized medication strategies, and innovate the process of developing new medicines. Their Center for Genomics Research uses  data science and AI  to analyze around two million genomes by 2026. Apart from this, they are training their AI systems to check these images for disease and biomarkers for effective medicines for imaging purposes. This approach helps them analyze samples accurately and more effortlessly. Moreover, it can cut the analysis time by around 30%.   

AstraZeneca also utilizes AI and machine learning to optimize the process at different stages and minimize the overall time for the clinical trials by analyzing the clinical trial data. Summing up, they use data science to design smarter clinical trials, develop innovative medicines, improve drug development and patient care strategies, and many more.

C. Wearable Technology  

Wearable technology is a multi-billion-dollar industry. With an increasing awareness about fitness and nutrition, more individuals now prefer using fitness wearables to track their routines and lifestyle choices.  

Fitness wearables are convenient to use, assist users in tracking their health, and encourage them to lead a healthier lifestyle. The medical devices in this domain are beneficial since they help monitor the patient's condition and communicate in an emergency situation. The regularly used fitness trackers and smartwatches from renowned companies like Garmin, Apple, FitBit, etc., continuously collect physiological data of the individuals wearing them. These wearable providers offer user-friendly dashboards to their customers for analyzing and tracking progress in their fitness journey.

3. Covid 19 and Data Science

In the past two years of the Pandemic, the power of data science has been more evident than ever. Different  pharmaceutical companies  across the globe could synthesize Covid 19 vaccines by analyzing the data to understand the trends and patterns of the outbreak. Data science made it possible to track the virus in real-time, predict patterns, devise effective strategies to fight the Pandemic, and many more.  

How Johnson and Johnson uses data science to fight the Pandemic   

The  data science team  at  Johnson and Johnson  leverages real-time data to track the spread of the virus. They built a global surveillance dashboard (granulated to county level) that helps them track the Pandemic's progress, predict potential hotspots of the virus, and narrow down the likely place where they should test its investigational COVID-19 vaccine candidate. The team works with in-country experts to determine whether official numbers are accurate and find the most valid information about case numbers, hospitalizations, mortality and testing rates, social compliance, and local policies to populate this dashboard. The team also studies the data to build models that help the company identify groups of individuals at risk of getting affected by the virus and explore effective treatments to improve patient outcomes.

4. Data Science in E-commerce  

In the  e-commerce sector , big data analytics can assist in customer analysis, reduce operational costs, forecast trends for better sales, provide personalized shopping experiences to customers, and many more.  

Amazon uses data science to personalize shopping experiences and improve customer satisfaction.  Amazon  is a globally leading eCommerce platform that offers a wide range of online shopping services. Due to this, Amazon generates a massive amount of data that can be leveraged to understand consumer behavior and generate insights on competitors' strategies. Data science case studies reveal how Amazon uses its data to provide recommendations to its users on different products and services. With this approach, Amazon is able to persuade its consumers into buying and making additional sales. This approach works well for Amazon as it earns 35% of the revenue yearly with this technique. Additionally, Amazon collects consumer data for faster order tracking and better deliveries.     

Similarly, Amazon's virtual assistant, Alexa, can converse in different languages; uses speakers and a   camera to interact with the users. Amazon utilizes the audio commands from users to improve Alexa and deliver a better user experience. 

5. Data Science in Supply Chain Management

Predictive analytics and big data are driving innovation in the Supply chain domain. They offer greater visibility into the company operations, reduce costs and overheads, forecasting demands, predictive maintenance, product pricing, minimize supply chain interruptions, route optimization, fleet management, drive better performance, and more.     

Optimizing supply chain with big data analytics: UPS

UPS  is a renowned package delivery and supply chain management company. With thousands of packages being delivered every day, on average, a UPS driver makes about 100 deliveries each business day. On-time and safe package delivery are crucial to UPS's success. Hence, UPS offers an optimized navigation tool ''ORION'' (On-Road Integrated Optimization and Navigation), which uses highly advanced big data processing algorithms. This tool for UPS drivers provides route optimization concerning fuel, distance, and time. UPS utilizes supply chain data analysis in all aspects of its shipping process. Data about packages and deliveries are captured through radars and sensors. The deliveries and routes are optimized using big data systems. Overall, this approach has helped UPS save 1.6 million gallons of gasoline in transportation every year, significantly reducing delivery costs.    

6. Data Science in Meteorology

Weather prediction is an interesting  application of data science . Businesses like aviation, agriculture and farming, construction, consumer goods, sporting events, and many more are dependent on climatic conditions. The success of these businesses is closely tied to the weather, as decisions are made after considering the weather predictions from the meteorological department.   

Besides, weather forecasts are extremely helpful for individuals to manage their allergic conditions. One crucial application of weather forecasting is natural disaster prediction and risk management.  

Weather forecasts begin with a large amount of data collection related to the current environmental conditions (wind speed, temperature, humidity, clouds captured at a specific location and time) using sensors on IoT (Internet of Things) devices and satellite imagery. This gathered data is then analyzed using the understanding of atmospheric processes, and machine learning models are built to make predictions on upcoming weather conditions like rainfall or snow prediction. Although data science cannot help avoid natural calamities like floods, hurricanes, or forest fires. Tracking these natural phenomena well ahead of their arrival is beneficial. Such predictions allow governments sufficient time to take necessary steps and measures to ensure the safety of the population.  

IMD leveraged data science to achieve a record 1.2m evacuation before cyclone ''Fani''   

Most  d ata scientist’s responsibilities  rely on satellite images to make short-term forecasts, decide whether a forecast is correct, and validate models. Machine Learning is also used for pattern matching in this case. It can forecast future weather conditions if it recognizes a past pattern. When employing dependable equipment, sensor data is helpful to produce local forecasts about actual weather models. IMD used satellite pictures to study the low-pressure zones forming off the Odisha coast (India). In April 2019, thirteen days before cyclone ''Fani'' reached the area,  IMD  (India Meteorological Department) warned that a massive storm was underway, and the authorities began preparing for safety measures.  

It was one of the most powerful cyclones to strike India in the recent 20 years, and a record 1.2 million people were evacuated in less than 48 hours, thanks to the power of data science.   

7. Data Science in the Entertainment Industry

Due to the Pandemic, demand for OTT (Over-the-top) media platforms has grown significantly. People prefer watching movies and web series or listening to the music of their choice at leisure in the convenience of their homes. This sudden growth in demand has given rise to stiff competition. Every platform now uses data analytics in different capacities to provide better-personalized recommendations to its subscribers and improve user experience.   

How Netflix uses data science to personalize the content and improve recommendations  

Netflix  is an extremely popular internet television platform with streamable content offered in several languages and caters to various audiences. In 2006, when Netflix entered this media streaming market, they were interested in increasing the efficiency of their existing ''Cinematch'' platform by 10% and hence, offered a prize of $1 million to the winning team. This approach was successful as they found a solution developed by the BellKor team at the end of the competition that increased prediction accuracy by 10.06%. Over 200 work hours and an ensemble of 107 algorithms provided this result. These winning algorithms are now a part of the Netflix recommendation system.  

Netflix also employs Ranking Algorithms to generate personalized recommendations of movies and TV Shows appealing to its users.   

Spotify uses big data to deliver a rich user experience for online music streaming  

Personalized online music streaming is another area where data science is being used.  Spotify  is a well-known on-demand music service provider launched in 2008, which effectively leveraged big data to create personalized experiences for each user. It is a huge platform with more than 24 million subscribers and hosts a database of nearly 20million songs; they use the big data to offer a rich experience to its users. Spotify uses this big data and various algorithms to train machine learning models to provide personalized content. Spotify offers a "Discover Weekly" feature that generates a personalized playlist of fresh unheard songs matching the user's taste every week. Using the Spotify "Wrapped" feature, users get an overview of their most favorite or frequently listened songs during the entire year in December. Spotify also leverages the data to run targeted ads to grow its business. Thus, Spotify utilizes the user data, which is big data and some external data, to deliver a high-quality user experience.  

8. Data Science in Banking and Finance

Data science is extremely valuable in the Banking and  Finance industry . Several high priority aspects of Banking and Finance like credit risk modeling (possibility of repayment of a loan), fraud detection (detection of malicious or irregularities in transactional patterns using machine learning), identifying customer lifetime value (prediction of bank performance based on existing and potential customers), customer segmentation (customer profiling based on behavior and characteristics for personalization of offers and services). Finally, data science is also used in real-time predictive analytics (computational techniques to predict future events).    

How HDFC utilizes Big Data Analytics to increase revenues and enhance the banking experience    

One of the major private banks in India,  HDFC Bank , was an early adopter of AI. It started with Big Data analytics in 2004, intending to grow its revenue and understand its customers and markets better than its competitors. Back then, they were trendsetters by setting up an enterprise data warehouse in the bank to be able to track the differentiation to be given to customers based on their relationship value with HDFC Bank. Data science and analytics have been crucial in helping HDFC bank segregate its customers and offer customized personal or commercial banking services. The analytics engine and SaaS use have been assisting the HDFC bank in cross-selling relevant offers to its customers. Apart from the regular fraud prevention, it assists in keeping track of customer credit histories and has also been the reason for the speedy loan approvals offered by the bank.  

9. Data Science in Urban Planning and Smart Cities  

Data Science can help the dream of smart cities come true! Everything, from traffic flow to energy usage, can get optimized using data science techniques. You can use the data fetched from multiple sources to understand trends and plan urban living in a sorted manner.  

The significant data science case study is traffic management in Pune city. The city controls and modifies its traffic signals dynamically, tracking the traffic flow. Real-time data gets fetched from the signals through cameras or sensors installed. Based on this information, they do the traffic management. With this proactive approach, the traffic and congestion situation in the city gets managed, and the traffic flow becomes sorted. A similar case study is from Bhubaneswar, where the municipality has platforms for the people to give suggestions and actively participate in decision-making. The government goes through all the inputs provided before making any decisions, making rules or arranging things that their residents actually need.  

10. Data Science in Agricultural Prediction   

Have you ever wondered how helpful it can be if you can predict your agricultural yield? That is exactly what data science is helping farmers with. They can get information about the number of crops they can produce in a given area based on different environmental factors and soil types. Using this information, the farmers can make informed decisions about their yield and benefit the buyers and themselves in multiple ways.  

Farmers across the globe and overseas use various data science techniques to understand multiple aspects of their farms and crops. A famous example of data science in the agricultural industry is the work done by Farmers Edge. It is a company in Canada that takes real-time images of farms across the globe and combines them with related data. The farmers use this data to make decisions relevant to their yield and improve their produce. Similarly, farmers in countries like Ireland use satellite-based information to ditch traditional methods and multiply their yield strategically.  

11. Data Science in the Transportation Industry   

Transportation keeps the world moving around. People and goods commute from one place to another for various purposes, and it is fair to say that the world will come to a standstill without efficient transportation. That is why it is crucial to keep the transportation industry in the most smoothly working pattern, and data science helps a lot in this. In the realm of technological progress, various devices such as traffic sensors, monitoring display systems, mobility management devices, and numerous others have emerged.  

Many cities have already adapted to the multi-modal transportation system. They use GPS trackers, geo-locations and CCTV cameras to monitor and manage their transportation system. Uber is the perfect case study to understand the use of data science in the transportation industry. They optimize their ride-sharing feature and track the delivery routes through data analysis. Their data science case studies approach enabled them to serve more than 100 million users, making transportation easy and convenient. Moreover, they also use the data they fetch from users daily to offer cost-effective and quickly available rides.  

12. Data Science in the Environmental Industry    

Increasing pollution, global warming, climate changes and other poor environmental impacts have forced the world to pay attention to environmental industry. Multiple initiatives are being taken across the globe to preserve the environment and make the world a better place. Though the industry recognition and the efforts are in the initial stages, the impact is significant, and the growth is fast.  

The popular use of data science in the environmental industry is by NASA and other research organizations worldwide. NASA gets data related to the current climate conditions, and this data gets used to create remedial policies that can make a difference. Another way in which data science is actually helping researchers is they can predict natural disasters well before time and save or at least reduce the potential damage considerably. A similar case study is with the World Wildlife Fund. They use data science to track data related to deforestation and help reduce the illegal cutting of trees. Hence, it helps preserve the environment.  

