master thesis data analysis

11 Tips For Writing a Dissertation Data Analysis

Since the evolution of the fourth industrial revolution – the Digital World; lots of data have surrounded us. There are terabytes of data around us or in data centers that need to be processed and used. The data needs to be appropriately analyzed to process it, and Dissertation data analysis forms its basis. If data analysis is valid and free from errors, the research outcomes will be reliable and lead to a successful dissertation. 

So, in today’s topic, we will cover the need to analyze data, dissertation data analysis, and mainly the tips for writing an outstanding data analysis dissertation. If you are a doctoral student and plan to perform dissertation data analysis on your data, make sure that you give this article a thorough read for the best tips!

What is Data Analysis in Dissertation?

Even f you have the data collected and compiled in the form of facts and figures, it is not enough for proving your research outcomes. There is still a need to apply dissertation data analysis on your data; to use it in the dissertation. It provides scientific support to the thesis and conclusion of the research.

Data Analysis Tools

There are plenty of indicative tests used to analyze data and infer relevant results for the discussion part. Following are some tests  used to perform analysis of data leading to a scientific conclusion:

11 Most Useful Tips for Dissertation Data Analysis

Doctoral students need to perform dissertation data analysis and then dissertation to receive their degree. Many Ph.D. students find it hard to do dissertation data analysis because they are not trained in it.

1. Dissertation Data Analysis Services

The first tip applies to those students who can afford to look for help with their dissertation data analysis work. It’s a viable option, and it can help with time management and with building the other elements of the dissertation with much detail.

Dissertation Analysis services are professional services that help doctoral students with all the basics of their dissertation work, from planning, research and clarification, methodology, dissertation data analysis and review, literature review, and final powerpoint presentation.

One great reference for dissertation data analysis professional services is Statistics Solutions , they’ve been around for over 22 years helping students succeed in their dissertation work. You can find the link to their website here .

Following are some helpful tips for writing a splendid dissertation data analysis:

2. Relevance of Collected Data

It involves  data collection  of your related topic for research. Carefully analyze the data that tends to be suitable for your analysis. Do not just go with irrelevant data leading to complications in the results. Your data must be relevant and fit with your objectives. You must be aware of how the data is going to help in analysis. 

3. Data Analysis

For analysis, it is crucial to use such methods that fit best with the types of data collected and the research objectives. Elaborate on these methods and the ones that justify your data collection methods thoroughly. Make sure to make the reader believe that you did not choose your method randomly. Instead, you arrived at it after critical analysis and prolonged research.

Data analysis involves two approaches –  Qualitative Data Analysis and Quantitative Data Analysis.   Qualitative data analysis  comprises research through experiments, focus groups, and interviews. This approach helps to achieve the objectives by identifying and analyzing common patterns obtained from responses. 

The overall objective of data analysis is to detect patterns and inclinations in data and then present the outcomes implicitly.  It helps in providing a solid foundation for critical conclusions and assisting the researcher to complete the dissertation proposal. 

4. Qualitative Data Analysis

Qualitative data refers to data that does not involve numbers. You are required to carry out an analysis of the data collected through experiments, focus groups, and interviews. This can be a time-taking process because it requires iterative examination and sometimes demanding the application of hermeneutics. Note that using qualitative technique doesn’t only mean generating good outcomes but to unveil more profound knowledge that can be transferrable.

Presenting qualitative data analysis in a dissertation  can also be a challenging task. It contains longer and more detailed responses. Placing such comprehensive data coherently in one chapter of the dissertation can be difficult due to two reasons. Firstly, we cannot figure out clearly which data to include and which one to exclude. Secondly, unlike quantitative data, it becomes problematic to present data in figures and tables. Making information condensed into a visual representation is not possible. As a writer, it is of essence to address both of these challenges.

This method involves analyzing qualitative data based on an argument that a researcher already defines. It’s a comparatively easy approach to analyze data. It is suitable for the researcher with a fair idea about the responses they are likely to receive from the questionnaires.

In this method, the researcher analyzes the data not based on any predefined rules. It is a time-taking process used by students who have very little knowledge of the research phenomenon.

5. Quantitative Data Analysis

The Presentation of quantitative data  depends on the domain to which it is being presented. It is beneficial to consider your audience while writing your findings. Quantitative data for  hard sciences  might require numeric inputs and statistics. As for  natural sciences , such comprehensive analysis is not required.

Following are some of the methods used to perform quantitative data analysis. 

6. Data Presentation Tools

Since large volumes of data need to be represented, it becomes a difficult task to present such an amount of data in coherent ways. To resolve this issue, consider all the available choices you have, such as tables, charts, diagrams, and graphs. 

7. Include Appendix or Addendum

After presenting a large amount of data, your dissertation analysis part might get messy and look disorganized. Also, you would not be cutting down or excluding the data you spent days and months collecting. To avoid this, you should include an appendix part. 

The data you find hard to arrange within the text, include that in the  appendix part of a dissertation . And place questionnaires, copies of focus groups and interviews, and data sheets in the appendix. On the other hand, one must put the statistical analysis and sayings quoted by interviewees within the dissertation. 

8. Thoroughness of Data

Thoroughly demonstrate the ideas and critically analyze each perspective taking care of the points where errors can occur. Always make sure to discuss the anomalies and strengths of your data to add credibility to your research.

9. Discussing Data

Discussion of data involves elaborating the dimensions to classify patterns, themes, and trends in presented data. In addition, to balancing, also take theoretical interpretations into account. Discuss the reliability of your data by assessing their effect and significance. Do not hide the anomalies. While using interviews to discuss the data, make sure you use relevant quotes to develop a strong rationale. 

10. Findings and Results

Findings refer to the facts derived after the analysis of collected data. These outcomes should be stated; clearly, their statements should tightly support your objective and provide logical reasoning and scientific backing to your point. This part comprises of majority part of the dissertation. 

11. Connection with Literature Review

The role of data analytics at the senior management level.

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Any form of the systematic decision-making process is better enhanced with data. But making sense of big data or even small data analysis when venturing into a decision-making process might

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Wrapping up.

Writing data analysis in the dissertation involves dedication, and its implementations demand sound knowledge and proper planning. Choosing your topic, gathering relevant data, analyzing it, presenting your data and findings correctly, discussing the results, connecting with the literature and conclusions are milestones in it. Among these checkpoints, the Data analysis stage is most important and requires a lot of keenness.

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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How do I make a data analysis for my bachelor, master or PhD thesis?

A data analysis is an evaluation of formal data to gain knowledge for the bachelor’s, master’s or doctoral thesis. The aim is to identify patterns in the data, i.e. regularities, irregularities or at least anomalies.

Data can come in many forms, from numbers to the extensive descriptions of objects. As a rule, this data is always in numerical form such as time series or numerical sequences or statistics of all kinds. However, statistics are already processed data.

Data analysis requires some creativity because the solution is usually not obvious. After all, no one has conducted an analysis like this before, or at least you haven't found anything about it in the literature.

The results of a data analysis are answers to initial questions and detailed questions. The answers are numbers and graphics and the interpretation of these numbers and graphics.

What are the advantages of data analysis compared to other methods?

• Numbers are universal
• The data is tangible.
• There are algorithms for calculations and it is easier than a text evaluation.
• The addressees quickly understand the results.
• You can really do magic and impress the addressees.
• It’s easier to visualize the results.

What are the disadvantages of data analysis?

• Garbage in, garbage out. If the quality of the data is poor, it’s impossible to obtain reliable results.
• The dependency in data retrieval can be quite annoying. Here are some tips for attracting participants for a survey.
• You have to know or learn methods or find someone who can help you.
• Mistakes can be devastating.
• Missing substance can be detected quickly.
• Pictures say more than a thousand words. Therefore, if you can’t fill the pages with words, at least throw in graphics. However, usually only the words count.

Under what conditions can or should I conduct a data analysis?

• If I have to.
• You must be able to get the right data.
• If I can perform the calculations myself or at least understand, explain and repeat the calculated evaluations of others.
• You want a clear personal contribution right from the start.

How do I create the evaluation design for the data analysis?

The most important thing is to ask the right questions, enough questions and also clearly formulated questions. Here are some techniques for asking the right questions:

Good formulation: What is the relationship between Alpha and Beta?

Poor formulation: How are Alpha and Beta related?

Now it’s time for the methods for the calculation. There are dozens of statistical methods, but as always, most calculations can be done with only a handful of statistical methods.

• Which detailed questions can be formulated as the research question?
• What data is available? In what format? How is the data prepared?
• Which key figures allow statements?
• What methods are available to calculate such indicators? Do my details match? By type (scales), by size (number of records).
• Do I not need to have a lot of data for a data analysis?

It depends on the media, the questions and the methods I want to use.

A fixed rule is that I need at least 30 data sets for a statistical analysis in order to be able to make representative statements about the population. So statistically it doesn't matter if I have 30 or 30 million records. That's why statistics were invented...

What mistakes do I need to watch out for?

• Don't do the analysis at the last minute.
• Formulate questions and hypotheses for evaluation BEFORE data collection!
• Stay persistent, keep going.
• Leave the results for a while then revise them.
• You have to combine theory and the state of research with your results.
• You must have the time under control

Which tools can I use?

You can use programs of all kinds for calculations. But asking questions is your most powerful aide.

Who can legally help me with a data analysis?

The great intellectual challenge is to develop the research design, to obtain the data and to interpret the results in the end.

Am I allowed to let others perform the calculations?

That's a thing. In the end, every program is useful. If someone else is operating a program, then they can simply be seen as an extension of the program. But this is a comfortable view... Of course, it’s better if you do your own calculations.

A good compromise is to find some help, do a practical calculation then follow the calculation steps meticulously so next time you can do the math yourself. Basically, this functions as a permitted training. One can then justify each step of the calculation in the defense.

What's the best place to start?

Clearly with the detailed questions and hypotheses. These two guide the entire data analysis. So formulate as many detailed questions as possible to answer your main question or research question. You can find detailed instructions and examples for the formulation of these so-called detailed questions in the Thesis Guide.

How does the Aristolo Guide help with data evaluation for the bachelor’s or master’s thesis or dissertation?

The Thesis Guide or Dissertation Guide has instructions for data collection, data preparation, data analysis and interpretation. The guide can also teach you how to formulate questions and answer them with data to create your own experiment. We also have many templates for questionnaires and analyses of all kinds. Good luck writing your text! Silvio and the Aristolo Team PS: Check out the Thesis-ABC and the Thesis Guide for writing a bachelor or master thesis in 31 days.

Raw Data to Excellence: Master Dissertation Analysis

Discover the secrets of successful dissertation data analysis. Get practical advice and useful insights from experienced experts now!

Have you ever found yourself knee-deep in a dissertation , desperately seeking answers from the data you’ve collected? Or have you ever felt clueless with all the data that you’ve collected but don’t know where to start? Fear not, in this article we are going to discuss a method that helps you come out of this situation and that is Dissertation Data Analysis.

Dissertation data analysis is like uncovering hidden treasures within your research findings. It’s where you roll up your sleeves and explore the data you’ve collected, searching for patterns, connections, and those “a-ha!” moments. Whether you’re crunching numbers, dissecting narratives, or diving into qualitative interviews, data analysis is the key that unlocks the potential of your research.

Dissertation Data Analysis

Dissertation data analysis plays a crucial role in conducting rigorous research and drawing meaningful conclusions. It involves the systematic examination, interpretation, and organization of data collected during the research process. The aim is to identify patterns, trends, and relationships that can provide valuable insights into the research topic.