Where to Find Full Data Science Case Studies?  

Data science is a highly evolving domain with many practical applications and a huge open community. Hence, the best way to keep updated with the latest trends in this domain is by reading case studies and technical articles. Usually, companies share their success stories of how data science helped them achieve their goals to showcase their potential and benefit the greater good. Such case studies are available online on the respective company websites and dedicated technology forums like Towards Data Science or Medium.  

Additionally, we can get some practical examples in recently published research papers and textbooks in data science.  

What Are the Skills Required for Data Scientists?  

Data scientists play an important role in the data science process as they are the ones who work on the data end to end. To be able to work on a data science case study, there are several skills required for data scientists like a good grasp of the fundamentals of data science, deep knowledge of statistics, excellent programming skills in Python or R, exposure to data manipulation and data analysis, ability to generate creative and compelling data visualizations, good knowledge of big data, machine learning and deep learning concepts for model building & deployment. Apart from these technical skills, data scientists also need to be good storytellers and should have an analytical mind with strong communication skills.    

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Conclusion  

These were some interesting  data science case studies  across different industries. There are many more domains where data science has exciting applications, like in the Education domain, where data can be utilized to monitor student and instructor performance, develop an innovative curriculum that is in sync with the industry expectations, etc.   

Almost all the companies looking to leverage the power of big data begin with a SWOT analysis to narrow down the problems they intend to solve with data science. Further, they need to assess their competitors to develop relevant data science tools and strategies to address the challenging issue.  Thus, the utility of data science in several sectors is clearly visible, a lot is left to be explored, and more is yet to come. Nonetheless, data science will continue to boost the performance of organizations in this age of big data.  

Frequently Asked Questions (FAQs)

A case study in data science requires a systematic and organized approach for solving the problem. Generally, four main steps are needed to tackle every data science case study: 

• Defining the problem statement and strategy to solve it  
• Gather and pre-process the data by making relevant assumptions  
• Select tool and appropriate algorithms to build machine learning /deep learning models 
• Make predictions, accept the solutions based on evaluation metrics, and improve the model if necessary. 

Getting data for a case study starts with a reasonable understanding of the problem. This gives us clarity about what we expect the dataset to include. Finding relevant data for a case study requires some effort. Although it is possible to collect relevant data using traditional techniques like surveys and questionnaires, we can also find good quality data sets online on different platforms like Kaggle, UCI Machine Learning repository, Azure open data sets, Government open datasets, Google Public Datasets, Data World and so on.  

Data science projects involve multiple steps to process the data and bring valuable insights. A data science project includes different steps - defining the problem statement, gathering relevant data required to solve the problem, data pre-processing, data exploration & data analysis, algorithm selection, model building, model prediction, model optimization, and communicating the results through dashboards and reports.  

Devashree Madhugiri

Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms. She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

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Case studies & examples

Agencies mobilize to improve emergency response in puerto rico through better data.

Federal agencies' response efforts to Hurricanes Irma and Maria in Puerto Rico was hampered by imperfect address data for the island. In the aftermath, emergency responders gathered together to enhance the utility of Puerto Rico address data and share best practices for using what information is currently available.

Federal Data Strategy

BUILDER: A Science-Based Approach to Infrastructure Management

The Department of Energy’s National Nuclear Security Administration (NNSA) adopted a data-driven, risk-informed strategy to better assess risks, prioritize investments, and cost effectively modernize its aging nuclear infrastructure. NNSA’s new strategy, and lessons learned during its implementation, will help inform other federal data practitioners’ efforts to maintain facility-level information while enabling accurate and timely enterprise-wide infrastructure analysis.

Department of Energy

data management , data analysis , process redesign , Federal Data Strategy

Business case for open data

Six reasons why making your agency's data open and accessible is a good business decision.

CDO Council Federal HR Dashboarding Report - 2021

The CDO Council worked with the US Department of Agriculture, the Department of the Treasury, the United States Agency for International Development, and the Department of Transportation to develop a Diversity Profile Dashboard and to explore the value of shared HR decision support across agencies. The pilot was a success, and identified potential impact of a standardized suite of HR dashboards, in addition to demonstrating the value of collaborative analytics between agencies.

Federal Chief Data Officer's Council

data practices , data sharing , data access

CDOC Data Inventory Report

The Chief Data Officers Council Data Inventory Working Group developed this paper to highlight the value proposition for data inventories and describe challenges agencies may face when implementing and managing comprehensive data inventories. It identifies opportunities agencies can take to overcome some of these challenges and includes a set of recommendations directed at Agencies, OMB, and the CDO Council (CDOC).

data practices , metadata , data inventory

DSWG Recommendations and Findings

The Chief Data Officer Council (CDOC) established a Data Sharing Working Group (DSWG) to help the council understand the varied data-sharing needs and challenges of all agencies across the Federal Government. The DSWG reviewed data-sharing across federal agencies and developed a set of recommendations for improving the methods to access and share data within and between agencies. This report presents the findings of the DSWG’s review and provides recommendations to the CDOC Executive Committee.

data practices , data agreements , data sharing , data access

Data Skills Training Program Implementation Toolkit

The Data Skills Training Program Implementation Toolkit is designed to provide both small and large agencies with information to develop their own data skills training programs. The information provided will serve as a roadmap to the design, implementation, and administration of federal data skills training programs as agencies address their Federal Data Strategy’s Agency Action 4 gap-closing strategy training component.

data sharing , Federal Data Strategy

Data Standdown: Interrupting process to fix information

Although not a true pause in operations, ONR’s data standdown made data quality and data consolidation the top priority for the entire organization. It aimed to establish an automated and repeatable solution to enable a more holistic view of ONR investments and activities, and to increase transparency and effectiveness throughout its mission support functions. In addition, it demonstrated that getting top-level buy-in from management to prioritize data can truly advance a more data-driven culture.

Office of Naval Research

data governance , data cleaning , process redesign , Federal Data Strategy

Data.gov Metadata Management Services Product-Preliminary Plan

Status summary and preliminary business plan for a potential metadata management product under development by the Data.gov Program Management Office

data management , Federal Data Strategy , metadata , open data

PDF (7 pages)

Department of Transportation Case Study: Enterprise Data Inventory

In response to the Open Government Directive, DOT developed a strategic action plan to inventory and release high-value information through the Data.gov portal. The Department sustained efforts in building its data inventory, responding to the President’s memorandum on regulatory compliance with a comprehensive plan that was recognized as a model for other agencies to follow.

Department of Transportation

data inventory , open data

Department of Transportation Model Data Inventory Approach

This document from the Department of Transportation provides a model plan for conducting data inventory efforts required under OMB Memorandum M-13-13.

data inventory

PDF (5 pages)

FEMA Case Study: Disaster Assistance Program Coordination

In 2008, the Disaster Assistance Improvement Program (DAIP), an E-Government initiative led by FEMA with support from 16 U.S. Government partners, launched DisasterAssistance.gov to simplify the process for disaster survivors to identify and apply for disaster assistance. DAIP utilized existing partner technologies and implemented a services oriented architecture (SOA) that integrated the content management system and rules engine supporting Department of Labor’s Benefits.gov applications with FEMA’s Individual Assistance Center application. The FEMA SOA serves as the backbone for data sharing interfaces with three of DAIP’s federal partners and transfers application data to reduce duplicate data entry by disaster survivors.

Federal Emergency Management Agency

data sharing

Federal CDO Data Skills Training Program Case Studies

This series was developed by the Chief Data Officer Council’s Data Skills & Workforce Development Working Group to provide support to agencies in implementing the Federal Data Strategy’s Agency Action 4 gap-closing strategy training component in FY21.

FederalRegister.gov API Case Study

This case study describes the tenets behind an API that provides access to all data found on FederalRegister.gov, including all Federal Register documents from 1994 to the present.

National Archives and Records Administration

PDF (3 pages)

Fuels Knowledge Graph Project

The Fuels Knowledge Graph Project (FKGP), funded through the Federal Chief Data Officers (CDO) Council, explored the use of knowledge graphs to achieve more consistent and reliable fuel management performance measures. The team hypothesized that better performance measures and an interoperable semantic framework could enhance the ability to understand wildfires and, ultimately, improve outcomes. To develop a more systematic and robust characterization of program outcomes, the FKGP team compiled, reviewed, and analyzed multiple agency glossaries and data sources. The team examined the relationships between them, while documenting the data management necessary for a successful fuels management program.

metadata , data sharing , data access

Government Data Hubs

A list of Federal agency open data hubs, including USDA, HHS, NASA, and many others.

Helping Baltimore Volunteers Find Where to Help

Bloomberg Government analysts put together a prototype through the Census Bureau’s Opportunity Project to better assess where volunteers should direct litter-clearing efforts. Using Census Bureau and Forest Service information, the team brought a data-driven approach to their work. Their experience reveals how individuals with data expertise can identify a real-world problem that data can help solve, navigate across agencies to find and obtain the most useful data, and work within resource constraints to provide a tool to help address the problem.

Census Bureau

geospatial , data sharing , Federal Data Strategy

How USDA Linked Federal and Commercial Data to Shed Light on the Nutritional Value of Retail Food Sales

Purchase-to-Plate Crosswalk (PPC) links the more than 359,000 food products in a comercial company database to several thousand foods in a series of USDA nutrition databases. By linking existing data resources, USDA was able to enrich and expand the analysis capabilities of both datasets. Since there were no common identifiers between the two data structures, the team used probabilistic and semantic methods to reduce the manual effort required to link the data.

Department of Agriculture

data sharing , process redesign , Federal Data Strategy

How to Blend Your Data: BEA and BLS Harness Big Data to Gain New Insights about Foreign Direct Investment in the U.S.

A recent collaboration between the Bureau of Economic Analysis (BEA) and the Bureau of Labor Statistics (BLS) helps shed light on the segment of the American workforce employed by foreign multinational companies. This case study shows the opportunities of cross-agency data collaboration, as well as some of the challenges of using big data and administrative data in the federal government.

Bureau of Economic Analysis / Bureau of Labor Statistics

data sharing , workforce development , process redesign , Federal Data Strategy

Implementing Federal-Wide Comment Analysis Tools

The CDO Council Comment Analysis pilot has shown that recent advances in Natural Language Processing (NLP) can effectively aid the regulatory comment analysis process. The proof-ofconcept is a standardized toolset intended to support agencies and staff in reviewing and responding to the millions of public comments received each year across government.

Improving Data Access and Data Management: Artificial Intelligence-Generated Metadata Tags at NASA

NASA’s data scientists and research content managers recently built an automated tagging system using machine learning and natural language processing. This system serves as an example of how other agencies can use their own unstructured data to improve information accessibility and promote data reuse.

National Aeronautics and Space Administration

metadata , data management , data sharing , process redesign , Federal Data Strategy

Investing in Learning with the Data Stewardship Tactical Working Group at DHS

The Department of Homeland Security (DHS) experience forming the Data Stewardship Tactical Working Group (DSTWG) provides meaningful insights for those who want to address data-related challenges collaboratively and successfully in their own agencies.

Department of Homeland Security

data governance , data management , Federal Data Strategy

Leveraging AI for Business Process Automation at NIH

The National Institute of General Medical Sciences (NIGMS), one of the twenty-seven institutes and centers at the NIH, recently deployed Natural Language Processing (NLP) and Machine Learning (ML) to automate the process by which it receives and internally refers grant applications. This new approach ensures efficient and consistent grant application referral, and liberates Program Managers from the labor-intensive and monotonous referral process.

National Institutes of Health

standards , data cleaning , process redesign , AI

FDS Proof Point

National Broadband Map: A Case Study on Open Innovation for National Policy

The National Broadband Map is a tool that provide consumers nationwide reliable information on broadband internet connections. This case study describes how crowd-sourcing, open source software, and public engagement informs the development of a tool that promotes government transparency.

Federal Communications Commission

National Renewable Energy Laboratory API Case Study

This case study describes the launch of the National Renewable Energy Laboratory (NREL) Developer Network in October 2011. The main goal was to build an overarching platform to make it easier for the public to use NREL APIs and for NREL to produce APIs.

National Renewable Energy Laboratory

Open Energy Data at DOE

This case study details the development of the renewable energy applications built on the Open Energy Information (OpenEI) platform, sponsored by the Department of Energy (DOE) and implemented by the National Renewable Energy Laboratory (NREL).

open data , data sharing , Federal Data Strategy

Pairing Government Data with Private-Sector Ingenuity to Take on Unwanted Calls

The Federal Trade Commission (FTC) releases data from millions of consumer complaints about unwanted calls to help fuel a myriad of private-sector solutions to tackle the problem. The FTC’s work serves as an example of how agencies can work with the private sector to encourage the innovative use of government data toward solutions that benefit the public.

Federal Trade Commission

data cleaning , Federal Data Strategy , open data , data sharing

Profile in Data Sharing - National Electronic Interstate Compact Enterprise

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the federal government and states support children who are being placed for adoption or foster care across state lines. It greatly reduces the work and time required for states to exchange paperwork and information needed to process the placements. Additionally, NEICE allows child welfare workers to communicate and provide timely updates to courts, relevant private service providers, and families.

Profile in Data Sharing - National Health Service Corps Loan Repayment Programs

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the Health Resources and Services Administration collaborates with the Department of Education to make it easier to apply to serve medically underserved communities - reducing applicant burden and improving processing efficiency.

Profile in Data Sharing - Roadside Inspection Data

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the Department of Transportation collaborates with the Customs and Border Patrol and state partners to prescreen commercial motor vehicles entering the US and to focus inspections on unsafe carriers and drivers.

Profiles in Data Sharing - U.S. Citizenship and Immigration Service

The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the U.S. Citizenship and Immigration Service (USCIS) collaborated with the Centers for Disease Control to notify state, local, tribal, and territorial public health authorities so they can connect with individuals in their communities about their potential exposure.

SBA’s Approach to Identifying Data, Using a Learning Agenda, and Leveraging Partnerships to Build its Evidence Base

Through its Enterprise Learning Agenda, Small Business Administration’s (SBA) staff identify essential research questions, a plan to answer them, and how data held outside the agency can help provide further insights. Other agencies can learn from the innovative ways SBA identifies data to answer agency strategic questions and adopt those aspects that work for their own needs.

Small Business Administration

process redesign , Federal Data Strategy

Supercharging Data through Validation as a Service

USDA's Food and Nutrition Service restructured its approach to data validation at the state level using an open-source, API-based validation service managed at the federal level.

data cleaning , data validation , API , data sharing , process redesign , Federal Data Strategy

The Census Bureau Uses Its Own Data to Increase Response Rates, Helps Communities and Other Stakeholders Do the Same

The Census Bureau team produced a new interactive mapping tool in early 2018 called the Response Outreach Area Mapper (ROAM), an application that resulted in wider use of authoritative Census Bureau data, not only to improve the Census Bureau’s own operational efficiency, but also for use by tribal, state, and local governments, national and local partners, and other community groups. Other agency data practitioners can learn from the Census Bureau team’s experience communicating technical needs to non-technical executives, building analysis tools with widely-used software, and integrating efforts with stakeholders and users.

open data , data sharing , data management , data analysis , Federal Data Strategy

The Mapping Medicare Disparities Tool

The Centers for Medicare & Medicaid Services’ Office of Minority Health (CMS OMH) Mapping Medicare Disparities Tool harnessed the power of millions of data records while protecting the privacy of individuals, creating an easy-to-use tool to better understand health disparities.

Centers for Medicare & Medicaid Services

geospatial , Federal Data Strategy , open data

The Veterans Legacy Memorial

The Veterans Legacy Memorial (VLM) is a digital platform to help families, survivors, and fellow veterans to take a leading role in honoring their beloved veteran. Built on millions of existing National Cemetery Administration (NCA) records in a 25-year-old database, VLM is a powerful example of an agency harnessing the potential of a legacy system to provide a modernized service that better serves the public.

Veterans Administration

data sharing , data visualization , Federal Data Strategy

Transitioning to a Data Driven Culture at CMS

This case study describes how CMS announced the creation of the Office of Information Products and Data Analytics (OIPDA) to take the lead in making data use and dissemination a core function of the agency.

data management , data sharing , data analysis , data analytics

PDF (10 pages)

U.S. Department of Labor Case Study: Software Development Kits

The U.S. Department of Labor sought to go beyond merely making data available to developers and take ease of use of the data to the next level by giving developers tools that would make using DOL’s data easier. DOL created software development kits (SDKs), which are downloadable code packages that developers can drop into their apps, making access to DOL’s data easy for even the most novice developer. These SDKs have even been published as open source projects with the aim of speeding up their conversion to SDKs that will eventually support all federal APIs.

Department of Labor

open data , API

U.S. Geological Survey and U.S. Census Bureau collaborate on national roads and boundaries data

It is a well-kept secret that the U.S. Geological Survey and the U.S. Census Bureau were the original two federal agencies to build the first national digital database of roads and boundaries in the United States. The agencies joined forces to develop homegrown computer software and state of the art technologies to convert existing USGS topographic maps of the nation to the points, lines, and polygons that fueled early GIS. Today, the USGS and Census Bureau have a longstanding goal to leverage and use roads and authoritative boundary datasets.

U.S. Geological Survey and U.S. Census Bureau

data management , data sharing , data standards , data validation , data visualization , Federal Data Strategy , geospatial , open data , quality

USA.gov Uses Human-Centered Design to Roll Out AI Chatbot

To improve customer service and give better answers to users of the USA.gov website, the Technology Transformation and Services team at General Services Administration (GSA) created a chatbot using artificial intelligence (AI) and automation.

General Services Administration

AI , Federal Data Strategy

resources.data.gov

An official website of the Office of Management and Budget, the General Services Administration, and the Office of Government Information Services.

This section contains explanations of common terms referenced on resources.data.gov.

Top 20 Analytics Case Studies in 2024

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Although the potential of Big Data and business intelligence are recognized by organizations, Gartner analyst Nick Heudecker says that the failure rate of analytics projects is close to 85%. Uncovering the power of analytics improves business operations, reduces costs, enhances decision-making , and enables the launching of more personalized products.

In this article, our research covers:

How to measure analytics success?

What are some analytics case studies.

According to  Gartner CDO Survey,  the top 3 critical success factors of analytics projects are:

• Creation of a data-driven culture within the organization,
• Data integration and data skills training across the organization,
• And implementation of a data management and analytics strategy.

The success of the process of analytics depends on asking the right question. It requires an understanding of the appropriate data required for each goal to be achieved. We’ve listed 20 successful analytics applications/case studies from different industries.

During our research, we examined that partnering with an analytics consultant helps organizations boost their success if organizations’ tech team lacks certain data skills.

*Vendors have not shared the client name

For more on analytics

If your organization is willing to implement an analytics solution but doesn’t know where to start, here are some of the articles we’ve written before that can help you learn more:

• AI in analytics: How AI is shaping analytics
• Edge Analytics in 2022: What it is, Why it matters & Use Cases
• Application Analytics: Tracking KPIs that lead to success

Finally, if you believe that your business would benefit from adopting an analytics solution, we have data-driven lists of vendors on our analytics hub and analytics platforms

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Introduction to Statistics and Data Analysis – A Case-Based Approach

Suggested citation:

Ziller, Conrad (2024). Introduction to Statistics and Data Analysis – A Case-Based Approach. Available online at https://bookdown.org/conradziller/introstatistics

To download the R-Scripts and data used in this book, go HERE .

Motivation for this Book

This short book is a complete introduction to statistics and data analysis using R and RStudio. It contains hands-on exercises with real data—mostly from social sciences. In addition, this book presents four key ingredients of statistical data analysis (univariate statistics, bivariate statistics, statistical inference, and regression analysis) as brief case studies. The motivation for this was to provide students with practical cases that help them navigate new concepts and serve as an anchor for recalling the acquired knowledge in exams or while conducting their own data analysis.

The case study logic is expected to increase motivation for engaging with the materials. As we all know, academic teaching is not the same as before the pandemic. Students are (rightfully) increasingly reluctant to chalk-and-talk techniques of teaching, and we have all developed dopamine-related addictions to social media content which have considerably shortened our ability to concentrate. This poses challenges to academic teaching in general and complex content such as statistics and data science in particular.

How to Use the Book

This book consists of four case studies that provide a short, yet comprehensive, introduction to statistics and data analysis. The examples used in the book are based on real data from official statistics and publicly available surveys. While each case study follows its own logic, I advise reading them consecutively. The goal is to provide readers with an opportunity to learn independently and to gather a solid foundation of hands-on knowledge of statistics and data analysis. Each case study contains questions that can be answered in the boxes below. The solutions to the questions can be viewed below the boxes (by clicking on the arrow next to the word “solution”). It is advised to save answers to a separate document because this content is not saved and cannot be accessed after reloading the book page.

A working sheet with questions, answer boxes, and solutions can be downloaded together with the R-Scrips HERE . You can read this book online for free. Copies in printable format may be ordered from the author.

This book can be used for teaching by university instructors, who may use data examples and analyses provided in this book as illustrations in lectures (and by acknowledging the source). This book can be used for self-study by everyone who wants to acquire foundational knowledge in basic statistics and practical skills in data analysis. The materials can also be used as a refresher on statistical foundations.

Beginners in R and RStudio are advised to install the programs via the following link https://posit.co/download/rstudio-desktop/ and to download the materials from HERE . The scripts from this material can then be executed while reading the book. This helps to get familiar with statistical analysis, and it is just an awesome feeling to get your own script running! (On the downside, it is completely normal and part of the process that code for statistical analysis does not work. This is what helpboards across the web and, more recently, ChatGPT are for. Just google your problem and keep on trying, it is, as always, 20% inspiration and 80% consistency.)

Organization of the Book

The book contains four case studies, each showcasing unique statistical and data-analysis-related techniques.

• Section 2: Univariate Statistics – Case Study Socio-Demographic Reporting

Section 2 contains material on the analysis of one variable. It presents measures of typical values (e.g., the mean) and the distribution of data.

• Section 3: Bivariate Statistics - Case Study 2020 United States Presidential Election

Section 3 contains material on the analysis of the relationship between two variables, including cross tabs and correlations.

• Section 4: Statistical Inference - Case Study Satisfaction with Government

Section 4 introduces the concept of statistical inference, which refers to inferring population characteristics from a random sample. It also covers the concepts of hypothesis testing, confidence intervals, and statistical significance.

• Section 5: Regression Analysis - Case Study Attitudes Toward Justice

Section 5 covers how to conduct multiple regression analysis and interpret the corresponding results. Multiple regression investigates the relationship between an outcome variable (e.g., beliefs about justice) and multiple variables that represent different competing explanations for the outcome.

Acknowledgments

Thank you to Paul Gies, Phillip Kemper, Jonas Verlande, Teresa Hummler, Paul Vierus, and Felix Diehl for helpful feedback on previous versions of this book. I want to thank Achim Goerres for his feedback early on and for granting me maximal freedom in revising and updating the materials of his introductory lectures on Methods and Statistics, which led to the writing of this book. Earlier versions of this book have been used in teaching courses on statistics in the Political Science undergraduate program at the University of Duisburg-Essen.

About the Author

Conrad Ziller is a Senior Researcher in the Department of Political Science at the University of Duisburg-Essen. His research interests focus on the role of immigration in politics and society, immigrant integration, policy effects on citizens, and quantitative methods. He is the principal investigator of research projects funded by the German Research Foundation and the Fritz Thyssen Foundation. More information about his research can be found here: https://conradziller.com/ .

The final part of the book is about linear regression analysis, which is the natural endpoint for a course on introductory statistics. However, the “ordinary” regression is where many further useful techniques come into play—most of which can subsumed under the label “Advanced Regression Models”. You will need them when analyzing, for example, panel data where the same respondents were interviewed multiple times or spatially clustered data from cross-national surveys.

I will extend this introduction with case studies on advanced regression techniques soon. If you want to get notified when this material is online, please sign up with your email address here: https://forms.gle/T8Hvhq3EmcywkTdFA .

In the meantime, I have a chapter on “Multiple Regression with Non-Independent Observations: Random-Effects and Fixed-Effects” that can be downloaded via https://ssrn.com/abstract=4747607 .

For feedback on the usefulness of the introduction and/or reports on errors and misspellings, I would be utmost thankful if you would send me a short notification at [email protected] .

Thanks much for engaging with this introduction!

The online version of this book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License .