The first step in dissertation data analysis is to carefully prepare and clean the collected data. This may involve removing any irrelevant or incomplete information, addressing missing data, and ensuring data integrity. Once the data is ready, various statistical and analytical techniques can be applied to extract meaningful information.

Descriptive statistics are commonly used to summarize and describe the main characteristics of the data, such as measures of central tendency (e.g., mean, median) and measures of dispersion (e.g., standard deviation, range). These statistics help researchers gain an initial understanding of the data and identify any outliers or anomalies.

Furthermore, qualitative data analysis techniques can be employed when dealing with non-numerical data, such as textual data or interviews. This involves systematically organizing, coding, and categorizing qualitative data to identify themes and patterns.

Types of Research

When considering research types in the context of dissertation data analysis, several approaches can be employed:

1. Quantitative Research

This type of research involves the collection and analysis of numerical data. It focuses on generating statistical information and making objective interpretations. Quantitative research often utilizes surveys, experiments, or structured observations to gather data that can be quantified and analyzed using statistical techniques.

2. Qualitative Research

In contrast to quantitative research, qualitative research focuses on exploring and understanding complex phenomena in depth. It involves collecting non-numerical data such as interviews, observations, or textual materials. Qualitative data analysis involves identifying themes, patterns, and interpretations, often using techniques like content analysis or thematic analysis.

3. Mixed-Methods Research

This approach combines both quantitative and qualitative research methods. Researchers employing mixed-methods research collect and analyze both numerical and non-numerical data to gain a comprehensive understanding of the research topic. The integration of quantitative and qualitative data can provide a more nuanced and comprehensive analysis, allowing for triangulation and validation of findings.

Primary vs. Secondary Research

Primary research.

Primary research involves the collection of original data specifically for the purpose of the dissertation. This data is directly obtained from the source, often through surveys, interviews, experiments, or observations. Researchers design and implement their data collection methods to gather information that is relevant to their research questions and objectives. Data analysis in primary research typically involves processing and analyzing the raw data collected.

Secondary Research

Secondary research involves the analysis of existing data that has been previously collected by other researchers or organizations. This data can be obtained from various sources such as academic journals, books, reports, government databases, or online repositories. Secondary data can be either quantitative or qualitative, depending on the nature of the source material. Data analysis in secondary research involves reviewing, organizing, and synthesizing the available data.

If you wanna deepen into Methodology in Research , also read: What is Methodology in Research and How Can We Write it?

Types of Analysis 

Various types of analysis techniques can be employed to examine and interpret the collected data. Of all those types, the ones that are most important and used are:

• Descriptive Analysis: Descriptive analysis focuses on summarizing and describing the main characteristics of the data. It involves calculating measures of central tendency (e.g., mean, median) and measures of dispersion (e.g., standard deviation, range). Descriptive analysis provides an overview of the data, allowing researchers to understand its distribution, variability, and general patterns.
• Inferential Analysis: Inferential analysis aims to draw conclusions or make inferences about a larger population based on the collected sample data. This type of analysis involves applying statistical techniques, such as hypothesis testing, confidence intervals, and regression analysis, to analyze the data and assess the significance of the findings. Inferential analysis helps researchers make generalizations and draw meaningful conclusions beyond the specific sample under investigation.
• Qualitative Analysis: Qualitative analysis is used to interpret non-numerical data, such as interviews, focus groups, or textual materials. It involves coding, categorizing, and analyzing the data to identify themes, patterns, and relationships. Techniques like content analysis, thematic analysis, or discourse analysis are commonly employed to derive meaningful insights from qualitative data.
• Correlation Analysis: Correlation analysis is used to examine the relationship between two or more variables . It determines the strength and direction of the association between variables. Common correlation techniques include Pearson’s correlation coefficient, Spearman’s rank correlation, or point-biserial correlation, depending on the nature of the variables being analyzed.

Basic Statistical Analysis

When conducting dissertation data analysis, researchers often utilize basic statistical analysis techniques to gain insights and draw conclusions from their data. These techniques involve the application of statistical measures to summarize and examine the data. Here are some common types of basic statistical analysis used in dissertation research:

• Descriptive Statistics
• Frequency Analysis
• Cross-tabulation
• Chi-Square Test
• Correlation Analysis

Advanced Statistical Analysis

In dissertation data analysis, researchers may employ advanced statistical analysis techniques to gain deeper insights and address complex research questions. These techniques go beyond basic statistical measures and involve more sophisticated methods. Here are some examples of advanced statistical analysis commonly used in dissertation research:

Regression Analysis

• Analysis of Variance (ANOVA)
• Factor Analysis
• Cluster Analysis
• Structural Equation Modeling (SEM)
• Time Series Analysis

Examples of Methods of Analysis

Regression analysis is a powerful tool for examining relationships between variables and making predictions. It allows researchers to assess the impact of one or more independent variables on a dependent variable. Different types of regression analysis, such as linear regression, logistic regression, or multiple regression, can be used based on the nature of the variables and research objectives.

Event Study

An event study is a statistical technique that aims to assess the impact of a specific event or intervention on a particular variable of interest. This method is commonly employed in finance, economics, or management to analyze the effects of events such as policy changes, corporate announcements, or market shocks.

Vector Autoregression

Vector Autoregression is a statistical modeling technique used to analyze the dynamic relationships and interactions among multiple time series variables. It is commonly employed in fields such as economics, finance, and social sciences to understand the interdependencies between variables over time.

Preparing Data for Analysis

1. become acquainted with the data.

It is crucial to become acquainted with the data to gain a comprehensive understanding of its characteristics, limitations , and potential insights. This step involves thoroughly exploring and familiarizing oneself with the dataset before conducting any formal analysis by reviewing the dataset to understand its structure and content. Identify the variables included, their definitions, and the overall organization of the data. Gain an understanding of the data collection methods, sampling techniques, and any potential biases or limitations associated with the dataset.

2. Review Research Objectives

This step involves assessing the alignment between the research objectives and the data at hand to ensure that the analysis can effectively address the research questions. Evaluate how well the research objectives and questions align with the variables and data collected. Determine if the available data provides the necessary information to answer the research questions adequately. Identify any gaps or limitations in the data that may hinder the achievement of the research objectives.

3. Creating a Data Structure

This step involves organizing the data into a well-defined structure that aligns with the research objectives and analysis techniques. Organize the data in a tabular format where each row represents an individual case or observation, and each column represents a variable. Ensure that each case has complete and accurate data for all relevant variables. Use consistent units of measurement across variables to facilitate meaningful comparisons.

4. Discover Patterns and Connections

In preparing data for dissertation data analysis, one of the key objectives is to discover patterns and connections within the data. This step involves exploring the dataset to identify relationships, trends, and associations that can provide valuable insights. Visual representations can often reveal patterns that are not immediately apparent in tabular data. 

Qualitative Data Analysis

Qualitative data analysis methods are employed to analyze and interpret non-numerical or textual data. These methods are particularly useful in fields such as social sciences, humanities, and qualitative research studies where the focus is on understanding meaning, context, and subjective experiences. Here are some common qualitative data analysis methods:

Thematic Analysis

The thematic analysis involves identifying and analyzing recurring themes, patterns, or concepts within the qualitative data. Researchers immerse themselves in the data, categorize information into meaningful themes, and explore the relationships between them. This method helps in capturing the underlying meanings and interpretations within the data.

Content Analysis

Content analysis involves systematically coding and categorizing qualitative data based on predefined categories or emerging themes. Researchers examine the content of the data, identify relevant codes, and analyze their frequency or distribution. This method allows for a quantitative summary of qualitative data and helps in identifying patterns or trends across different sources.

Grounded Theory

Grounded theory is an inductive approach to qualitative data analysis that aims to generate theories or concepts from the data itself. Researchers iteratively analyze the data, identify concepts, and develop theoretical explanations based on emerging patterns or relationships. This method focuses on building theory from the ground up and is particularly useful when exploring new or understudied phenomena.

Discourse Analysis

Discourse analysis examines how language and communication shape social interactions, power dynamics, and meaning construction. Researchers analyze the structure, content, and context of language in qualitative data to uncover underlying ideologies, social representations, or discursive practices. This method helps in understanding how individuals or groups make sense of the world through language.

Narrative Analysis

Narrative analysis focuses on the study of stories, personal narratives, or accounts shared by individuals. Researchers analyze the structure, content, and themes within the narratives to identify recurring patterns, plot arcs, or narrative devices. This method provides insights into individuals’ live experiences, identity construction, or sense-making processes.

Applying Data Analysis to Your Dissertation

Applying data analysis to your dissertation is a critical step in deriving meaningful insights and drawing valid conclusions from your research. It involves employing appropriate data analysis techniques to explore, interpret, and present your findings. Here are some key considerations when applying data analysis to your dissertation:

Selecting Analysis Techniques

Choose analysis techniques that align with your research questions, objectives, and the nature of your data. Whether quantitative or qualitative, identify the most suitable statistical tests, modeling approaches, or qualitative analysis methods that can effectively address your research goals. Consider factors such as data type, sample size, measurement scales, and the assumptions associated with the chosen techniques.

Data Preparation

Ensure that your data is properly prepared for analysis. Cleanse and validate your dataset, addressing any missing values, outliers, or data inconsistencies. Code variables, transform data if necessary, and format it appropriately to facilitate accurate and efficient analysis. Pay attention to ethical considerations, data privacy, and confidentiality throughout the data preparation process.

Execution of Analysis

Execute the selected analysis techniques systematically and accurately. Utilize statistical software, programming languages, or qualitative analysis tools to carry out the required computations, calculations, or interpretations. Adhere to established guidelines, protocols, or best practices specific to your chosen analysis techniques to ensure reliability and validity.

Interpretation of Results

Thoroughly interpret the results derived from your analysis. Examine statistical outputs, visual representations, or qualitative findings to understand the implications and significance of the results. Relate the outcomes back to your research questions, objectives, and existing literature. Identify key patterns, relationships, or trends that support or challenge your hypotheses.

Drawing Conclusions

Based on your analysis and interpretation, draw well-supported conclusions that directly address your research objectives. Present the key findings in a clear, concise, and logical manner, emphasizing their relevance and contributions to the research field. Discuss any limitations, potential biases, or alternative explanations that may impact the validity of your conclusions.

Validation and Reliability

Evaluate the validity and reliability of your data analysis by considering the rigor of your methods, the consistency of results, and the triangulation of multiple data sources or perspectives if applicable. Engage in critical self-reflection and seek feedback from peers, mentors, or experts to ensure the robustness of your data analysis and conclusions.

In conclusion, dissertation data analysis is an essential component of the research process, allowing researchers to extract meaningful insights and draw valid conclusions from their data. By employing a range of analysis techniques, researchers can explore relationships, identify patterns, and uncover valuable information to address their research objectives.

Turn Your Data Into Easy-To-Understand And Dynamic Stories

Decoding data is daunting and you might end up in confusion. Here’s where infographics come into the picture. With visuals, you can turn your data into easy-to-understand and dynamic stories that your audience can relate to. Mind the Graph is one such platform that helps scientists to explore a library of visuals and use them to amplify their research work. Sign up now to make your presentation simpler. 