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Qualitative case study data analysis: an example from practice

Affiliation.

• 1 School of Nursing and Midwifery, National University of Ireland, Galway, Republic of Ireland.
• PMID: 25976531
• DOI: 10.7748/nr.22.5.8.e1307

Aim: To illustrate an approach to data analysis in qualitative case study methodology.

Background: There is often little detail in case study research about how data were analysed. However, it is important that comprehensive analysis procedures are used because there are often large sets of data from multiple sources of evidence. Furthermore, the ability to describe in detail how the analysis was conducted ensures rigour in reporting qualitative research.

Data sources: The research example used is a multiple case study that explored the role of the clinical skills laboratory in preparing students for the real world of practice. Data analysis was conducted using a framework guided by the four stages of analysis outlined by Morse ( 1994 ): comprehending, synthesising, theorising and recontextualising. The specific strategies for analysis in these stages centred on the work of Miles and Huberman ( 1994 ), which has been successfully used in case study research. The data were managed using NVivo software.

Review methods: Literature examining qualitative data analysis was reviewed and strategies illustrated by the case study example provided. Discussion Each stage of the analysis framework is described with illustration from the research example for the purpose of highlighting the benefits of a systematic approach to handling large data sets from multiple sources.

Conclusion: By providing an example of how each stage of the analysis was conducted, it is hoped that researchers will be able to consider the benefits of such an approach to their own case study analysis.

Implications for research/practice: This paper illustrates specific strategies that can be employed when conducting data analysis in case study research and other qualitative research designs.

Keywords: Case study data analysis; case study research methodology; clinical skills research; qualitative case study methodology; qualitative data analysis; qualitative research.

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Fundamentals of Data Analysis in Excel – Case Study

• Construct data using a variety of functions and formulas
• Present data with a mixture of formatting and structures
• Visualize data with Excel Charts

• What You'll Learn
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Data Analysis in Excel Case Study Overview

Dive into the world of Excel data analysis with this engaging case study, featuring seven unique challenges. Each challenge, growing progressively more complex, offers a practical opportunity to sharpen your Excel skills through real-world problem-solving scenarios. You’ll be provided with different datasets for each challenge, where your task is to manipulate, analyze, or visually represent the data using Excel’s diverse set of tools. These challenges are not just about mastering Excel functions; they’re designed to apply these skills to solve actual problems you might encounter professionally.

Data Analysis in Excel Case Study Learning Objectives

By the end of the practice lab, you should be able to:

• Transform data with conditional formulas, Lookup functions, and SUMPRODUCT
• Analyze data to highlight insights with conditional formatting, Excel Tables, and Dynamic Arrays
• Visualize data effectively by creating and formatting Excel Charts

Who Should Take This Case Study?

This Excel Case Study is perfect for those who want to put their knowledge about Data Analysis in Excel into practice with selected, real-life scenarios. This makes the case study a great follow-up to BIDA’s Fundamentals of Data Analysis in Excel.

Prerequisite

Ideally some basic Excel skills including formulas and basic pivot tables.

Software Requirements

Fundamentals of data analysis in excel - case study.

Approx 3h to complete

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What you'll learn

Case study introduction, looking up data, conditional formulas, conditional formatting, excel tables, dynamic arrays, visualizing data, case study wrap-up, qualified assessment, this course is part of the following programs.

Why stop here? Expand your skills and show your expertise with the professional certifications, specializations, and CPE credits you’re already on your way to earning.

Business Intelligence & Data Analyst (BIDA®) Certification

• Skills Learned Data visualization, data warehousing and transformation, data modeling and analysis
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Data Analysis in Excel Specialization

• Skills Learned Data Modelling & Analysis, Data Transformation, Data Visualization
• Career Prep Data Analyst, Business Intelligence Specialist, Data Scientist, Finance Analyst

Business Intelligence Analyst Specialization

• Skills learned Data Transformation & Automation, Data Visualization, Coding, Data Modeling
• Career prep Data Analyst, Business Intelligence Specialist, Finance Analyst, Data Scientist

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Title: journey-based transit equity analysis: a case study in the greater boston area.

Abstract: In this paper, a new methodology, journey-based equity analysis, is presented for measuring the equity of transit convenience between income groups. Two data sources are combined in the proposed transit equity analysis: on-board ridership surveys and passenger origin-destination data. The spatial unit of our proposed transit equity analysis is census blocks, which are relatively stable over time and allows an exploration of the data that is granular enough to make conclusions about the service convenience various communities are facing. A case study in the Greater Boston area using real data from the Massachusetts Bay Transportation Authority (MBTA) bus network demonstrates a significant difference in transit service convenience, measured by number of transfers per unit distance, transfer wait time and travel time per unit distance, between low-income riders and high income riders. Implications of analysis results to transit agencies are also discussed.

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Transforming Childhood Vaccination Rates in Rural Egypt: A Case Study on Results-Based Management in Healthcare Programs

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This case study examines the implementation of a results-based management (RBM) approach in a childhood vaccination program across rural Egypt. The project, initiated in 2020, aimed to address the persistently low immunization rates in remote areas by restructuring healthcare delivery and resource allocation. The study details how the RBM framework was applied to set clear, measurable objectives, develop key performance indicators, and establish a robust monitoring and evaluation system. It highlights the innovative use of mobile health technologies for data collection and analysis, enabling real-time adjustments to the program strategy. Over a three-year period, the initiative achieved a remarkable 40% increase in vaccination coverage, significantly reducing the incidence of preventable childhood diseases in the target regions. The case study explores the challenges encountered, including cultural barriers and logistical hurdles, and describes the adaptive management techniques employed to overcome these obstacles. This research provides valuable insights into the effective application of RBM principles in resource-constrained settings, demonstrating how data-driven decision-making and stakeholder engagement can lead to substantial improvements in public health outcomes. The findings offer practical guidelines for healthcare managers and policymakers seeking to enhance the efficiency and impact of their programs in similar contexts.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study was supported by the HealthForAll Fund under grant number HF-EA-3849.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study protocol was reviewed and approved by the ResearchOcrats Health Ethics Committee (Approval No. ERC-2049-015). Informed consent was obtained from all study participants, and data anonymization procedures were implemented to protect participant privacy.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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The oral health care system in Libya: a case study

• Aisha Aloshaiby 1   na1 ,
• Amal Gaber 1   na1 &
• Arheiam Arheiam 1  

BMC Oral Health volume  24 , Article number:  888 ( 2024 ) Cite this article

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Metrics details

This study aims to describe the Libyan oral health care system in terms of its structure, function, workforce, funding, reimbursement and target groups.

A single descriptive case study approach and multiple sources of data collection were used to provide an in-depth understanding of the Libyan oral health care system. A purposeful sample of the key informants (Managers of oral health centers, dentists of various specialties with experience in the field, dentists, nurses, dental technicians, and officials in the affairs of medical insurance) was recruited. The case and its boundaries were guided by the study’s aim. Both qualitative and quantitative analyses were conducted. Descriptive statistics were used for quantitative data. Framework analysis, informed by the study objectives, was used to analyze interviews and documents.

The analysis showed that oral health services are integrated into medical services. The provision of dental care is mainly treatment-based, in the private sector. The oral health services in the public sector are mainly emergency care and exodontia. The dental workforce included in the study were mostly dentists (89% General Dental Practitioners (GDPs), 11% specialists), with a marked deficiency in dental technicians and nurses. Around 40% of dentists work in both the private and public sectors. The government provides the funding for the public sector, but the private sector is self-funded. No specific target group(s) nor clear policies were reported. However, the system is built around primary health care as an overarching policy. Dental caries is the most common oral problem among Libyan preschool children affecting around 70% and is the most common cause of tooth loss among adults.

The oral health care system in Libya is mainly privatized. The public health services are poorly organized and malfunctioning. There is an urgent need to develop policies and plans to improve the oral health care system in Libya.

Peer Review reports

Introduction

A health care system is a complex organization whose primary goal is to promote, restore or maintain health. It includes all institutions, people and actions that work together to achieve its aim [ 1 ]. According to the World Health Organization (WHO), a health care system includes service delivery, health workforce, information, medical products, vaccines and technologies, financing, and leadership/governance [ 2 ]. Dentistry is only one component of the broader health care system. According to Gift and Andersen, any oral health care system can be described in terms of six aspects (Structure, Functions, Personnel, Funding, Reimbursement and Target population) which vary in their application in different countries [ 3 ]. In addition, health care systems are not static and influenced by many factors such as demographic changes, advances in technology, expectations and a country’s economic and political situation [ 4 , 5 ]. Disparities in human and financial resources, dental workforce and the provision of health services between developed and developing countries are well documented [ 6 ]. It is, therefore, crucial for each country to regularly examine its health care system to ensure that it is taking account of population changes, health needs, workforce numbers, skills and expectations [ 7 ].

In recent years many countries have been affected by political, security, economic, and social challenges that have significant impact on health care services [ 8 ]. Libya is one of the Arab League countries that went through turmoil of political, armed and economic crises since February 2011 [ 9 ]. As a result, the Libyan health care system which was once a model of success for other developing countries, is negatively affected [ 10 ]. Although several conferences and workshops were organized by local and international agencies to assess and address the challenges facing the Libyan health care system [ 11 ], little attention has been given to oral health care which has its own challenges. For instance, there is an unprecedented increase in the number of graduating dentists at the expense of their quality [ 12 ]. Moreover, recently published studies highlighted highly unmet treatment needs among Libyan children and adults [ 13 , 14 , 15 ]. It is important to understand the dynamics of the oral health care system in Libya to inform future planning of oral health services. Therefore, the present study aims to describe the oral health care system in Libya according to the six aspects suggested by Gift and Andersen [ 3 ], which include Structure, Functions, Personnel, Funding, Reimbursement and Target population.

Study design

A single descriptive, exploratory case study design with a mix of qualitative and quantitative data collection tools was used to describe the Libyan oral health care system. This approach allows the triangulation of evidence from multiple sources and a comprehensive and detailed understanding of the studied phenomenon [ 16 , 17 , 18 ].

The case is defined as the oral health care system in Libya. The case boundaries were guided by the study research question and the aspects of the oral health care system according to Gift and Andersen [ 3 ] as follows: (1) Structure: how the system is structured; (2) Functions: what the system set out to achieve; (3) Personnel: who delivers the work; (4) Funding: where the funds are derived from; (5) Reimbursement: how workers are paid; (6) Target population: which groups are prioritized.

The study was conducted in Benghazi, the second-largest city in Libya, encompassing both urban and rural districts. Although Libya is one the largest countries in Africa, it has around seven million inhabitants mainly living in northern cities and most of them live in Tripoli and Benghazi. The city of Benghazi has the oldest dental school in Libya. The city of Benghazi has a full range of oral health care facilities and services and hence is considered a representative of the Libyan oral health care system.

Data collection

Three strands of data collection were carried out sequentially: (1) semi-structured qualitative interviews; (2) documentary analysis; and (3) a questionnaire survey. The data was collected from the chief staff working in the Ministry of Health (MoH), health insurance companies, administrative personnel, business owners, and service providers (dental practitioners and dental auxiliaries).

Qualitative interviews

Semi-structured interviews with a purposeful sample of the key informants (Managers of oral health centers and hospitals, dentists of various specialties with experience in the field, nurses, dental technicians, and officials in the affairs of medical insurance) were recruited for qualitative data collection. Chief staff of health care institutions and senior dental professionals were first identified by contacting the officials in the MoH and by consulting the senior staff in the dental school. The informants were invited to take part in the study. The aim of the study was explained and the informants were handed out study information sheets and a consent form. Another appointment was then decided to conduct the interview. A snowballing of the study sample was then applied to identify the potential key informants who were approached at their workplace.

The interviews were undertaken by the principal investigator (PI) who received special training in conducting interviews. All interviews were audio-recorded and took place in a quiet non-clinical room. The interviews were conducted using a topic guide based on the research question and the aspects of the oral health care system according to Gift and Andersen [ 3 ]. They started with an open question: How do you describe the oral health care system in Libya? The data collection continued until no new information was obtained which is known as saturation [ 16 ].