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About Sowjanya Pedada

Sowjanya is a passionate writer and an avid reader. She holds MBA in Agribusiness Management and now is working as a content writer. She loves to play with words and hopes to make a difference in the world through her writings. Apart from writing, she is interested in reading fiction novels and doing craftwork. She also loves to travel and explore different cuisines and spend time with her family and friends.

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• GETTING STARTED
• Introduction
• FUNDAMENTALS

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Data analysis techniques

In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.

The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.

We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).

At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:

REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process

This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.

REASON B It takes time to get your head around data analysis

When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.

Final thoughts...

Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .

Thesis/Capstone for Master's in Data Science | Northwestern SPS - Northwestern School of Professional Studies

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Capstone and thesis overview.

Capstone and thesis are similar in that they both represent a culminating, scholarly effort of high quality. Both should clearly state a problem or issue to be addressed. Both will allow students to complete a larger project and produce a product or publication that can be highlighted on their resumes. Students should consider the factors below when deciding whether a capstone or thesis may be more appropriate to pursue.

A capstone is a practical or real-world project that can emphasize preparation for professional practice. A capstone is more appropriate if:

• you don't necessarily need or want the experience of the research process or writing a big publication
• you want more input on your project, from fellow students and instructors
• you want more structure to your project, including assignment deadlines and due dates
• you want to complete the project or graduate in a timely manner

A student can enroll in MSDS 498 Capstone in any term. However, capstone specialization courses can provide a unique student experience and may be offered only twice a year. 

A thesis is an academic-focused research project with broader applicability. A thesis is more appropriate if:

• you want to get a PhD or other advanced degree and want the experience of the research process and writing for publication
• you want to work individually with a specific faculty member who serves as your thesis adviser
• you are more self-directed, are good at managing your own projects with very little supervision, and have a clear direction for your work
• you have a project that requires more time to pursue

Students can enroll in MSDS 590 Thesis as long as there is an approved thesis project proposal, identified thesis adviser, and all other required documentation at least two weeks before the start of any term.

From Faculty Director, Thomas W. Miller, PhD

Capstone projects and thesis research give students a chance to study topics of special interest to them. Students can highlight analytical skills developed in the program. Work on capstone and thesis research projects often leads to publications that students can highlight on their resumes.”

A thesis is an individual research project that usually takes two to four terms to complete. Capstone course sections, on the other hand, represent a one-term commitment.

Students need to evaluate their options prior to choosing a capstone course section because capstones vary widely from one instructor to the next. There are both general and specialization-focused capstone sections. Some capstone sections offer in individual research projects, others offer team research projects, and a few give students a choice of individual or team projects.

Students should refer to the SPS Graduate Student Handbook for more information regarding registration for either MSDS 590 Thesis or MSDS 498 Capstone.

Capstone Experience

If students wish to engage with an outside organization to work on a project for capstone, they can refer to this checklist and lessons learned for some helpful tips.

Capstone Checklist

• Start early — set aside a minimum of one to two months prior to the capstone quarter to determine the industry and modeling interests.
• Networking — pitch your idea to potential organizations for projects and focus on the business benefits you can provide.
• Permission request — make sure your final project can be shared with others in the course and the information can be made public.
• Engagement — engage with the capstone professor prior to and immediately after getting the dataset to ensure appropriate scope for the 10 weeks.
• Teambuilding — recruit team members who have similar interests for the type of project during the first week of the course.

Capstone Lesson Learned

• Access to company data can take longer than expected; not having this access before or at the start of the term can severely delay the progress
• Project timeline should align with coursework timeline as closely as possible
• One point of contact (POC) for business facing to ensure streamlined messages and more effective time management with the organization
• Expectation management on both sides: (business) this is pro-bono (students) this does not guarantee internship or job opportunities
• Data security/masking not executed in time can risk the opportunity completely

Publication of Work

Northwestern University Libraries offers an option for students to publish their master’s thesis or capstone in Arch, Northwestern’s open access research and data repository.

Benefits for publishing your thesis:

• Your work will be indexed by search engines and discoverable by researchers around the world, extending your work’s impact beyond Northwestern
• Your work will be assigned a Digital Object Identifier (DOI) to ensure perpetual online access and to facilitate scholarly citation
• Your work will help accelerate discovery and increase knowledge in your subject domain by adding to the global corpus of public scholarly information

Get started:

• Visit Arch online
• Log in with your NetID
• Describe your thesis: title, author, date, keywords, rights, license, subject, etc.
• Upload your thesis or capstone PDF and any related supplemental files (data, code, images, presentations, documentation, etc.)
• Select a visibility: Public, Northwestern-only, Embargo (i.e. delayed release)
• Save your work to the repository

Your thesis manuscript or capstone report will then be published on the MSDS page. You can view other published work here .

For questions or support in publishing your thesis or capstone, please contact [email protected] .

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Review our examples before placing an order, learn how to draft academic papers, a step-by-step guide to dissertation data analysis.

How to Write a Dissertation Conclusion? | Tips & Examples

What is PhD Thesis Writing? | Beginner’s Guide

A data analysis dissertation is a complex and challenging project requiring significant time, effort, and expertise. Fortunately, it is possible to successfully complete a data analysis dissertation with careful planning and execution.

As a student, you must know how important it is to have a strong and well-written dissertation, especially regarding data analysis. Proper data analysis is crucial to the success of your research and can often make or break your dissertation.

To get a better understanding, you may review the data analysis dissertation examples listed below;

• Impact of Leadership Style on the Job Satisfaction of Nurses
• Effect of Brand Love on Consumer Buying Behaviour in Dietary Supplement Sector
• An Insight Into Alternative Dispute Resolution
• An Investigation of Cyberbullying and its Impact on Adolescent Mental Health in UK

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Types of data analysis for dissertation.

The various types of data Analysis in a Dissertation are as follows;

1.   Qualitative Data Analysis

Qualitative data analysis is a type of data analysis that involves analyzing data that cannot be measured numerically. This data type includes interviews, focus groups, and open-ended surveys. Qualitative data analysis can be used to identify patterns and themes in the data.

2.   Quantitative Data Analysis

Quantitative data analysis is a type of data analysis that involves analyzing data that can be measured numerically. This data type includes test scores, income levels, and crime rates. Quantitative data analysis can be used to test hypotheses and to look for relationships between variables.

3.   Descriptive Data Analysis

Descriptive data analysis is a type of data analysis that involves describing the characteristics of a dataset. This type of data analysis summarizes the main features of a dataset.

4.   Inferential Data Analysis

Inferential data analysis is a type of data analysis that involves making predictions based on a dataset. This type of data analysis can be used to test hypotheses and make predictions about future events.

5.   Exploratory Data Analysis

Exploratory data analysis is a type of data analysis that involves exploring a data set to understand it better. This type of data analysis can identify patterns and relationships in the data.

Time Period to Plan and Complete a Data Analysis Dissertation?

When planning dissertation data analysis, it is important to consider the dissertation methodology structure and time series analysis as they will give you an understanding of how long each stage will take. For example, using a qualitative research method, your data analysis will involve coding and categorizing your data.

This can be time-consuming, so allowing enough time in your schedule is important. Once you have coded and categorized your data, you will need to write up your findings. Again, this can take some time, so factor this into your schedule.

Finally, you will need to proofread and edit your dissertation before submitting it. All told, a data analysis dissertation can take anywhere from several weeks to several months to complete, depending on the project’s complexity. Therefore, starting planning early and allowing enough time in your schedule to complete the task is important.

Essential Strategies for Data Analysis Dissertation

A.   Planning

The first step in any dissertation is planning. You must decide what you want to write about and how you want to structure your argument. This planning will involve deciding what data you want to analyze and what methods you will use for a data analysis dissertation.

B.   Prototyping

Once you have a plan for your dissertation, it’s time to start writing. However, creating a prototype is important before diving head-first into writing your dissertation. A prototype is a rough draft of your argument that allows you to get feedback from your advisor and committee members. This feedback will help you fine-tune your argument before you start writing the final version of your dissertation.

C.   Executing

After you have created a plan and prototype for your data analysis dissertation, it’s time to start writing the final version. This process will involve collecting and analyzing data and writing up your results. You will also need to create a conclusion section that ties everything together.

D.   Presenting

The final step in acing your data analysis dissertation is presenting it to your committee. This presentation should be well-organized and professionally presented. During the presentation, you’ll also need to be ready to respond to questions concerning your dissertation.

Data Analysis Tools

Numerous suggestive tools are employed to assess the data and deduce pertinent findings for the discussion section. The tools used to analyze data and get a scientific conclusion are as follows:

a.     Excel

Excel is a spreadsheet program part of the Microsoft Office productivity software suite. Excel is a powerful tool that can be used for various data analysis tasks, such as creating charts and graphs, performing mathematical calculations, and sorting and filtering data.

b.     Google Sheets

Google Sheets is a free online spreadsheet application that is part of the Google Drive suite of productivity software. Google Sheets is similar to Excel in terms of functionality, but it also has some unique features, such as the ability to collaborate with other users in real-time.

c.     SPSS

SPSS is a statistical analysis software program commonly used in the social sciences. SPSS can be used for various data analysis tasks, such as hypothesis testing, factor analysis, and regression analysis.

d.     STATA

STATA is a statistical analysis software program commonly used in the sciences and economics. STATA can be used for data management, statistical modelling, descriptive statistics analysis, and data visualization tasks.

SAS is a commercial statistical analysis software program used by businesses and organizations worldwide. SAS can be used for predictive modelling, market research, and fraud detection.

R is a free, open-source statistical programming language popular among statisticians and data scientists. R can be used for tasks such as data wrangling, machine learning, and creating complex visualizations.

g.     Python

A variety of applications may be used using the distinctive programming language Python, including web development, scientific computing, and artificial intelligence. Python also has a number of modules and libraries that can be used for data analysis tasks, such as numerical computing, statistical modelling, and data visualization.

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Tips to Compose a Successful Data Analysis Dissertation

a.   Choose a Topic You’re Passionate About

The first step to writing a successful data analysis dissertation is to choose a topic you’re passionate about. Not only will this make the research and writing process more enjoyable, but it will also ensure that you produce a high-quality paper.

Choose a topic that is particular enough to be covered in your paper’s scope but not so specific that it will be challenging to obtain enough evidence to substantiate your arguments.

b.   Do Your Research

data analysis in research is an important part of academic writing. Once you’ve selected a topic, it’s time to begin your research. Be sure to consult with your advisor or supervisor frequently during this stage to ensure that you are on the right track. In addition to secondary sources such as books, journal articles, and reports, you should also consider conducting primary research through surveys or interviews. This will give you first-hand insights into your topic that can be invaluable when writing your paper.

c.   Develop a Strong Thesis Statement

After you’ve done your research, it’s time to start developing your thesis statement. It is arguably the most crucial part of your entire paper, so take care to craft a clear and concise statement that encapsulates the main argument of your paper.

Remember that your thesis statement should be arguable—that is, it should be capable of being disputed by someone who disagrees with your point of view. If your thesis statement is not arguable, it will be difficult to write a convincing paper.

d.   Write a Detailed Outline

Once you have developed a strong thesis statement, the next step is to write a detailed outline of your paper. This will offer you a direction to write in and guarantee that your paper makes sense from beginning to end.

Your outline should include an introduction, in which you state your thesis statement; several body paragraphs, each devoted to a different aspect of your argument; and a conclusion, in which you restate your thesis and summarize the main points of your paper.

e.   Write Your First Draft

With your outline in hand, it’s finally time to start writing your first draft. At this stage, don’t worry about perfecting your grammar or making sure every sentence is exactly right—focus on getting all of your ideas down on paper (or onto the screen). Once you have completed your first draft, you can revise it for style and clarity.