Document analysis

Documentary analysis was conducted to examine the relevant reports, policies, service documents, and academic publications. All accessible electronic and paper reports at local and national levels were included. While studies published in the last ten years were only included to obtain current health status information, searching policies and other reports were not limited to a specific time. Relevant documents were identified through searches in electronic databases like PubMed and Google Scholar, as well as governmental websites. Additionally, grey literature was sourced by reaching out to key individuals in academic and healthcare institutions [ 16 ].

Questionnaire survey

A self-administered, paper-based questionnaire survey was conducted among dentists in Benghazi. A purposeful sample approach was adopted to recruit participants who were selected in a way that ensures including a range of dentists who represent various oral health care settings and facilities and years of experience to reflect the range of dental care provided in the city. Therefore, the dental facilities were the sampling units. In each dental facility, dentists were approached personally and invited to take part in the survey. The questionnaire was designed by the research team and the questions revolved around the six components of the oral health care system and were informed by the findings of qualitative analysis. The questionnaire was pre-tested for clarity and understandability among a group of 10 dentists. Most of the feedback received was related to question wording, which was modified accordingly. The final questionnaire comprised of sixteen questions. These were five open-ended questions and eleven close-ended questions (Yes/No and multiple-choice, eight of them have an open option). Due to the exploratory nature of the study, a free text response section was included at the end of each structured question, allowing participants to add any additional comments. The final questionnaire had 6 sections representing the components of the oral health care system and an introductory section collecting professional and socio-demographic characteristics of the participants. The questionnaire took on average 15 min to be completed.

The PI handed the questionnaire to the dentists in their work setting and explained the aim of the study. The PI was available (via phone) to clarify any issue related to the study and the questionnaire. The questionnaires were collected within a week from the reception desk. The consent to take part in the study was implicit by returning a completed questionnaire.

Data analysis

The quantitative and qualitative analyses were conducted separately and then integrated into one framework comprised of the pre-defined six components of the oral health care system.

Qualitative data

A framework analysis approach was performed to analyze the qualitative interviews and retrieved documents according to the six components of oral health care system [ 17 , 19 ]. The analysis process started with initial familiarization through listening to the audiotapes, reading and re-reading of interviews transcribed verbatim to gain an overview of ranges and diversities of the gathered material. The analysis was conducted concurrently with data collection and emerging codes and sub-themes were explored in the subsequent interviews. Only data that showed relevance to research questions were coded. Finally, the coded data were organized into overarching six themes representing the components of the oral health care system. A discussion among the research team was held to remove unsupported themes, create new themes, reduce homogenous themes together, and split heterogeneous ones Then data extracts were selected to be presented in the research context. The validity of the analysis has been achieved with the participation of an experienced researcher other than the principal investigator in the analysis. In addition, a third person from outside the dental field was involved in confirming that the extracts are representative to the themes emerged from the analysis.

Quantitative data

Descriptive quantitative analysis was undertaken to summarize the distribution of study sample characteristics. The answers to free-text questions were analyzed using a qualitative content analysis strategy, which involved categorization of the answers [ 20 ].

Characteristics of study samples

Twelve qualitative interviews were conducted. The interviewees were dentists working in different sectors and facilities (5), chief staff, university and health insurance (3), dental nurses (2), and dental technicians (2) (appendix 2 ). In the questionnaire survey, a total of 121 out of 150 questionnaires were received with complete information, suitable for data analysis, giving a response rate of 80.6%. The participants aged between 23 and 63 years of age, and almost three-quarters of them were females (89, 73.6%), The majority of the participants were GDPs (108, 89.3%), and held only Bachelor of Dental Surgery (BDS). The participants worked in the public sector, private sector and both sectors were, respectively, 33.9%, 26.4%, and 39.7%. (Table  1 )

Components of the Libyan oral health care system

Structure: how the system is structured.

The Libyan oral health care system is a hybrid system comprised of public and private sectors. Dental services were provided in medical polyclinics/hospitals or through a standalone dental facility (Table  2 ).

Dental care in the public sector is mainly run by MoH. However, non-MoH facilities also provide dental services such as that affiliated to educational institutions for training purposes or other health service facilities affiliated to the ministries of defense, justice and social services.

Informant (5): “Not all health institutions are affiliated with the Ministry of Health; dental services are also provided by other facilities that do not belong to MoH. These can be classified as educational which includes teaching hospitals and training centers , and service-oriented such as ministries of defense and interior and social care institutions”.

Dental services are generally integrated within health care facilities providing medical services. However, this is not always the case and there are separate dental facilities that exclusively provide dental care, such as the Specialized Dental Center and dental schools. Exclusively dental care facilities are mainly in the private sector taking the form of solo or multiple units’ dental practices and private dental schools’ clinics Interestingly, only three out of the six dental schools have their own private dental clinics for training and providing dental services. The other three schools are newly established and do not yet have dental clinics. Yet, private medical polyclinics and hospitals including dental units provide dental care.

Informant (1): “There are dental services that are provided in private clinics and private hospitals through departments in these clinics and hospitals , and some dental services are provided as a part of the dental training courses for dental students at universities or private institutes”.

Informant (4): “In the private sector , dental clinics are mostly found as multi-chair centers , 5 dental chairs or more , but also there are places with one or two chairs”.

The MoH has a two-level administrative hierarchy (Fig.  1 ). The national level is expected to offer secondary and tertiary care level. Facilities at the national level are under the direct supervision of the MoH and include general, central and specialized hospitals, rural hospitals, including the Specialized Dental Center.

Informant (4): “The Specialized Dental Center is the only institution that is directly affiliated to the Ministry of Health and funded by the Ministry directly”.

The local level is organized into Regional Health Service Administration that is responsible for health units, centers, polyclinics, and primary health care facilities.

Schematic administrative distribution of dental facilities related to MoH

Dental services are distributed throughout health care facilities at both local and national levels. However, the integration of dental care into medical service did not mean providing the same primary, secondary and tertiary care. Instead, general dental services with unclear distinctions between care levels are provided. Any type of dental care can be provided in any health facility depending on the availability of resources.

Informant (5): “No we are not aware of primary , secondary , or tertiary services in dentistry. All services are provided according to availability. In Aljala trauma hospital we treat maxillofacial trauma and injuries but also there is a dental unit to provide dental care such as simple filling and extraction though not always available because of shortage of LA or dental materials”.

Function: what does the system set out to achieve?

The available policy documents indicate that the Libyan health care system adopts a primary health care preventive services policy and provides emergency dental care to all people but this was not the case on the ground.

Informant (7): “The services required from the public sector are supposed to be all services related to pain removal , whether with or without treatment , but in reality , the state is currently unable to provide these services. It only provides services such as examination and simple extraction , due to the lack of capabilities”.

The dental services were treatment-oriented and mainly provided in the private sector (Table  3 ). Preventive dental services and primary health care services are rarely provided and are limited to volunteer activities by some non-governmental organizations and educational institutions as part of their training.

Informant (1): “In the past , the school programs and primary health care services provided along with mother and child services were active , but now none of this exists. It is only the building and a small staff without any activities”.

Informant (6): “Most oral health education campaigns are performed by scientific groups such as community and pediatric associations and internship students with support from toothpaste companies”.

In the private sector, a whole range of dental services is available. Private dental centers were well-equipped with dental facilities and experienced staff. On the other hand, the dental services provided in the public sector were limited to diagnostic services and exodontia. An exception to this is the Specialized Dental Center which offers a range of services such as restorative dentistry, minor oral surgery, periodontics and removable prosthodontics.

Informant (2): “The only place in the public sector that provides all dental services is the Specialized Dental Center”.

Informant (4): “Private dental centers are well-equipped with dental facilities; the whole range of dental services are provided in this sector”.

The analysis of published studies indicates that there are highly unmet treatment needs among Libyan children and adults. More than 40% of Libyan children had untreated dental caries in their primary and permanent teeth (Table  4 ). In addition, Untreated caries and severe periodontitis are the most common reasons for tooth loss among Libyan adults [ 21 , 22 ].

Personnel: who delivers the work

The dental workforce in the Libyan health care system is comprised of dentists, dental nurses, and dental technicians. The number of dentists has increased markedly in the last 10 years (Fig.  2 shows the rising number of dental graduates in one dental school) following the increased admission of new dental students in the only government dental school at the University of Benghazi. In addition, Benghazi hosts six private dental schools. One of these schools has graduated approximately 340 dentists over the past decade, with a recent increase in new student enrollment. Two of the schools have students in their final year of study, while the remaining three are newly established.

Informant (3): “The increase in the number of private universities , which are 6 universities , made the situation worse and caused overcrowding of dental graduates”.

Informant (4): “There is no control over the numbers of dental students despite the attempts to restrict the inclusion rate and the result is that there are large numbers of dentists and most of them are jobless”.

Most dentists work as general dental practitioners, with shortage in numbers of specialists in orthodontics and maxillofacial surgery.

Informant (6): “At the level of Benghazi , there is a big shortage of dental specialists in Orthodontics and Maxillofacial surgery”.

Source: Registrar’s Office, Faculty of Dentistry, University of Benghazi

Number of dental graduates at the University of Benghazi between 1979 and 2022.

Other dental care personnel in the Libyan oral health care system includes dental laboratory technicians and dental nurses. None of them are allowed to work on dental patients. There is a clear shortage in the numbers of qualified dental nurses.

Informant (4): “Throughout my career as a dentist , I met a few officially trained dental nurses. Most of nurses worked for me are either medical nurses , dental hygienist or I trained them to be dental assistant.”

In recent years, dental hygienists have emerged as a new dental care personnel category. Dental hygienists are dental auxiliaries who are trained to provide preventive dental care and oral prophylaxis, unlike dental nurses who are not allowed to provide any type of dental care. However, the Libyan health care system does not recognize them as a specialty and hence they left without jobs in the public sector and many of them work as nurses in private practices.

Informant (3): “I graduated from the Higher Institute of Medical Professions as a dental hygienist but in reality , our profession is not recognized in Libyan law , and legally it is forbidden for anyone except the dentists to work on the patients. Therefore , most of us were left without jobs or worked as dental nurses in the private clinics , if they are lucky”.

Funding: where the funds are derived from

The public dental services are funded by the government through the National Bureau of Medical Supplies. The Libyan health services including dental care, are provided free of charge. The public oral health care system in Libya relies heavily on the state’s general budget, allocating approximately 4% of Libya’s Gross Domestic Product to health care [ 28 ]. In addition, national-level facilities receive a separate fund to purchase required equipment and consumables, but in recent years dental services have been less prioritized and many non-governmental organizations and companies provided dental supplies as informal support.

Informant (5): “The Libyan public health sector is funded by the Ministry of finance and operated through the medical supply unit and offers all dental materials and equipment”.

Informant (6): “In recent years , the resources are very scarce and most dental supplies are offered as support from private organizations and companies”.

The private sector is self-funded. The patients usually pay fees for service or through one of the insurance schemes offered by some companies such as oil companies and banks to the employees and their families. The insurance schemes usually cover diagnostic and treatment services but do not include prosthodontic and orthodontic treatment.

Informant (5): “In private , financing is done through the fees paid by patients and insurance companies”.

Reimbursement: how workers are paid

The reimbursement in the Libyan oral health care system includes salaries and a proportion of income. Fixed salaries are provided to those who work in the public sectors and dental auxiliaries in the private sector. Most dentists working in the private sector are reimbursed by the pre-agreed proportion of their income.

Informant (5): “All dental workers in the public sector and dental nurses in the private sector receive a fixed salary”.

Informant (7): “The reimbursement in the private sector varies according to the agreement between the employer and the dentist or dental technicians. It is usually a pre-agreed percentage of the income incurred by the dentist and often ranges between 30% and 70% , depending on the years of experience and qualifications”.

Target population: prioritized groups

There are no specified target groups in the Libyan oral health care system. The private sector provides care on demand to anyone seeking dental services, while the public sector operates on the principle of health for all. Consequently, all groups are targeted at their point of entry into the system. For instance, psychiatric patients receive care at the psychiatry hospital, which includes a dental unit. Similarly, other groups with special needs, such as the mentally disabled, diabetics, and the elderly, are also targeted.

Informant (4): “There are no places that have priorities. Dental services are distributed through all medical facilities , such as centers for communicable diseases , diabetes , mental illnesses , and the disabled , and large hospitals”.