And there you have it! Following these simple tips can increase your chances of success when writing your data analysis dissertation. Just remember to start early, give yourself plenty of time to research and revise, and consult with your supervisor frequently throughout the process.

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Studying the above examples gives you valuable insight into the structure and content that should be included in your own data analysis dissertation. You can also learn how to effectively analyze and present your data and make a lasting impact on your readers.

In addition to being a useful resource for completing your dissertation, these examples can also serve as a valuable reference for future academic writing projects. By following these examples and understanding their principles, you can improve your data analysis skills and increase your chances of success in your academic career.

You may also contact Premier Dissertations to develop your data analysis dissertation.

For further assistance, some other resources in the dissertation writing section are shared below;

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Data science masters theses.

The Master of Science in Data Science program requires the successful completion of 12 courses to obtain a degree. These requirements cover six core courses, a leadership or project management course, two required courses corresponding to a declared specialization, two electives, and a capstone project or thesis. This collection contains a selection of masters theses or capstone projects by MSDS graduates.

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Mastering Dissertation Data Analysis: A Comprehensive Guide

By Laura Brown on 29th December 2023

To craft an effective dissertation data analysis chapter, you need to follow some simple steps:

• Start by planning the structure and objectives of the chapter.
• Clearly set the stage by providing a concise overview of your research design and methodology.
• Proceed to thorough data preparation, ensuring accuracy and organisation.
• Justify your methods and present the results using visual aids for clarity.
• Discuss the findings within the context of your research questions.
• Finally, review and edit your chapter to ensure coherence.

This approach will ensure a well-crafted and impactful analysis section.

Before delving into details on how you can come up with an engaging data analysis show in your dissertation, we first need to understand what it is and why it is required.

What Is Data Analysis In A Dissertation?

The data analysis chapter is a crucial section of a research dissertation that involves the examination, interpretation, and synthesis of collected data. In this chapter, researchers employ statistical techniques, qualitative methods, or a combination of both to make sense of the data gathered during the research process.

Why Is The Data Analysis Chapter So Important?

The primary objectives of the data analysis chapter are to identify patterns, trends, relationships, and insights within the data set. Researchers use various tools and software to conduct a thorough analysis, ensuring that the results are both accurate and relevant to the research questions or hypotheses. Ultimately, the findings derived from this chapter contribute to the overall conclusions of the dissertation, providing a basis for drawing meaningful and well-supported insights.

Steps Required To Craft Data Analysis Chapter To Perfection

Now that we have an idea of what a dissertation analysis chapter is and why it is necessary to put it in the dissertation, let’s move towards how we can create one that has a significant impact. Our guide will move around the bulleted points that have been discussed initially in the beginning. So, it’s time to begin.

Step 1: Planning Your Data Analysis Chapter

Planning your data analysis chapter is a critical precursor to its successful execution.

• Begin by outlining the chapter structure to provide a roadmap for your analysis.
• Start with an introduction that succinctly introduces the purpose and significance of the data analysis in the context of your research.
• Following this, delineate the chapter into sections such as Data Preparation, where you detail the steps taken to organise and clean your data.
• Plan on to clearly define the Data Analysis Techniques employed, justifying their relevance to your research objectives.
• As you progress, plan for the Results Presentation, incorporating visual aids for clarity. Lastly, earmark a section for the Discussion of Findings, where you will interpret results within the broader context of your research questions.

This structured approach ensures a comprehensive and cohesive data analysis chapter, setting the stage for a compelling narrative that contributes significantly to your dissertation. You can always seek our dissertation data analysis help to plan your chapter.

Step 2: Setting The Stage – Introduction to Data Analysis

Your primary objective is to establish a solid foundation for the analytical journey. You need to skillfully link your data analysis to your research questions, elucidating the direct relevance and purpose of the upcoming analysis.

Simultaneously, define key concepts to provide clarity and ensure a shared understanding of the terms integral to your study. Following this, offer a concise overview of your data set characteristics, outlining its source, nature, and any noteworthy features.

This meticulous groundwork alongside our help with dissertation data analysis lays the base for a coherent and purposeful chapter, guiding readers seamlessly into the subsequent stages of your dissertation.

Step 3: Data Preparation

Now this is another pivotal phase in the data analysis process, ensuring the integrity and reliability of your findings. You should start with an insightful overview of the data cleaning and preprocessing procedures, highlighting the steps taken to refine and organise your dataset. Then, discuss any challenges encountered during the process and the strategies employed to address them.

Moving forward, delve into the specifics of data transformation procedures, elucidating any alterations made to the raw data for analysis. Clearly describe the methods employed for normalisation, scaling, or any other transformations deemed necessary. It will not only enhance the quality of your analysis but also foster transparency in your research methodology, reinforcing the robustness of your data-driven insights.

Step 4: Data Analysis Techniques

The data analysis section of a dissertation is akin to choosing the right tools for an artistic masterpiece. Carefully weigh the quantitative and qualitative approaches, ensuring a tailored fit for the nature of your data.

Quantitative Analysis

• Descriptive Statistics: Paint a vivid picture of your data through measures like mean, median, and mode. It’s like capturing the essence of your data’s personality.
• Inferential Statistics:Take a leap into the unknown, making educated guesses and inferences about your larger population based on a sample. It’s statistical magic in action.

Qualitative Analysis

• Thematic Analysis: Imagine your data as a novel, and thematic analysis as the tool to uncover its hidden chapters. Dissect the narrative, revealing recurring themes and patterns.
• Content Analysis: Scrutinise your data’s content like detectives, identifying key elements and meanings. It’s a deep dive into the substance of your qualitative data.

Providing Rationale for Chosen Methods

You should also articulate the why behind the chosen methods. It’s not just about numbers or themes; it’s about the story you want your data to tell. Through transparent rationale, you should ensure that your chosen techniques align seamlessly with your research goals, adding depth and credibility to the analysis.

Step 5: Presentation Of Your Results

You can simply break this process into two parts.

a.    Creating Clear and Concise Visualisations

Effectively communicate your findings through meticulously crafted visualisations. Use tables that offer a structured presentation, summarising key data points for quick comprehension. Graphs, on the other hand, visually depict trends and patterns, enhancing overall clarity. Thoughtfully design these visual aids to align with the nature of your data, ensuring they serve as impactful tools for conveying information.

b.    Interpreting and Explaining Results

Go beyond mere presentation by providing insightful interpretation by taking data analysis services for dissertation. Show the significance of your findings within the broader research context. Moreover, articulates the implications of observed patterns or relationships. By weaving a narrative around your results, you guide readers through the relevance and impact of your data analysis, enriching the overall understanding of your dissertation’s key contributions.

Step 6: Discussion of Findings

While discussing your findings and dissertation discussion chapter , it’s like putting together puzzle pieces to understand what your data is saying. You can always take dissertation data analysis help to explain what it all means, connecting back to why you started in the first place.

Be honest about any limitations or possible biases in your study; it’s like showing your cards to make your research more trustworthy. Comparing your results to what other smart people have found before you adds to the conversation, showing where your work fits in.

Looking ahead, you suggest ideas for what future researchers could explore, keeping the conversation going. So, it’s not just about what you found, but also about what comes next and how it all fits into the big picture of what we know.

Step 7: Writing Style and Tone

In order to perfectly come up with this chapter, follow the below points in your writing and adjust the tone accordingly,

• Use clear and concise language to ensure your audience easily understands complex concepts.
• Avoid unnecessary jargon in data analysis for thesis, and if specialised terms are necessary, provide brief explanations.
• Keep your writing style formal and objective, maintaining an academic tone throughout.
• Avoid overly casual language or slang, as the data analysis chapter is a serious academic document.
• Clearly define terms and concepts, providing specific details about your data preparation and analysis procedures.
• Use precise language to convey your ideas, minimising ambiguity.
• Follow a consistent formatting style for headings, subheadings, and citations to enhance readability.
• Ensure that tables, graphs, and visual aids are labelled and formatted uniformly for a polished presentation.
• Connect your analysis to the broader context of your research by explaining the relevance of your chosen methods and the importance of your findings.
• Offer a balance between detail and context, helping readers understand the significance of your data analysis within the larger study.
• Present enough detail to support your findings but avoid overwhelming readers with excessive information.
• Use a balance of text and visual aids to convey information efficiently.
• Maintain reader engagement by incorporating transitions between sections and effectively linking concepts.
• Use a mix of sentence structures to add variety and keep the writing engaging.
• Eliminate grammatical errors, typos, and inconsistencies through thorough proofreading.
• Consider seeking feedback from peers or mentors to ensure the clarity and coherence of your writing.

You can seek a data analysis dissertation example or sample from CrowdWriter to better understand how we write it while following the above-mentioned points.

Step 8: Reviewing and Editing

Reviewing and editing your data analysis chapter is crucial for ensuring its effectiveness and impact. By revising your work, you refine the clarity and coherence of your analysis, enhancing its overall quality.

Seeking feedback from peers, advisors or dissertation data analysis services provides valuable perspectives, helping identify blind spots and areas for improvement. Addressing common writing pitfalls, such as grammatical errors or unclear expressions, ensures your chapter is polished and professional.

Taking the time to review and edit not only strengthens the academic integrity of your work but also contributes to a final product that is clear, compelling, and ready for scholarly scrutiny.

Concluding On This Data Analysis Help

Be it master thesis data analysis, an undergraduate one or for PhD scholars, the steps remain almost the same as we have discussed in this guide. The primary focus is to be connected with your research questions and objectives while writing your data analysis chapter.

Do not lose your focus and choose the right analysis methods and design. Make sure to present your data through various visuals to better explain your data and engage the reader as well. At last, give it a detailed read and seek assistance from experts and your supervisor for further improvement.

Laura Brown, a senior content writer who writes actionable blogs at Crowd Writer.

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Home > Dissertations, Theses & Capstones Projects by Program > Data Analysis & Visualization Master’s Theses and Capstone Projects

Data Analysis & Visualization Master’s Theses and Capstone Projects

Dissertations/theses/capstones from 2024 2024.