The present exploratory case study set out to describe the Libyan oral health care system in terms of its structure, function, personnel, funding, reimbursement and target groups. The Libyan oral health care system, like many other countries [ 5 , 29 , 30 ] is generally comprised of the public sector and a pre-dominating private sector. However, the privatization of the Libyan oral health care system appeared to be the result of the chaotic nature of the public dental services which were managed by multiple bodies (MoH and Non-MoH), integrated into the medical services and mostly limited to routine oral examinations and simple extraction with scarcity of resources.

The public dental services in Libya were poorly functioning which might be attributed to the low priority given to dental care in the light of limited funding and increased health care demands; evoked by the ongoing political and armed conflict in the country since the 2011 uprisings [ 31 ]. Although the health care policy in Libya promotes equal and free access to health services, it has not been implemented appropriately. Therefore, there is a need for dental services reform to embrace the primary health care approach and ensure universal access to care [ 32 ]. There are many initiatives in the region already in place that enhance the accessibility of dental care, such as including dental services in the national health insurance program [ 33 , 34 ]. Moreover, establishing a separated administrative body for dental services, as is the case in Saudia Arabia, can ensure the appropriate allocation of resources and support of oral health research and promotion intatives [ 35 ].

The private sector, on the other hand, is self-funded and independently regulated. As a result, the provision of dental care is thriving and subject to quality-based competition. In addition, fixed salary is the only reimbursement method in the public sector which could have negatively affected the staff’s motivation and the type and quality of services provided as indicated in other research [ 36 ]. On the other hand, reimbursement in the private sector is linked to the type of services provided and the income generated, which appears to be a motivating factor for delivering high-quality and varied dental care. Additionally, financial factors have been identified by Libyan dentists as a significant barrier to providing preventive dental services [ 37 ].

An important aspect explored in the current study was the Libyan dental workforce. It was found that the dental care personnel in Libya include mainly dentists, dental nurses, dental technicians, and dental hygienists. Anecdotal evidence suggests that the number of dental graduates and dental schools is increasing in other parts of Libya. It is estimated that the dentist-population ratio in Libya is approximately 8.8 per 10,000 individuals, which exceeds the recommended global ratio and is higher than that observed in other countries in the region [ 38 , 39 , 40 , 41 , 42 , 43 ].

The high influx of Libyan dentists who graduated with questionable quality and limited job opportunities is attributed to the lack of coordination between health and education authorities which resulted in large numbers of dental students beyond the capacity of the low-resourced governmental dental schools and establishment of large number of private dental schools [ 12 ].

On the other hand, there is a clear deficiency in the number of qualified dental nurses. This may partly be explained by the absence of an official dental nursing training program in any Libyan educational institution over the past two decades. Alternately, there was a program for dental hygienists, but it is not a recognized profession in Libya. Consequently, graduated dental hygienists were not allowed to work in the public sector and many of them work in the private sector as dental nurses. Taken together, these observations highlight both quality and planning problems related to the Libyan dental workforce and the provision of dental care which require urgent interventions at the national level to control the dental education and training sector.

Unlike many developed countries that have established initiatives to target special groups such as elderly people, pregnant women, and low-income families [ 44 , 45 , 46 ], there are no specific target groups in the Libyan oral health care system. This may be attributed to the fact that the Libyan health care system was originally built on the idea of integrating primary oral health services at multiple points of entry for the entire population. However, targeting special groups requires the establishment of dental data infrastructure and surveillance data to guide these efforts.

This study has some limitations worth discussing. First, the study was conducted in the city of Benghazi as a representative of the whole country. There could be small differences in the numbers and distribution of dental facilities, such as private dental schools, which are concentrated in Benghazi and Tripoli. However, the Libyan health care system is centrally organized, and anecdotal evidence indicates similar elements of the health care system across the country. Additionally, the use of semi-structured interviews may be influenced by the personal thoughts and ideas of the interviewees, and it can be time-consuming. Furthermore, the documentary analysis might be limited by the availability of documents online and other grey materials. However, the researcher expanded the search beyond online resources and contacted key informants in health care facilities to ensure covering as many resources as possible.

Conclusions

The oral health care system in Libya is comprised of general and private sectors, although it is mainly privatized. The public dental services are poorly functioning, with highly unmet treatment needs, inappropriate implementation of primary health care policies, and uncontrolled production of the dental workforce. There is an urgent need to develop policies and plans to improve the oral health care system in Libya, involving both health and education parties. Establishing a separate administrative body for oral health services and reorienting oral health programs to meet the needs of underprivileged populations, such as children and elderly people, is highly recommended. Supporting dental schools to admit smaller numbers of dental students and provide appropriate training can enhance the quality of the dental workforce, increase the availability of dental services, and enable better control of the emerging workforce. Fostering collaboration between health and educational providers is of paramount importance for an appropriately functioning oral health care system.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Bachelor of Dental Surgery

Doctor of Philosophy

General Dental Practitioners

Master of Science

Ministry of Health

Principal investigator

World Health Organization

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Aisha Aloshaiby, Amal Gaber & Arheiam Arheiam

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Aloshaiby, A., Gaber, A. & Arheiam, A. The oral health care system in Libya: a case study. BMC Oral Health 24 , 888 (2024). https://doi.org/10.1186/s12903-024-04684-x

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Case-by-case combination of the prostate imaging reporting and data system version 2.1 with the Likert score to reduce the false-positives of prostate MRI: a proof-of-concept study

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• Valeria Peruzzi 1 ,
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• Maria De Martino 2 ,
• Lorenzo Cereser 1 ,
• Letizia Casarotto 3 ,
• Stefano Pizzolitto 3 ,
• Miriam Isola 2 ,
• Alessandro Crestani 4 ,
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To retrospectively investigate whether a case-by-case combination of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) with the Likert score improves the diagnostic performance of mpMRI for clinically significant prostate cancer (csPCa), especially by reducing false-positives.

One hundred men received mpMRI between January 2020 and April 2021, followed by prostate biopsy. Reader 1 (R1) and reader 2 (R2) (experience of > 3000 and < 200 mpMRI readings) independently reviewed mpMRIs with the PI-RADS version 2.1. After unveiling clinical information, they were free to add (or not) a Likert score to upgrade or downgrade or reinforce the level of suspicion of the PI-RADS category attributed to the index lesion or, rather, identify a new index lesion. We calculated sensitivity, specificity, and predictive values of R1/R2 in detecting csPCa when biopsying PI-RADS ≥ 3 index-lesions (strategy 1) versus PI-RADS ≥ 3 or Likert ≥ 3 index-lesions (strategy 2), with decision curve analysis to assess the net benefit. In strategy 2, the Likert score was considered dominant in determining biopsy decisions.

csPCa prevalence was 38%. R1/R2 used combined PI-RADS and Likert categorization in 28%/18% of examinations relying mainly on clinical features such as prostate specific antigen level and digital rectal examination than imaging findings. The specificity/positive predictive values were 66.1/63.1% for R1 (95%CI 52.9–77.6/54.5–70.9) and 50.0/51.6% (95%CI 37.0-63.0/35.5-72.4%) for R2 in the case of PI-RADS-based readings, and 74.2/69.2% for R1 (95%CI 61.5–84.5/59.4–77.5%) and 56.6/54.2% (95%CI 43.3-69.0/37.1-76.6%) for R2 in the case of combined PI-RADS/Likert readings. Sensitivity/negative predictive values were unaffected. Strategy 2 achieved greater net benefit as a trigger of biopsy for R1 only.

Case-by-case combination of the PI-RADS version 2.1 with Likert score translated into a mild but measurable impact in reducing the false-positives of PI-RADS categorization, though greater net benefit in reducing unnecessary biopsies was found in the experienced reader only.

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Introduction

Since its introduction in 2012 [ 1 ] and revision as version 2 in 2015 [ 2 ], the Prostate imaging reporting and data system (PI-RADS) has become the most widely accepted standard for interpreting multiparametric magnetic resonance imaging (mpMRI) of the prostate. Current version 2.1 [ 3 ], released in 2019, has been validated by several studies [ 4 , 5 , 6 , 7 , 8 ] and, according to a recent metanalysis, shows pooled positive predictive value (PPV) for clinically significant prostate cancer (csPCa) of 16, 59 and 85% for PI-RADS 3, 4 and 5 category, respectively [ 9 ]. Though the PI-RADS promotes a standardized lesion-based scoring approach, interpretation remains subjective in several instances, thus explaining its moderate inter-reader agreement only with version 2 and 2.1 [ 10 , 11 ]. Current limitations of version 2.1 also include the need to clarify some interpretation criteria, lack of definite criteria for scoring the central zone, lack of assessment of the prostate background potentially affecting cancer detection [ 12 ] and, importantly, still limited specificity translating in too many unnecessary biopsies [ 7 ].

Not surprisingly, the PI-RADS is not of universal use in the setting of initial diagnosis of csPCa. While the joint societies' European guidelines endorse it with a "strong" strength rating [ 13 ], other recommendations favor the Likert score as the preferred alternative for reporting prostate MRI [ 14 , 15 ]. Comparably to the PI-RADS, the Likert score expresses the risk that a mpMRI observation is a csPCa on an ascending 1–5 scale, though this system works as a gestalt subjective assessment not relying on a dominant sequence or specific criteria to define each risk category [ 16 ]. This allows for much flexibility when interpreting findings that are difficult-to-categorize with the PI-RADS, and the possibility to take clinical information into account, e.g., age, prostate-specific antigen (PSA) level, PSA density (PSAD), family history, and so on [ 16 , 17 ].

A few studies comparing both systems on an intra-patient basis found that the Likert score has the potential for greater diagnostic accuracy [ 18 ] and improved specificity compared to PI-RADS version 2 [ 19 ]. This suggests the potential for maximizing cancer detection while avoiding unnecessary biopsies, which still represent the Achilles's heel of prostate mpMRI [ 20 ]. On the other hand, the absence of standardized rules of image interpretation translates into its dependence on the radiologist's experience [ 16 , 18 ] and limited potential for reproducibility across different institutions and practice settings compared to the relatively objective PI-RADS [ 16 ]. A recent British audit of cancer yields after prostate MRI found PI-RADS version 2 and the Likert score clinically equivalent, with most discrepancies confined to the PI-RADS 4 category [ 21 ]. Given the difficulty in establishing the superiority of one system over the other, we hypothesized that a two-step combined use of both systems could maximize the related advantages while minimizing disadvantages, thus potentially improving the diagnostic performance, especially in terms of reducing false-positive cases. We assumed that while the PI-RADS version 2.1 can represent the basis for reporting (first step), the radiologist could refine lesion categorization with the Likert score in all those selected cases in which the PI-RADS is perceived as not fully catching the complexity of risk assessment (second step).

This study aimed to assess whether the above-mentioned case-by-case strategy of combining the PI-RADS version 2.1 categories with the Likert score reduces the number of false-positive cases for csPCa and the appropriateness of mpMRI-informed biopsy decisions.

Material and methods

Study population and standard of reference.

The Institutional Review Board approved this monocentric study. The acquisition of informed consent was waived because of the retrospective design.

We searched the institutional database for all consecutive ≥ 18-year-old men who underwent prostate mpMRI followed by prostate biopsy between January 2020 and April 2021. Indications to mpMRI were clinical suspicion of csPCa (PSA value ≥ 3 ng/ml and/or positive digital rectal examination) in biopsy-naïve men or persistent clinical suspicion of csPCa despite one or more prior negative prostate biopsies. We identified144 eligible subjects who received prostate through the transperineal route by one of three urologists using software-assisted mpMRI-ultrasound guidance (Applio 300, Toshiba/Canon). The biopsy included 4 target cores (2 in-target and 2 peri-target) on PI-RADS ≥ 3 lesions, followed by 12 systematic cores. Per internal policy, patients with PI-RADS 1–2 examinations and high clinical risk received only systematic biopsy. After excluding 10 men because of the exclusion criteria shown in Fig.  1 , we used freely available software ( https://www.randomizer.org/ ) to randomly select 100 over the remaining 134 men as the final study population (Fig.  1 ). This number of patients was defined in advance when planning the study as a balance between the available time for performing the readings and the study duration. All included men were Caucasian.

Study flowchart. BCG = bacillus Calmette-Guérin; mpMRI = multiparametric magnetic resonance imaging; TURP = transurethral resection of the prostate

The standard of reference was represented by ISUP-compliant histological examination performed on biopsy cores [ 22 ] by one of three genitourinary pathologists (5–30 years of experience). csPCa was defined as a lesion showing the highest ISUP grading group ≥ 2 on systematic or targeted biopsy.

Imaging protocol

Examinations were acquired on a 1.5 T (MAGNETOM Aera, Siemens Healthineers) or a 3.0 T MRI equipment (Achieva, Philips Medical Systems) in 13/100 and 87/100 cases, respectively. A 32-channel surface coil was used. All patients received preliminary cleansing enema and i.m. administration of 20 mg hyoscine butylbromide (Buscopan, Boehringer Ingelheim) as an antiperistaltic agent.

Acquisition parameters are detailed in Supplementary Tables 1 and 2. On the 1.5 T magnet, the maximum b-value in the second diffusion-weighted sequences was interpolated up to 1400 s/mm 2 . The apparent diffusion coefficient (ADC) map was built upon a monoexponential fitting of signal decay versus b-values of the first diffusion-weighted sequence (maximum b = 1000 s/mm 2 ). Dynamic contrast-enhanced imaging (DCE) was acquired intravenously after administering 0.2 mL/Kg of gadoteridol (Prohance, Bracco) at an injection rate of 3 ml/s using a remote-controlled power injector (Medrad Spectris Solaris EP). DCE series was presented as native images and subtracted ones.

Image analysis

Two readers independently analyzed images on a Picture Archiving and Communication System console (Suite Estensa, Ebit). Readers included one radiologist (R1) with an experience of > 3000 examinations (R.G.) and a non-experienced radiologist resident (R2) mentored by R1 during clinical activity (< 200 readings) (V.P.). A study coordinator (P.P.) showed them mpMRI examinations using a two-phase strategy.

In the first phase, blinded to clinical information, readers were allowed to report up to four lesions to be scored with the PI-RADS version 2.1 (“PI-RADS” from here on out) [ 3 ] and asked to clearly identify the index lesion as the one showing the highest PI-RADS category or the largest size in the case of more lesions with the same PI-RADS category. When readers found no lesions, the examination was assumed to include a PI-RADS 1 "index lesion" for analysis. In the second phase, the coordinator disclosed clinical data, including age, results from prior biopsy, if any, last PSA value, results of the digital rectal examination, prostate volume calculated in the original mpMRI report, PSAD, ongoing therapy with alpha-blockers if any, family history of csPCa, and symptoms if any. Based on those clinical features and depending on the mpMRI appearance, readers were then allowed to make case-by-case additional use of the Likert score according to the following rules: (i) combining the PI-RADS category of the lesions with a Likert score, e.g., to reinforce a level of suspicion (e.g., PI-RADS 3 combined with Likert 3 score) or instead upgrading or downgrading it (e.g., PI-RADS 3 combined with Likert 2 or PI-RADS 3 combined with Likert 4); (ii) identifying and categorize a new lesion and assign it a Likert score only. Using the Likert score was not mandatory, so readers were asked to explain reasons for doing so on a case-by-case basis, detailing the number and type of clinical variables and imaging findings that triggered combined scoring. Imaging features supporting Likert scoring were those summarized by Latifoltojar et al. in Supplementary Tables 5, 6 and 7 of their paper [ 17 ], as well as the PI-RADS descriptors for T2-weighted imaging, DWI and DCE [ 3 ]. Differently from the PI-RADS, we did not establish in advance which imaging or clinical feature should have been selected or privileged for image analysis, nor defined exact combinatory rules to achieve a certain Likert score. Readers were also free to integrate clinical information with no predefinite rules, except establishing that the PSAD value to be considered “suspicious” was 0.15 ng/mL/mL (not a standalone criterion for malignancy). Our strategy aimed at: (a) reflecting the subjective nature of the Likert system and facilitate the comparison with previous works on the same topic; (b) to prevent the risk of testing a set of combinatory rules rather than the properly said Likert score; (c) to prevent the risk that a set of definite combinatory rules could overinflate the performance of the less experienced reader.

The Likert score was assumed to express the risk that a mpMRI finding was a csPCa as follows: 1 = highly unlikely; 2 = unlikely; 3 = equivocal; 4 = likely; 5 = highly likely [ 21 ].

Statistical analysis

After observing non-normal data distribution with the Shapiro–Wilk test, we used the median and the interquartile range (IQR) to report continuous variables. Relevant proportions were coupled with 95% confidence intervals (95% CI). Descriptive statistics was also used to report how many lesions were found by R1 and R2 and how they were categorized with the PI-RADS and Likert scores.

Concerning PI-RADS categorization, we decided not to run an inter-reader agreement analysis because readers could detect different lesions. We then calculated the per-category rate of concordant categorizations, i.e., how many times R1 and R2 assessed the same index lesion as PI-RADS 1–2, PI-RADS 3, or PI-RADS 4–5 over the total number of index lesions scored with the same PI-RADS category.

Based on the rules of comparison between mpMRI results and prostate biopsy shown in Table  1 , we calculated the per-index lesion sensitivity, specificity, PPV and negative predictive value (NPV) for csPCa of two different biopsy strategies, as follows: (i) strategy 1 (PI-RADS categorization only), i.e., biopsying any index lesion categorized PI-RADS ≥ 3; (ii) strategy 2 (PI-RADS categorization combined with the Likert score), i.e., biopsying any index lesion categorized as PI-RADS ≥ 3 (in cases receiving PI-RADS categorization only) or Likert ≥ 3 (in cases receiving combined scoring). In strategy 2, the Likert categorization, when attributed, was considered dominant compared to the PI-RADS. E.g., a PI-RADS 2 lesion upgraded to Likert 4 was assumed to be biopsied, while a PI-RADS 3 lesion downgraded to Likert 2 was assumed to avoid biopsy. For analysis, newly identified lesions in reading phase 2 showing a Likert score greater than the PI-RADS category of the index lesion established in reading phase 1 were assumed to represent the index lesion for biopsy strategy 2.

The clinical impact of both biopsy strategies was assessed with the decision curve analysis [ 23 ], assuming that the reference "treat all" and "treat none" strategies meant to biopsy all men and biopsy none, respectively. Net benefit, i.e., the balance between the advantage of diagnosing true positives weighted for the harm of biopsying false positives, was calculated at disease threshold probabilities of 10, 15, 20, 25, and 30%, respectively.

Calculations were performed using commercially available software (MedCalc software bv, version 18.11.16), except for decision analysis, which was run on Stata using source codes freely available at https://www.mskcc.org/departments/epidemiology-biostatistics/biostatistics/decision-curve-analysis .

Study population

The median age of the men included was 66.0 years (IQR 61.0–72.0). The median serum PSA and PSAD were 6.44 ng/mL (IQR 4.85–8.94) and 0.11 ng/mL/mL (IQR 0.07–0.17), respectively. Seventy-nine/100 men were biopsy-naïve, while the remaining 21/100 showed previous negative biopsy. csPCa was found in 38/100 men (38%; 95% CI 29.59–46.41). Lesions included 17/38 ISUP 2 cancers (44.73%), 12/38 ISUP 3 cancers (31.57%), 7/38 ISUP 4 cancers (18.42%), and 2/38 ISUP 5 cancers (5.26%). Clinically insignificant cancer (ISUP 1) was found in 12/100 men (12%).

PI-RADS categorization

R1 and R2 reported 119 and 131 mpMRI findings on one hundred men, respectively. Table 2 summarizes the distribution of their PI-RADS categories. Index lesions were found in the peripheral zone and transition zone in 55/100 and 20/100 cases by R1 and 68/100 and 18/100 cases by R2, respectively. The remaining 25/100 cases (R1) and 14/100 cases (R2) were PI-RADS 1 "index lesions" not corresponding to definite mpMRI observations.

Readers identified the same index lesion in 66/100 cases (66%; 95% CI 50.40–69.60), providing the same PI-RADS categorization in 55/66 cases (83.3%; 95% CI 74.34–92.32). In particular, the rate of concordant categorizations was 15/55 (27.3%; 95% CI 15.50–39.04) for PI-RADS 1–2 assignments, 1/55 (1.8%; 95% CI 00.05–05.35) for PI-RADS 3 assignments, and 39/55 (70.9%; 95% CI 58.91–82.91) for PI-RADS 4–5 assignments. The eleven cases of discordant categorizations are detailed in Supplementary Table 3.

Combined PI-RADS-Likert score categorization

R1 and R2 provided combined PI-RADS-Likert categorization of the index lesion in 28/100 (28%; 95% CI 19.20–36.80) and 18/100 (18%; 95% CI 10.47–25.53) cases, respectively, as summarized in Fig.  2 and in Supplementary Table 4. The latter shows that, for both readers, the use of the Likert score was mostly supported by PSAD values and the results of DRE.

Distribution of cases in which reader 1 ( a ) and reader 2 ( b ) used the Likert score to complement the PI-RADS categorization of index lesions. FN = false-negative; FP = false-positive; PI-RADS = Prostate Imaging Reporting and data System version 2.1; TN = true negative; TP = true-positive

R1 assigned a Likert ≤ 2 score to PI-RADS ≤ 2 findings in 10/28 cases (35.7%) and a Likert ≥ 3 score to PI-RADS ≥ 3 findings in 9/28 cases (32.1%), suggesting that Likert scoring was used to reinforce the lesion risk in around two-thirds of cases (Fig.  3 ). The same trend was observed for R2, who assigned a Likert ≥ 3 score to PI-RADS ≥ 3 findings in 11/18 cases (61.1%). Most reinforcements of suspicious cases regarded PI-RADS 4 assignments (7/9 for R1 and 9/11 for R2). R1 observed no PI-RADS 5 or Likert 5 cases, while R2 upgraded 3 PI-RADS 4 lesions to Likert 5.

Case of Likert scoring by reader 1 reinforcing the level of suspicion of PI-RADS categorization in a 53-year-old man with a prostate-specific antigen level density of 0.08 ng/mL/mL and negative digital rectal examination. The index lesion in the right anterior peripheral zone of the midgland showed wedge-shaped mild hypointensity on the apparent diffusion coefficient map (arrow in a ) and wedge-shaped mild hyperintensity on b = 2000s/mm 2 image ( b ), slight hypointensity on T2-weighted imaging (arrow in c ) and early focal enhancement after contrast administration (arrow in d ). The lesion was assessed as PI-RADS 2 and Likert 2. Transperineal systematic biopsy cores in the same quadrant and adjacent quadrant showed gland atrophy/subatrophy and chronic prostatitis

The secondary main trend consisted in assigning a Likert ≤ 2 score to PI-RADS ≥ 3 findings, i.e., 7/28 (25.0%) cases by R1 and 6/18 (33.3%) cases by R2, respectively. Reclassification beneath the threshold for biopsy translated into a switch from false-positives to true-negatives in all cases (Fig.  4 ). In a minority of cases, R1 and R2 assigned a Likert ≥ 3 score to PI-RADS ≤ 2 lesions (2/28 and 1/18 cases, respectively), all of which were found to be false-positives at systematic biopsy. Reclassifications are shown in Fig.  2 . As an overall balance between the false-positive cases saved or induced by the use of the Likert score, strategy 2 could have avoided 5 and 4 unnecessary biopsies for R1 and R2, respectively.

Case of Likert-induced downgranding of lesion suspicion by reader 1 in a 62-year-old man. A mildly-hypointense atypical nodule in the left anterior transition zone of the midgland (arrow in a and b ) showed restricted diffusion with marked hyperintensity on b = 2000s/mm 2 image ( c ) and marked hypointensity on the apparent diffusion coefficient map ( d ), and was categorized as a PI-RADS 2 upgraded to 3. Based on prostate-specific antigen level density of 0.07 ng/mL/mL and negative digital rectal examination, reader 1 downgraded the level of suspicion to Likert 2. A targeted prostate biopsy showed chronic prostatitis

Diagnostic performance

The diagnostic performance of biopsy strategies 1 and 2 is shown in Table  3 . For both readers, strategy 2 translated into increased specificity and PPV while maintaining comparable sensitivity and NPV.