Assessing Job Vulnerability and Employment Growth in the Era of Large Language Models (LLMs) , Prudence P. Brou

The Charge Forward: An Assessment of Electric Vehicle Charging Infrastructure in New York City , Christopher S. Cali

Visualizing a Life, Uprooted: An Interactive, Web-Map and Scroll-Driven Exploration of the Oral History of my Great-Grandfather – from Ottoman Cilicia to Lebanon and Beyond , Alyssa Campbell

Examining the Health Risks of Particulate Matter 2.5 in New York City: How it Affects Marginalized Groups and the Steps Needed to Reduce Air Pollution , Freddy Castro

Clustering of Patients with Heart Disease , Mukadder Cinar

Modeling of COVID-19 Clinical Outcomes in Mexico: An Analysis of Demographic, Clinical, and Chronic Disease Factors , Livia Clarete

The Complete Sight and Sound Greatest Films of All Time Database , Katie Donia

Wrapped Insights: A Data-Driven Approach to Personalizing User Experiences in a Digital Tipping Platform , Hamza Habeeb

The Efficacy of Using Machine Learning Techniques for Identifying and Classifying “Fake News” , Muhammad Islam

Invisible Hand of Socioeconomic Factors in Rising Trend of Maternal Mortality Rates in the U.S. , Disha Kanada

Factors that Impact New York City Public High School Graduation: Finding Barriers to Education through Data Analysis and Visualization , Kyoung Kang

Multi-Perspective Analysis for Derivative Financial Product Prediction with Stacked Recurrent Neural Networks, Natural Language Processing and Large Language Model , Ethan Lo

What Does One Billion Dollars Look Like?: Visualizing Extreme Wealth , William Mahoney Luckman

Making Sense of Making Parole in New York , Alexandra McGlinchy

Employment Outcomes in Higher Education , Yunxia Wei

Dissertations/Theses/Capstones from 2023 2023

Phantom Shootings , Allan Ambris

Naming Venus: An Exploration of Goddesses, Heroines, and Famous Women , Kavya Beheraj

Social Impacts of Robotics on the Labor and Employment Market , Kelvin Espinal

Fighting the Invisibility of Domestic Violence , Yesenny Fernandez

Navigating Through World’s Military Spending Data with Scroll-Event Driven Visualization , Hong Beom Hur

Evocative Visualization of Void and Fluidity , Tomiko Karino

Analyzing Relationships with Machine Learning , Oscar Ko

Analyzing ‘Fight the Power’ Part 1: Music and Longevity Across Evolving Marketing Eras , Shokolatte Tachikawa

Stand-up Comedy Visualized , Berna Yenidogan

Dissertations/Theses/Capstones from 2022 2022

El Ritmo del Westside: Exploring the Musical Landscape of San Antonio’s Historic Westside , Valeria Alderete

A Comparison of Machine Learning Techniques for Validating Students’ Proficiency in Mathematics , Alexander Avdeev

A Machine Learning Approach to Predicting the Onset of Type II Diabetes in a Sample of Pima Indian Women , Meriem Benarbia

Disrepair, Displacement and Distress: Finding Housing Stories Through Data Visualizations , Jennifer Cheng

Blockchain: Key Principles , Nadezda Chikurova

Data for Power: A Visual Tool for Organizing Unions , Shay Culpepper

Happiness From a Different Perspective , Suparna Das

Happiness and Policy Implications: A Sociological View , Sarah M. Kahl

Heating Fire Incidents in New York City , Merissa K. Lissade

NYC vs. Covid-19: The Human and Financial Resources Deployed to Fight the Most Expensive Health Emergency in History in NYC during the Year 2020 , Elmer A. Maldonado Ramirez

Slices of the Big Apple: A Visual Explanation and Analysis of the New York City Budget , Joanne Ramadani

The Value of NFTs , Angelina Tham

Air Pollution, Climate Change, and Our Health , Kathia Vargas Feliz

Peru's Fishmeal Industry: Its Societal and Environmental Impact , Angel Vizurraga

Why, New York City? Gauging the Quality of Life Through the Thoughts of Tweeters , Sheryl Williams

Dissertations/Theses/Capstones from 2021 2021

Data Analysis and Visualization to Dismantle Gender Discrimination in the Field of Technology , Quinn Bolewicki

Remaking Cinema: Black Hollywood Films, Filmmakers, and Finances , Kiana A. Carrington

Detecting Stance on Covid-19 Vaccine in a Polarized Media , Rodica Ceslov

Dota 2 Hero Selection Analysis , Zhan Gong

An Analysis of Machine Learning Techniques for Economic Recession Prediction , Sheridan Kamal

Black Women in Romance , Vianny C. Lugo Aracena

The Public Innovations Explorer: A Geo-Spatial & Linked-Data Visualization Platform For Publicly Funded Innovation Research In The United States , Seth Schimmel

Making Space for Unquantifiable Data: Hand-drawn Data Visualization , Eva Sibinga

Who Pays? New York State Political Donor Matching with Machine Learning , Annalisa Wilde

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Research Topics & Ideas: Data Science

50 Topic Ideas To Kickstart Your Research Project

If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.

To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .

Data Science-Related Research Topics

• Developing machine learning models for real-time fraud detection in online transactions.
• The use of big data analytics in predicting and managing urban traffic flow.
• Investigating the effectiveness of data mining techniques in identifying early signs of mental health issues from social media usage.
• The application of predictive analytics in personalizing cancer treatment plans.
• Analyzing consumer behavior through big data to enhance retail marketing strategies.
• The role of data science in optimizing renewable energy generation from wind farms.
• Developing natural language processing algorithms for real-time news aggregation and summarization.
• The application of big data in monitoring and predicting epidemic outbreaks.
• Investigating the use of machine learning in automating credit scoring for microfinance.
• The role of data analytics in improving patient care in telemedicine.
• Developing AI-driven models for predictive maintenance in the manufacturing industry.
• The use of big data analytics in enhancing cybersecurity threat intelligence.
• Investigating the impact of sentiment analysis on brand reputation management.
• The application of data science in optimizing logistics and supply chain operations.
• Developing deep learning techniques for image recognition in medical diagnostics.
• The role of big data in analyzing climate change impacts on agricultural productivity.
• Investigating the use of data analytics in optimizing energy consumption in smart buildings.
• The application of machine learning in detecting plagiarism in academic works.
• Analyzing social media data for trends in political opinion and electoral predictions.
• The role of big data in enhancing sports performance analytics.
• Developing data-driven strategies for effective water resource management.
• The use of big data in improving customer experience in the banking sector.
• Investigating the application of data science in fraud detection in insurance claims.
• The role of predictive analytics in financial market risk assessment.
• Developing AI models for early detection of network vulnerabilities.

Data Science Research Ideas (Continued)

• The application of big data in public transportation systems for route optimization.
• Investigating the impact of big data analytics on e-commerce recommendation systems.
• The use of data mining techniques in understanding consumer preferences in the entertainment industry.
• Developing predictive models for real estate pricing and market trends.
• The role of big data in tracking and managing environmental pollution.
• Investigating the use of data analytics in improving airline operational efficiency.
• The application of machine learning in optimizing pharmaceutical drug discovery.
• Analyzing online customer reviews to inform product development in the tech industry.
• The role of data science in crime prediction and prevention strategies.
• Developing models for analyzing financial time series data for investment strategies.
• The use of big data in assessing the impact of educational policies on student performance.
• Investigating the effectiveness of data visualization techniques in business reporting.
• The application of data analytics in human resource management and talent acquisition.
• Developing algorithms for anomaly detection in network traffic data.
• The role of machine learning in enhancing personalized online learning experiences.
• Investigating the use of big data in urban planning and smart city development.
• The application of predictive analytics in weather forecasting and disaster management.
• Analyzing consumer data to drive innovations in the automotive industry.
• The role of data science in optimizing content delivery networks for streaming services.
• Developing machine learning models for automated text classification in legal documents.
• The use of big data in tracking global supply chain disruptions.
• Investigating the application of data analytics in personalized nutrition and fitness.
• The role of big data in enhancing the accuracy of geological surveying for natural resource exploration.
• Developing predictive models for customer churn in the telecommunications industry.
• The application of data science in optimizing advertisement placement and reach.

Recent Data Science-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.

Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• Data Science in Healthcare: COVID-19 and Beyond (Hulsen, 2022)
• Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation (Mukherjee & Rao, 2022)
• Survey on Statistics and ML in Data Science and Effect in Businesses (Reddy et al., 2022)
• Visualization in Data Science VDS @ KDD 2022 (Plant et al., 2022)
• An Essay on How Data Science Can Strengthen Business (Santos, 2023)
• A Deep study of Data science related problems, application and machine learning algorithms utilized in Data science (Ranjani et al., 2022)
• You Teach WHAT in Your Data Science Course?!? (Posner & Kerby-Helm, 2022)
• Statistical Analysis for the Traffic Police Activity: Nashville, Tennessee, USA (Tufail & Gul, 2022)
• Data Management and Visual Information Processing in Financial Organization using Machine Learning (Balamurugan et al., 2022)
• A Proposal of an Interactive Web Application Tool QuickViz: To Automate Exploratory Data Analysis (Pitroda, 2022)
• Applications of Data Science in Respective Engineering Domains (Rasool & Chaudhary, 2022)
• Jupyter Notebooks for Introducing Data Science to Novice Users (Fruchart et al., 2022)
• Towards a Systematic Review of Data Science Programs: Themes, Courses, and Ethics (Nellore & Zimmer, 2022)
• Application of data science and bioinformatics in healthcare technologies (Veeranki & Varshney, 2022)
• TAPS Responsibility Matrix: A tool for responsible data science by design (Urovi et al., 2023)
• Data Detectives: A Data Science Program for Middle Grade Learners (Thompson & Irgens, 2022)
• MACHINE LEARNING FOR NON-MAJORS: A WHITE BOX APPROACH (Mike & Hazzan, 2022)
• COMPONENTS OF DATA SCIENCE AND ITS APPLICATIONS (Paul et al., 2022)
• Analysis on the Application of Data Science in Business Analytics (Wang, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

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If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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How to collect data for your thesis

Collecting theoretical data

Search for theses on your topic, use content-sharing platforms, collecting empirical data, qualitative vs. quantitative data, frequently asked questions about gathering data for your thesis, related articles.

After choosing a topic for your thesis , you’ll need to start gathering data. In this article, we focus on how to effectively collect theoretical and empirical data.

Empirical data : unique research that may be quantitative, qualitative, or mixed.

Theoretical data : secondary, scholarly sources like books and journal articles that provide theoretical context for your research.

Thesis : the culminating, multi-chapter project for a bachelor’s, master’s, or doctoral degree.

Qualitative data : info that cannot be measured, like observations and interviews .

Quantitative data : info that can be measured and written with numbers.

At this point in your academic life, you are already acquainted with the ways of finding potential references. Some obvious sources of theoretical material are:

• edited volumes
• conference proceedings
• online databases like Google Scholar , ERIC , or Scopus

You can also take a look at the top list of academic search engines .

Looking at other theses on your topic can help you see what approaches have been taken and what aspects other writers have focused on. Pay close attention to the list of references and follow the bread-crumbs back to the original theories and specialized authors.

Another method for gathering theoretical data is to read through content-sharing platforms. Many people share their papers and writings on these sites. You can either hunt sources, get some inspiration for your own work or even learn new angles of your topic. 

Some popular content sharing sites are:

With these sites, you have to check the credibility of the sources. You can usually rely on the content, but we recommend double-checking just to be sure. Take a look at our guide on what are credible sources?

The more you know, the better. The guide, " How to undertake a literature search and review for dissertations and final year projects ," will give you all the tools needed for finding literature .

In order to successfully collect empirical data, you have to choose first what type of data you want as an outcome. There are essentially two options, qualitative or quantitative data. Many people mistake one term with the other, so it’s important to understand the differences between qualitative and quantitative research .

Boiled down, qualitative data means words and quantitative means numbers. Both types are considered primary sources . Whichever one adapts best to your research will define the type of methodology to carry out, so choose wisely.

In the end, having in mind what type of outcome you intend and how much time you count on will lead you to choose the best type of empirical data for your research. For a detailed description of each methodology type mentioned above, read more about collecting data .

Once you gather enough theoretical and empirical data, you will need to start writing. But before the actual writing part, you have to structure your thesis to avoid getting lost in the sea of information. Take a look at our guide on how to structure your thesis for some tips and tricks.

The key to knowing what type of data you should collect for your thesis is knowing in advance the type of outcome you intend to have, and the amount of time you count with.