Concerning the clinical impact for R1, decision curve analysis (Fig.  5 ) showed greater net benefit of strategy 2 compared to strategy 1 over the whole range of disease probability, with net benefit values at 10, 15, 20, 25 and 30% of csPCa likelihood of 0.34 versus 0.33, 0.33 versus 0.32, 0.32 versus 0.30, 0.30 versus 0.29 and 0.29 versus 0.27, respectively. In the case of R2, the curves of strategy 2 and strategy 1 largely overlapped up to around 25% threshold probability, with comparable net benefit values at 10% (0.29), 15% (0.27), and 20% of csPCa likelihood, and greater net benefit values at 25% (0.23 versus 0.22) and 30% (0.20 vs. 0.19) disease probability.

Decision curve analysis for reader 1 ( a ) and reader 2 ( b ) (see the main text for details)

In this study, we observed that, when combining the PI-RADS version 2.1 categorization of prostate index lesions with a case-by-case use of the Likert score, R1 and R2 downgraded the risk of csPCa beneath the threshold actioning prostate biopsy in 25 and 33.3% of the reclassified cases, respectively. This translated into increased mpMRI specificity and PPV in diagnosing ISUP ≥ 2 prostate cancer, with no detrimental effect on sensitivity and NPV. While this trend was observed in both readers, the net benefit on decision curve analysis improved for R1 only, supporting previous observations that adequate reader experience is the prerequisite for using the Likert scale [ 24 , 25 ]. This is related to the fact that most experienced readers are able to account for additional clinical and imaging factors when interpreting mpMRI and, in turn, make image interpretation more flexible and nuanced. Of note, greater net benefit was observed across the whole range of csPCa probability in our population (79% biopsy-naïve men and 21% re-biopsy patients).

As far as we know, previous studies did not investigate a similar strategy but rather compared the PI-RADS (version 1 or 2) versus the Likert score as alternative systems for diagnosis [ 18 , 19 , 26 , 27 , 28 , 29 ]. In line with our results, one of those works by Zawaideh et al. on 199 men [ 19 ] found that, being equal the sensitivity (94%) and NPV (96%), the use of the Likert score translated into lower positive call rate, and in turn greater per-lesion specificity and PPV for ISUP ≥ 2 cancers than the PI-RADS version 2 (77 versus 66% and 66 versus 58%, respectively). Differently from Khoo et al. [ 18 ], we did not observe an increase in cancer detection rate since sensitivity remained stable for R1 (94.7%) or minimally dropped for R2 (from 86.8 to 84.2%). This is in line with the fact that the Likert score upgraded the PI-RADS risk in a very minority of cases, and most times inappropriately, e.g., 2/2 cases upgraded from PI-RADS ≤ 2 to Likert 3 and 1/2 cases upgraded from PI-RADS 3 to Likert ≥ 4 were false-positive for the most experienced reader. Our results suggest that the Likert-induced upgrading is expectedly rare and should be regarded with caution as a trigger for biopsy, though further studies should confirm this issue and assess how to overcome it.

Our findings are more directly comparable with those by Stevens et al. [ 30 ], who performed logistic regression to identify Likert findings predicting csPCa and, in turn, built a model to automatically adjust indeterminate PI-RADS 3 cases (version 2) with the Likert score. In the testing cohort, the adjustment translated into an increase in specificity from 30.3 to 74.9%, comparable to the one we observed for our most experienced reader when using strategy 2 (74.2%). However, we did not focus analysis on indeterminate cases only and showed lower PI-RADS 3 call rates (10.1% for R1 and 16.8% for R2) than those Authors (135/411 men, i.e., 32.8% in the building cohort, and 159/380 men, i.e., 41.8% in the testing cohort). One can assume that, even though the Authors' model determined a comparable increase in specificity and PPV in a validation study [ 26 ], our results are at lower risk of overestimation in favor of Likert-induced effects over the entire spectrum of PI-RADS categories.

A strength of our study is that we blinded readers to clinical information when reporting with the PI-RADS, thus eliminating those confounders that could have translated into using "modified PI-RADS categories" close to Likert ones in clinical practice and previous trials [ 26 ]. Using this strategy translated into accurate lesion risk reduction, and, in turn, false-positives, e.g., as occurred for R1 in seven PI-RADS ≥ 3 cases reclassified as Likert ≤ 2 which were found to be inflammation on prostate biopsy (Suppl. Table 4). Though in a different setting, our results compare to those by Zawaideh et al. [ 19 ], who observed significantly more Likert negative/PI-RADS positive than Likert positive/PI-RADS negative cases. Notably, both readers used the Likert score mainly to reinforce the level of suspicion already expressed by the PI-RADS category, in line with the fact that this system expands image analysis and risk stratification from the lesion-based level of the PI-RADS to a more comprehensive patient-level. In the case of R1, this occurred mostly to reinforce a PI-RADS ≤ 2 category as a Likert ≤ 2 score (10/28 reclassified cases), in accordance with a recent audit of cancer yield showing that negative mpMRIs are a major source of agreement between PI-RADS version 2 and Likert scoring [ 21 ]. One can hypothesize that selective reporting of both the PI-RADS and Likert score could help identifying those cases in clinical practice and research in which clinical information was determinant in shaping the above-mentioned “modified PI-RADS” categories. A more systematic assessment and quantification could be helpful to further refine PI-RADS categories and provide more nuanced risk stratification.

As this was a proof-of-concept study, we did not run a systematic analysis of how much reproducible a selective use of additional Likert scoring can be, and whether it can depend on lesion location (i.e., peripheral zone versus transition zone findings) or other factors, e.g., how much complete the available clinical information is at the time of mpMRI reporting. While the Likert score is not-standardized by definition, it was found to compare the diagnostic performance of the PI-RADS [ 21 ], effectively impact biopsy decisions in reference studies such as the MRI-FIRST [ 31 ], and potentially prompt which imaging features can be helpful in future revisions of the PI-RADS [ 21 ]. In our study, both readers relied more on clinical variables than imaging findings when using the Likert score (Supplementary Table 4), especially PSAD and DRE compared to the remaining clinical information available during reading phase 2 (age, prior biopsy, PSA, prostate volume, ongoing therapy with alpha-blockers, family history of csPCa, and symptoms). This result is in line with the role that PSAD and DRE have in shaping biopsy decisions and defining patient risk categories, respectively [ 13 ]. Our results support the concept that, while the most reproducible and impacting features supporting a selective use of the Likert score should be further elucidated, the strategy we investigated is of clinical added value in reducing the false positives in the real world. Further studies should also assess whether Likert-adjusted PI-RADS categories compare with risk calculators in assessing the pre-biopsy risk of harboring csPCa [ 32 ] or can represent additional variables to be included in clinical-imaging-based risk models, assumed that the same expert readers who provided reliable PI-RADS categorization can properly refine them as we showed.

We must acknowledge several study limitations. Given the retrospective design, we could not perform a targeted biopsy of index lesions found by Likert scoring only, so a systematic biopsy was used as a surrogate standard of reference. Second, we did not compare our approach to strategies proven to reduce the false-positive rate (e.g., adjusting the PI-RADS with PSAD [ 13 ]) or multivariable models stratifying patients’ risk by combining clinical features with mpMRI findings [ 32 ]. However, in the absence of a definite strategy on how to refine PI-RADS categories, the Likert adjustment strategy could be used in high-volume centers and multidisciplinary contexts where the urologist and other professionals can become familiar with the increased complexity of the mpMRI report, and more likely trust the Likert score as the "dominant" category for shaping biopsy decision (e.g., when a PI-RADS 4 lesion is downgraded to Likert 2). At the same time, this strategy could help less experienced readers to capitalize on the more standardized approach of the PI-RADS during the learning curve phase while having the capability, under supervision, to face more difficult cases with the flexibility inherent to the Likert score. Third, we only included biopsied patients, thus making difficult understanding how the combined PI-RADS-Likert categorization can work in low risk patients with negative mpMRI. Finally, R2 was a resident mentored by R1 in clinical practice, suggesting that her criteria for using the Likert score can largely reflect those those of R1, and, in turn, limit the generalizability of our inter-reader comparison outside the monocentric setting of this study. This could be further emphasized by the fact that the use of the PI-RADS version 2.1 translated into a diagnostic performance of R2 close to that of the experienced radiologist in our study, suggesting that the effect of combining the Likert score with a standardized system should be tested on a larger scale in less experienced readers.

In conclusion, our proof-of-concept study supports the hypothesis that combining the PI-RADS version 2.1 categories with the Likert score can improve the specificity and PPV of prostate mpMRI with no detrimental effect on sensitivity and NPV. Regardless of readers’ experience, clinical features (especially PSAD and DRE) were the most impactful ones in determining combined PI-RADS and Likert scoring. However, the reproducibility of the factors triggering the selective use of the Likert score should be tested on a larger scale. While overall mild, the improvement translated into greater net benefit in shaping biopsy decisions in the case of R1, suggesting that this strategy can be easily and effectively used by more experienced readers in clinical practice.

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Acknowledgements

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Institutional Review Board of the Department of Medicine (DMED), University of Udine ([email protected]), with the approval number RIF. Prot IRB: 75/2023. The acquisition of informed consent was waived because of the retrospective design.

Open access funding provided by Università degli Studi di Udine within the CRUI-CARE Agreement. No funds, grants, or other support was received.

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Paolo Polizzi

Present address: UOC Radiologia, Ospedale Civile SS. Giovanni e Paolo, ULSS 3 Serenissima, 6776 - 30122, Castello, Venezia, Italy

Gianluca Giannarini and Chiara Zuiani share the senior co-authorship.

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Institute of Radiology, Department of Medicine (DMED), University of Udine, University Hospital S. Maria della Misericordia – Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15 – 33100, Udine, Italy

Rossano Girometti, Valeria Peruzzi, Paolo Polizzi, Lorenzo Cereser & Chiara Zuiani

Division of Medical Statistics, Department of Medicine (DMED), University of Udine, pl.le Kolbe, 4 – 33100, Udine, Italy

Maria De Martino & Miriam Isola

Pathology Unit, University Hospital S. Maria della Misericordia – Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15 – 33100, Udine, Italy

Letizia Casarotto & Stefano Pizzolitto

Urology Unit, University Hospital S. Maria della Misericordia – Azienda Sanitaria-Universitaria Friuli Centrale (ASU FC), p.le S. Maria della Misericordia, 15 – 33100, Udine, Italy

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Rossano Girometti, Valeria Peruzzi, Paolo Polizzi, Letizia Casarotto, Miriam Isola, and Alessandro Crestani. The first draft of the manuscript was written by Rossano Girometti, Maria De Martino, Lorenzo Cereser, and Gianluca Giannarini, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Girometti, R., Peruzzi, V., Polizzi, P. et al. Case-by-case combination of the prostate imaging reporting and data system version 2.1 with the Likert score to reduce the false-positives of prostate MRI: a proof-of-concept study. Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04506-2

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Effects on the indoor environment in a stable for horses in winter: a case study, 1. introduction, 2. materials and methods, 2.1. description of the farm, 2.2. data acquisition and processing, 3.1. long-term registration measurement of the basic parameters of the indoor environment, 3.1.1. measurement results of the indoor environment in the stable, 3.1.2. analysis of the indoor environment in the stable during a working day, 3.1.3. analysis of the indoor environment in the stable during a non-working day, 3.1.4. measurement results of the indoor environment in the indoor riding arena, 3.2. measurement of airborne dust concentration, 3.3. measurement of the noise level of the indoor environment in the stable, 3.4. measurement of light conditions inside the stable, 3.4.1. results of the light condition measurements inside the stable during one-day measurement, 3.4.2. results of the daylight factor measurements, 4. discussion, 5. conclusions, author contributions, institutional review board statement, data availability statement, acknowledgments, conflicts of interest.

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Kic, P.; Wohlmuthová, M.; Starostová, L. Effects on the Indoor Environment in a Stable for Horses in Winter: A Case Study. Agriculture 2024 , 14 , 1287. https://doi.org/10.3390/agriculture14081287

Kic P, Wohlmuthová M, Starostová L. Effects on the Indoor Environment in a Stable for Horses in Winter: A Case Study. Agriculture . 2024; 14(8):1287. https://doi.org/10.3390/agriculture14081287

Kic, Pavel, Marie Wohlmuthová, and Lucie Starostová. 2024. "Effects on the Indoor Environment in a Stable for Horses in Winter: A Case Study" Agriculture 14, no. 8: 1287. https://doi.org/10.3390/agriculture14081287

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