Some obvious sources of theoretical material are journals, libraries and online databases like Google Scholar , ERIC or Scopus , or take a look at the top list of academic search engines . You can also search for theses on your topic or read content sharing platforms, like Medium , Issuu , or Slideshare .

To gather empirical data, you have to choose first what type of data you want. There are two options, qualitative or quantitative data. You can gather data through observations, interviews, focus groups, or with surveys, tests, and existing databases.

Qualitative data means words, information that cannot be measured. It may involve multimedia material or non-textual data. This type of data claims to be detailed, nuanced and contextual.

Quantitative data means numbers, information that can be measured and written with numbers. This type of data claims to be credible, scientific and exact.

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• Dissertation & Thesis Outline | Example & Free Templates

Dissertation & Thesis Outline | Example & Free Templates

Published on June 7, 2022 by Tegan George . Revised on November 21, 2023.

A thesis or dissertation outline is one of the most critical early steps in your writing process . It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding the specifics of your dissertation topic and showcasing its relevance to your field.

Generally, an outline contains information on the different sections included in your thesis or dissertation , such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review, research methods, avenues for future research, etc.)

In the final product, you can also provide a chapter outline for your readers. This is a short paragraph at the end of your introduction to inform readers about the organizational structure of your thesis or dissertation. This chapter outline is also known as a reading guide or summary outline.

Table of contents

How to outline your thesis or dissertation, dissertation and thesis outline templates, chapter outline example, sample sentences for your chapter outline, sample verbs for variation in your chapter outline, other interesting articles, frequently asked questions about thesis and dissertation outlines.

While there are some inter-institutional differences, many outlines proceed in a fairly similar fashion.

• Working Title
• “Elevator pitch” of your work (often written last).
• Introduce your area of study, sharing details about your research question, problem statement , and hypotheses . Situate your research within an existing paradigm or conceptual or theoretical framework .
• Subdivide as you see fit into main topics and sub-topics.
• Describe your research methods (e.g., your scope , population , and data collection ).
• Present your research findings and share about your data analysis methods.
• Answer the research question in a concise way.
• Interpret your findings, discuss potential limitations of your own research and speculate about future implications or related opportunities.

For a more detailed overview of chapters and other elements, be sure to check out our article on the structure of a dissertation or download our template .

To help you get started, we’ve created a full thesis or dissertation template in Word or Google Docs format. It’s easy adapt it to your own requirements.

 Download Word template    Download Google Docs template

It can be easy to fall into a pattern of overusing the same words or sentence constructions, which can make your work monotonous and repetitive for your readers. Consider utilizing some of the alternative constructions presented below.

Example 1: Passive construction

The passive voice is a common choice for outlines and overviews because the context makes it clear who is carrying out the action (e.g., you are conducting the research ). However, overuse of the passive voice can make your text vague and imprecise.

Example 2: IS-AV construction

You can also present your information using the “IS-AV” (inanimate subject with an active verb ) construction.

A chapter is an inanimate object, so it is not capable of taking an action itself (e.g., presenting or discussing). However, the meaning of the sentence is still easily understandable, so the IS-AV construction can be a good way to add variety to your text.

Example 3: The “I” construction

Another option is to use the “I” construction, which is often recommended by style manuals (e.g., APA Style and Chicago style ). However, depending on your field of study, this construction is not always considered professional or academic. Ask your supervisor if you’re not sure.

Example 4: Mix-and-match

To truly make the most of these options, consider mixing and matching the passive voice , IS-AV construction , and “I” construction .This can help the flow of your argument and improve the readability of your text.

As you draft the chapter outline, you may also find yourself frequently repeating the same words, such as “discuss,” “present,” “prove,” or “show.” Consider branching out to add richness and nuance to your writing. Here are some examples of synonyms you can use.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

Research bias

• Anchoring bias
• Halo effect
• The Baader–Meinhof phenomenon
• The placebo effect
• Nonresponse bias
• Deep learning
• Generative AI
• Machine learning
• Reinforcement learning
• Supervised vs. unsupervised learning

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When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

• Your chapters (sometimes subdivided into further topics like literature review , research methods , avenues for future research, etc.)

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

George, T. (2023, November 21). Dissertation & Thesis Outline | Example & Free Templates. Scribbr. Retrieved August 26, 2024, from https://www.scribbr.com/dissertation/dissertation-thesis-outline/

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Department of Data Science

Master Thesis

Master theses.

Below you find our current topic proposals as pdf-files.

If you are interested in a certain topic, please send an e-mail to wima-abschlussarbeiten[at]lists.fau.de. Please refrain from writing emails to other addresses.

Your e-mail should include

• your transcript of records
• a letter of motivation (approximately half a page)
• desired date at which you want to start
• latest possible date of submission.

In your letter of motivation please state which of the topic proposals you are interested in. If none of these proposals interest you please state which type of thesis you desire (e.g. literature study) and which field you are interested in.

Topic proposals (with corresponding advisers)

• Optimization of Optical Particle Properties under Uncertainty (Frauke Liers)
• Analysis and Prediction of Asynchronous Event Sequences Considering Uncertainty @ Medical Technology (Frauke Liers, thesis together with Siemens Healthineers, Erlangen)
• Optimized Qubit Routing for Commuting Gates (Frauke Liers)

Furthermore, students are welcome to contact abschlussarbeiten[at]lists.fau.de to jointly define a thesis topic in one of the following areas:

• Optimization under uncertainty (Frauke Liers)
• Integration of data analysis with optimization (Frauke Liers)

Previous Theses

• Adviser: Alexander Martin
• Adviser: Kevin-Martin Aigner, Fauke Liers
• Adviser: Jan Rolfes, Timm Oertel
• Adviser: Jan Rolfes, Frauke Liers
• Adviser: Jan Rolfes, Jana Dienstbier, Frauke Liers
• Adviser: Martina Kuchlbauer, Frauke Liers
• Adviser: Martina Kuchlbauer, Jana Dienstbier, Frauke Liers
• Adviser: Yiannis Giannakopoulos
• Adviser: Andreas Bärmann, Alexander Martin
• Adviser: Jan Krause, Andreas Bärmann, Alexander Martin
• Adviser: Christian Biefel, Frauke Liers
• Adviser: Jonasz Staszek, Alexander Martin
• Adviser: Lukas Hümbs, Alexander Martin
• Adviser:Kristin Braun, Frauke Liers
• Adviser: Lukas Glomb, Florian Rösel, Frauke Liers
• Adviser: Bismark Singh, Alexander Martin
• Adviser:Kristin Braun, Johannes Thürauf, Robert Burlacu,Frauke Liers
• Adviser: Oskar Schneider, Alexander Martin
• Optimization of energy supply in critical infrastructures using battery electric vehicles Adviser: Bismark Singh, Alexander Martin
• Approximations to the Clustered Traveling Salesman Problem with an Application in Perm, Russia Adviser: Bismark Singh, Alexander Martin
• Decomposition methods for energy optimization models Adviser: Bismark Singh, Alexander Martin
• Aircraft Trajectory Optimization and Disjoint Paths Adviser: Benno Hoch, Frauke Liers
• Obere und untere Schranken für das Set-Cover mittels Lasserre Hierachie Adviser: Jan Rolfes, Alexander Martin
• Separation Algorithms and Reformulations for Single-Item Lot-Sizing with Non-Delivery Penalties Adviser: Dieter Weninger
• Optimales Scheduling an Maschinen Adviser: Kevin-Martin Aigner, Jan Rolfes, Alexander Martin
• Optimization of scenario-expanded tail assignment problems including maintenance Adviser: Lukas Glomb, Florian Rösel, Frauke Liers
• Anti-Lifting: Sparsifizierung bei gemischt-ganzzahligen Optimierungsproblemen Adviser: Katrin Halbig, Alexander Martin
• Solving Mixed-Integer Problems using Machine Learning for the Optimization of Energy Production Adviser: Christian Biefel, Lukas Hümbs, Alexander Martin
• Preprocessing Techniques for Mixed-Integer Bilevel Problems Adviser: Thomas Kleinert, Dieter Weninger, Alexander Martin
• Projection and Farkas Type Lemmas for Mixed Integer Programs Adviser: Richard Krug, Alexander Martin
• Gamma-robuste lineare Komplementaritätssysteme Adviser: Vanessa Krebs, Martin Schmidt
• Verseiloptimierung (Kooperation mit LEONI) Adviser: Alexander Martin
• Data-based Methods for Chance Constraints in DC Optimal Power Flow with Extension to Curtailment Adviser: Kevin-Martin Aigner, Frauke Liers
• Different Concepts of Distributionally Robust Vehicle Routing Problems Adviser: Sebastian Tschuppik, Dennis Adelhütte, Frauke Liers
• Optimierungsmethoden für Logistikprozesse im Krankenhaus Adviser: Andreas Bärmann, Dieter Weninger, Alexander Martin
• Lagrange Relaxierung Energienetze Kooperation Jülich Adviser: Johannes Thürauf, Lars Schwee
• The price of robustness in the European entry-exit market Adviser: Thomas Kleinert, Frauke Liers
• Kosteneffizienter Betrieb von Smart Grids mit Gomory Schnittebenen Adviser: Martin Schmidt, Galina Orlinskaya
• On finding sparse descriptions of polyhedra with mixed-integer programming Adviser: Alexander Martin, Patrick Gemander, Oskar Schneider
• Mathematische Optimierung für chromatographische Verfahren zur Trennung von Stoffgemischen Adviser: Frauke Liers, Robert Burlacu
• Machine Learning gestützte Prognose der Performance zukünftiger Lieferungen unter Verwendung von adaptiven Algorithmen und geeigneten Datenstrukturen im Transportmanagement Adviser: Frauke Liers, Andreas Bärmann
• Discrete optimization for optimal train control Adviser: Alexander Martin, Andreas Bärmann
• Optimierte Flottenplanung in der Luftfahrt unter Berücksichtigung der Betankungsstrategie Adviser: Alexander Martin, Andreas Bärmann
• Lipschitzoptimierung am Beispiel des europäischen Gasmarktes Adviser: Martin Schmidt, Thomas Kleinert
• Graphzerlegungen und Alternating Direction Methode für Gasnetzwerke Adviser: Martin Schmidt
• Multikriterielle Optimierung für Graphendekompositionen in der Gasnetzoptimierung Adviser: Martin Schmidt
• Robuste Gleichgewichtsprobleme im Energiebereich Adviser: Martin Schmidt, Vanessa Krebs
• MIP Methoden in der Fördertechnik Adviser: Alexander Martin, Andreas Bärmann, Patrick Gemander
• Verwendung von SVMs für medizinische Diagnostik Adviser: Frauke Liers, Dieter Weninger
• Ausbauplanung für städtische Verkehrsnetze Adviser: Alexander Martin, Andreas Bärmann
• Zuschnittoptimierung und Parametervariation in der Flachglasindustrie Adviser: Lars Schewe
• Ermittlung optimaler Höchstabfluggewichte unter Unsicherheit Adviser: Alexander Martin, Lena Hupp, Martin Weibelzahl
• Online-Optimierung in Hinblick auf Prognoseunsicherheiten bei erneuerbaren Energien mittels basisorientierter Szenarienreduktion Adviser: Alexander Martin, Christoph Thurner
• A variable decomposition algorithm for production planning Adviser: Alexander Martin, Dieter Weninger
• Mixed integer moving horizon control for flexible energy storage systems Adviser: Martin Schmidt
• Mathematische Analyse von Kompaktheitsmaßen in der Gebietsplanung anhand eines Modells zur Dienstleisterauswahl bei Transportausschreibungen Adviser: Alexander Martin
• Methoden zur Laufzeitverbesserung eines Mixed-Integer Program in der Entsorgungslogistik Adviser: Alexander Martin
• Aktuelle Erkenntnisse bei Pivotregeln des Simplexverfahrens Adviser: Frauke Liers
• Radius of robust feasibility for the robust stochastic nomination validition problem in passive gas networks Adviser: Frauke Liers, Denis Aßmann
• Mathematische Modelle und Optimierung für die automatische Permutation von Schließanlagen Adviser: Alexander Martin
• Mathematische Modellierung von Stromnetzen: Ein Vergleich AC- und DC-Modell hinsichtlich Investitionsentscheidungen Adviser: Frauke Liers
• Diskrete Optimierung im Immobilien-Investing Adviser: Alexander Martin
• Polyedrische und komplexitätstheoretische Untersuchungen von bipartiten Matchingproblemen mit quadratischen Termen Adviser: Frauke Liers
• Discrete Selection of Diameters for Constructing Optimal Hydrogen Pipeline Networks Adviser: Lars Schewe
• Optimierte Tourenplanung im Krankentransport unter Berücksichtigung von Zeitfenstern Adviser: Frauke Liers
• Optimale Preiszonen und Investitionsentscheidungen unter Berücksichtigung von Stromspeichern – Eine modelltheoretische Analyse des Strommarkts Adviser: Alexander Martin
• Robuste Eigenanteilplanung und Belegungsplanung sowie Personalplanung für ein Pflegeheim Adviser: Frauke Liers
• Dynamische automatisierte Rampensteuerung Adviser: Alexander Martin
• A combinatorial splitting algorithm for checking feasibility of passive gas networks under uncertain injection patterns Adviser: Frauke Liers, Denis Aßmann
• Integrated Optimization Problems in the Airline Industry Adviser: Alexander Martin
• Mathematische Modellierung eines Produktionshochlaufs bei Kromberg & Schubert Adviser: Frauke Liers
• On the uniqueness of competitive market equilibria on DC networks Adviser: Martin Schmidt
• The Clique-Problem under Multiple-Choice Constraints with Cycle-Free Dependency Graphs Adviser: Alexander Martin, Andreas Bärmann
• A Decomposition Approach for a Multilevel Graph Partitioning Model of the German Electricity Market Adviser: Martin Schmidt
• Zyklisches Scheduling in der Kirche – Mathematische Modellierung und Optimierung Adviser: Alexander Martin, Thorsten Ramsauer
• Optimierung von Flugbahnen: Ein gemischt-ganzzahliges Modell zur Berechnung von optimalen Trajektorien-Netzwerken Adviser: Frauke Liers
• Robuste Optimierungsmethoden für Nominierungsvalidierung in Gasnetzwerken bei Nachfrageunsicherheiten Adviser: Frauke Liers, Denis Aßmann
• Stable Set Problem with Multiple Choice Constraints on Staircase Graphs Adviser: Alexander Martin
• Anwendung von robusten Flussproblemen für die optimale Speichersteuerung im Smart Grid Adviser: Frauke Liers
• Das Sternsingerproblem: Planung, Modellierung und mathematische Optimierung Adviser: Alexander Martin, Martin Weibelzahl
• A Bilevel Optimization Model for Holy Mass Planning Adviser: Alexander Martin, Martin Weibelzahl
• Optimal Personnel Management in Church: A Robust Optimization Approach for Operative and Strategic Planning Adviser: Frauke Liers, Martin Weibelzahl
• Active-Passive-Vehicle-Routing-Problem Adviser: Alexander Martin, Michael Drexl (Fraunhofer SCS)
• Robuste Optimierung in der Flugplanung: Entwicklung eines statischen sowie eines zeitexpandierten Modells zur robusten Zeitfenster-Zuordnung in der prätaktischen Phase Adviser: Frauke Liers
• Personaleinsatzplanung im Einzelhandel unter Berücksichtigung von Unsicherheiten mithilfe mathematischer Optimierung Adviser: Alexander Martin, Falk Meyerholz (Fraunhofer ILS)
• Clustering von Bahnweichen und Analyse von Störungen zur Optimierung der Instandhaltungsmaßnahmen Adviser: Frauke Liers, Thomas Böhm (DLR)
• Mikroökonomische Haushaltstheorie unter Unsicherheit: mathematische Perspektive Adviser: Alexander Martin, Martin Weibelzahl
• Lösung von realen Probleminstanzen bei der Tourenplanung in der ambulanten Pflege mit Hilfe eines Cluster-First-Route-Second-Ansatzes Adviser: Alexander Martin
• Revenue Management als Netzwerkproblem unter Unsicherheiten mit Anwendung im Fernbusmarkt Adviser: Alexander Martin, Lars Schewe
• Das Partial Digest Problem mit absoluten Fehlern als Matching und Anwendung der Lagrange-Relaxierung Adviser: Frauke Liers
• Optimierung im Produktionsablauf bei Elektrolux Rothenburg – Laserline (schriftliche Hausarbeit im Rahmen der Ersten Staatsprüfung für das Lehramt an Gymnasien in Bayern) Adviser: Alexander Martin
• Memetische Optimierung des Generalized Travelling Salesman Problems Adviser: Alexander Martin
• Estimation the optimal schedule of a Vehicle Routing Problem arising in Bulk Distribution Network Optimisation Adviser: Alexander Martin
• Nominierungsvalidierung bei Gasnetzen: Einfluss und mögliche Behandlung von Unsicherheiten Adviser: Frauke Liers
• Ein exaktes Lösungsverfahren für das Optimierungsproblem des Partial Digest mit absoluten Fehlern Adviser: Frauke Liers
• Mathematische Optimierung des Bidmanagements in der Reisebranche Adviser: Alexander Martin, Lars Schewe
• Optimierung der Staffeleinteilung in der Fußball-Landesliga Bayern und Konzipierung vereinsfreundlicher Spielpläne Adviser: Alexander Martin, Andreas Heidt
• Potentialanalyse für die Transportlogistik im Krankenhauswesen Adviser: Alexander Martin, Andrea Peter
• Anpassungstests mit Nuisanceparmetern für das lineare Regressionsmodell Adviser: Alexander Martin
• Robuste Optimierung für Scheduling Probleme im Luftverkehrsmanagement Adviser: Alexander Martin, Andreas Heidt
• Reisewegbasierte Flottenoptimierung bei differenzierter Passagiernachfrage Adviser: Alexander Martin
• Gemischt  bivariate Verteilungen unter Verwendung einer doppelt-stochastischen Summe und ihre Anwendunge n Adviser: Alexander Martin, Ingo Klein
• Optimierung einer Indoor-Navigation am Flughafen München mittels adaptivem, heuristischem Dijkstra-Verfahren basierend auf partieller Gridgraphenstruktur Adviser: Alexander Martin, Andreas Bärmann
• A Comparison of cutting-plane closures in R² and R³ Adviser: Alexander Martin, Sebastian Pokutta
• Tourenplanung bei der Abokiste Adviser: Alexander Martin, Andreas Bärmann
• Klassifizierung und Strukturanalyse von Produktionsplanungsmodellen, die durch gemischt-ganzzahlig-lineare Programme modelliert sind. Adviser: Alexander Martin, Dieter Weninger
• Performance-oriented Optimization Techniques for Facility Design Floorplanning Problems Adviser: Alexander Martin, Stefan Schmieder
• Standortoptimierung als Entscheidungshilfe für Familien bei der Wahl eines Wohnortes unter Berücksichtigung der Infrastruktur Adviser: Alexander Martin
• Kapazitätsbestimmung in linearen Netzwerken Adviser: Alexander Martin, Lars Schewe
• Portfoliooptimierung – Der Ansatz von Markowitz unter realen Nebenbedingungen Adviser: Alexander Martin
• Entwicklung eines Steuerungstools for Cross-Docking Prozesse bei der BMW Group Adviser: Alexander Martin
• Modellierung und Lösung eines mehrperiodischen deterministischen Standortplanungsproblems unter volantilen Bedarfsmengen Adviser: Alexander Martin
• Analytische Optimierung von Netzschutzkennlinien Adviser: Alexander Martin, Lars Schewe
• Hedging von Katastrophenrisiken durch den Einsatz von Industry Loss Warranties Adviser: Alexander Martin, Nadine Gatzert
• Anwendung mathematischer Werkzeuge zur Umlaufoptimierung am praktischen Beispiel Adviser: Alexander Martin
• Optimization Methods for Asset Liability Management in a Non-Life Insurance Company Adviser: Alexander Martin, Nadine Gatzert
• Parametrisierung von 3D-Blattmodellen zur Detektion und Ergänzung unvollständiger Messdaten Adviser: Alexander Martin, Günther Greiner
• Conducting Optimal Risk Classification for Substandard Annities in the Presence of Underwriting Risk Adviser: Alexander Martin
• Incorporating Convexe Hull into an Algorithmic Approach for Territory Design Problems Adviser: Alexander Martin, Sonja Friedrich
• Expanding Brand & Bound for binary integer programs with a pseudo-boolean solver and a SAT based Presolver Adviser: Alexander Martin
• “Effiziente randomisierte Algorithmen für das Erfüllbarkeitsproblem” – Beschleunigung durch Variableninvertierung Adviser: Alexander Martin
• Preprocessingansätze für die Planung von gekoppelten Strom-, Gas- und Wärmenetzen  Adviser: Alexander Martin, Andrea Zelmer, Debora Mahlke
• Die kontinuierliche Analyse der kooperativen Effizienz in Gesundheit mit Hilfe parametrischer iund nicht-parametrischer Verfahren Adviser: Alexander Martin, Freimut Bodendorf
• Incorporating Convex hulls into an Algorithmic Approach for Territory Design Problems Adviser: Alexander Martin
• Auffalten von orthogonalen Bäumen Adviser: Alexander Martin, Ute Günther
• Heuristic Approaches for the Gate Assignment Problem Adviser: Alexander Martin, Andrea Peter
• Synchronisation von Tauschpunkten für Flugbesatzungen und technische Anforderungen in der Rotationsplanung von  Verkehrsflugzeugen Adviser: Alexander Martin, Andrea Peter in co-operation with Lufthansa Passage
• Verteilte vs. ganzheitliche Optimierung in der Luftfahrt Adviser: Alexander Martin, Sebastian Pokutta, Andrea Peter in co-operation with DLR Braunschweig
• Optimale Schichtenerstellung zur Personalbedarfsermittlung Adviser: Alexander Martin, Henning Homfeld in co-operation with DB Schenker Rail
• Polyedrische Untersuchungen von Multiple Knapsack Ungleichungen Adviser: Alexander Martin, Henning Homfeld
• Using vehicle routing heuristics to estimate costs in gas cylinder delivery Adviser: Alexander Martin in co-operation with Linde Gas
• An Overview on Algorithms for Graph Reliability and possible Transfor for Dynamic Graph Reliability Adviser: Alexander Martin,  Sebastian Pokutta, Nicole  Nowak
• Eine Verallgemeinerung des Quadratic Bottleneck Assignment Problem und Anwendung Adviser: Alexander Martin, Lars Schewe, Sonja Friedrich
• Vergleich von Optimierungsmodellen beim Erdgashandel Adviser: Alexander Martin, Johannes Müller
• Branch and Cut Verfahren in der Standortplanung Adviser: Wolfgang Domschke, Alexander Martin
• Evolution of the Performance of Separate Scheduling Solvers Forced to Cooperate Adviser: Alexander Martin, Andrea Peter
• Heuristische Ansätze für den Umgang mit Fertigungsrestriktionen in der Herstellung von Blechprofilen Adviser: Alexander Martin, Ute Günther
• An overview of algorithms for graph reliability and possible transfer dynamic graph reliability Advisor: Alexander Martin, Nicole Ziems
• Analyzing and modeling of selected parameters of the facade construction of a building with respect to the sustainability and efficiency of the building Adviser: Alexander Martin
• Preprocessingtechniken in der Gasnetzwerkoptimierung Adviser: Alexander Martin, Björn Geißler
• Optimierungsmethoden für die Kopplung von Day-Ahead-Strommärkten Adviser: Alexander Martin, Antonio Morsi, Björn Geißler
• Ein pfadbasiertes Modell für das Routing von Güterwagen im Einzelwagenverkehr Adviser: Alexander Martin, Henning Homfeld
• Azyklische Fahrzeugeinsatz- und Instandhaltungsoptimierung im Schienenpersonennahverkehr Adviser: Alexander Martin, Henning Homfeld
• Ein Arboreszenzmodell für das Leitwegproblem Adviser: Alexander Martin, Henning Homfeld
• Approximation einer Hyperbel in der diskreten Optimierung Adviser: Alexander Martin, Henning Homfeld
• Dynamische Programmierung in der Gasnetzwerkoptimierung Adviser: Alexander Martin, Susanne Moritz, Björn Geißler, Antonio Morsi
• Lösungsmethoden für das Pin Assignment Problem Adviser: Alexander Martin, Antonio Morsi, Björn Geißler
• Exploiting Heuristics for the Vehicle Routing Problem to estimate Gas Delivery Costs Adviser: Alexander Martin
• Ganzzahlige Optimierung zur Bestimmung konsistenter Eröffnungspreise von Futures-Kontrakten und ihrer Kombinationen Advisor: Alexander Martin
• Verfahren zur Lösung des soft rectangle packing problem Adviser: Alexander Martin, Armin Fügenschuh
• Optimierung der Leitwegeplanung im Schienengüterverkehr Adviser: Alexander Martin, Armin Fügenschuh, Henning Homfeld
• Optimierungsmethoden zur Berechnung von Cross-Border-Flow beim Market-Coupling im europäischen Stromhandel Adviser: Alexander Martin, Antonio Morsi, Björn Geißler
• Gemischt-ganzzahliges Modell zur Entwicklung optimaler Erneuerungsstrategien für Wasserversorgungsnetze Adviser: Alexander Martin, Antonio Morsi
• Algorithmische Behandlung des All-Different Constraints im Branch&Cut Adviser: Alexander Martin, Thorsten Gellermann
• Empiric Analysis of Convex Underestimators in Mixed Integer Nonlinear Optimization Adviser: Alexander Martin, Thorsten Gellermann
• Partial Reverse Search Adviser: Alexander Martin, Lars Schewe
• Zufallsbasierte Heuristik für gekoppelte Netzwerke in der dezentralen Energieversorgung Adviser: Alexander Martin, Debora Mahlke, Andrea Zelmer
• Polyedrische Untersuchungen an einem stochastischen Optimierungsproblem aus der regenerativen Energieversorgung Adviser: Alexander Martin, Debora Mahlke, Andrea Zelmer
• Relax & Fix Heuristik für ein stochastisches Problem aus der regenerativen Energieversorgung Adviser: Alexander Martin, Debora Mahlke, Andrea Zelmer
• Test Sets for Spanning Tree Problems with Side Constraints Adviser: Alexander Martin, Ute Günther
• Polyedrische Untersuchungen zur Kostenoptimierung der Geldautomatenbefüllung Adviser: Alexander Martin, Ute Günther
• Effiziente stückweise lineare Approximation bivariater Funktionen Adviser: Ulrich Reif, Armin Fügenschuh, Andrea Peter
• The 3-Steiner Ratio in Octilinear Geometry Adviser: Karsten Weihe, Alexander Martin
• An empirical investigation of local search algorithms to minimize the weighted number of tardy jobs in Single Machine Scheduling Adviser: T. Stützle, Alexander Martin
• Optimization of Collateralization concerning Large Exposures Adviser: S. Dewal, Alexander Martin
• Optimierungsmodelle zur Linienbündelung im ÖPNV Adviser: Alexander Martin, Armin Fügenschuh
• Solving dynamic Scheduling Problems with Unary Resources Adviser: Alexander Martin
• Parameteranalyse in der Optimierungssoftware Carmen-PAC Adviser: Armin Fügenschuh, Alexander Martin
• Ein Data Mining Ansatz zur Abschätzung von zyklischen Werkstoffkennwerten Adviser: Armin Fügenschuh
• Automatische Parameteroptimierung im Crew Assignment System Carmen Adviser: Armin Fügenschuh, Alexander Martin
• Branch and Price-Verfahren für Losgrößenprobleme Adviser: Wolfgang Domschke, Alexander Martin
• Pin Assignment im Multilayer Chip Design Adviser: Alexander Martin, Karsten Weihe
• Augmentierende Vektoren mit beschränktem Support Adviser: Alexander Martin, Karsten Weihe
• An LP-based Rounding Approach to Coupled Supply Network Planning Adviser: Alexander Martin, Debora Mahlke, Andrea Zelmer
• Bounded Diameter Minimum Spanning Tree Adviser: Alexander Martin, Ute Günther
• Degree and Diameter Bounded Minimum Spanning Trees Adviser: Alexander Martin, Ute Günther
• Ein MILP, ein MINLP und ein graphentheoretischer Ansatz für die Free-Flight Optimierung Adviser: Alexander Martin, Armin Fügenschuh
• Vehicle Routing for Mobile Nurses  Adviser: Alexander Martin, Armin Fügenschuh
• Linearization Methods for the Optimization of Screening Processes in the Recovered Paper Production Adviser: Mirjam Duer, Armin Fügenschuh
• Solving Real-World Vehicle Routing Problems using MILP and PGreedy Heuristics Adviser: Alexander Martin, Armin Fügenschuh
• Supporting Geo-based Routing in Pub/Sub Middleware Adviser: A. Buchmann, Alexander Martin
• Towards Adaptive Optimization of Advice Dispatch Adviser: Alexander Martin, M. Mezini
• Optimierung von Lokumläufen in Schienengüterverkehr Adviser: Alexander Martin, Armin Fügenschuh
• An Approximation Algorithm for Edge-Coloring of Multigraphs Adviser: Alexander Martin, Daniel Junglas
• Modelling nonlinear stock holding costs in a facility location problem airsing in supply network optimisation  Adviser: Alexander Martin, Björn Samuelsson
• Selected General Purpose Heuristics for Solving Mixed Integer Programs  Adviser: Alexander Martin, Marzena Fügenschuh
• Ein Genetischer Algorithmus für das Proteinfaltungsproblem im HP-Modell Adviser: Alexander Martin
• Algorithmic Approaches for Two Fundamental Optimization Problems: Workload-Balancing And Planar Steiner Trees Adviser: Alexander Martin, Matthias Müller-Hannemann
• Stundenplan-Optimierung: Modelle und Software Adviser: Alexander Martin, Armin Fügenschuh
• Vehicle Routing: Modelle und Software Adviser: Alexander Martin, Armin Fügenschuh
• Parametrized GRASP Heuristics for Combinatorial Optimization Problems  Adviser: Alexander Martin, Armin Fügenschuh
• Optimal Unrolling of Integral Branched Sheet Metal Components Adviser: Alexander Martin, Daniel Junglas
• Leerwagenoptimierung im Schienengüterverkehr Adviser: Alexander Martin, Armin Fügenschuh, Gerald Pfau, DB AG
• Mathematische Modelle und Methoden in der Entscheidungsfindung im Supply Chain Management  Adviser: Alexander Martin, Simone Göttlich
• Modifikation des Approximationsalgorithmus von Hart und Istrail für das Proteinfaltungsproblem im HP-Modell Adviser: Alexander Martin, Agnes Dittel
• Ein Verbesserungsalgorithmus der Proteinfaltung mit dem HP-Modell von Ken Dill Adviser: Alexander Martin, Agnes Dittel
• Entwurf und Evaluation von MILP-Modellierungen zur Optimierung einer synchronisierten Abfüll- und Verpackungsstufe in der Produktionsfeinplanung  Adviser: Alexander Martin, Heinrich Braun, SAP AG, Thomas Kasper, SAP AG
• Integration von Strafkosten für zu niedrige Sicherheitsbestände bei Losgrößenmodellen  Adviser: Hartmut Stadtler, Institut für Betriebswirtschaftslehre, Christian Seipl, Institut für Betriebswirtschaftslehre, Alexander Martin
• Vergleich von Algorithmen zur Lösung ganzzahliger linearer Ungleichungssysteme mit höchstens zwei Variablen pro Ungleichung  Adviser: Alexander Martin, Armin Fügenschuh
• Schaltbedingungen bei der Optimierung von Gasnetzen: Polyedrische Untersuchungen und Schnittebenen  Adviser: Alexander Martin, Susanne Moritz
• Der Simulated Annealing Algorithmus zur transienten Optimierung von Gasnetzen  Adviser: Alexander Martin, Susanne Moritz
• Kantenfärbung in Multigraphen Adviser: Alexander Martin, Daniel Junglas
• Didaktische Aspekte einer Einbeziehung von Geschichte in den Mathematikunterricht am Beispiel von Kartographie Adviser: Alexander Martin
• Vehicle Routing Adviser: Alexander Martin, Armin Fügenschuh
• Ein genetischer Algorithmus zur Lösung eines multiplen Traveling Salesman Problems mit gekoppelten Zeitfenstern Adviser: Alexander Martin, Armin Fügenschuh
• Integrierte Optimierung von Schulanfangszeiten und des Nahverkehrsangebots – ein Constraint-Programming Ansatz im Vergleich zu Ganzzahliger Optimierung Adviser: Alexander Martin, Armin Fügenschuh
• Heuristic methods for site selection, installation selection and mobile assignment in UMTS  Adviser: Alexander Martin, Armin Fügenschuh
• Optimization of the School Bus Traffic in Rural Areas – Modeling and Solving a Distance Constrained, Capacitated Vehicle Routing Problem with Pickup and Delivery, Flexible Time Windows and Several Time Constraints  Adviser: Alexander Martin, Armin Fügenschuh
• Augmentierungsverfahren für Standortplanungsprobleme  Adviser: Alexander Martin, Armin Fügenschuh
• Chain-3 Constraints for an IP Model of Ken A. Dill’s HP Lattice model  Adviser: Alexander Martin, Armin Fügenschuh
• Stundenplangenerierung an einer Grundschule Adviser: Alexander Martin, Armin Fügenschuh, Agnes Dittel
• Vergleichende Untersuchung von Heuristiken für das Routing- und Wellenlängenzuordnungsproblem bei rein transparenten oprischen Telekommunikationsnetzen.  Adviser: Alexander Martin, Manfred Körkel
• Short Chain Constraints for an IP Model of Ken A. Dill’s HP-Lattice Model Adviser: Alexander Martin, Armin Fügenschuh
• Eine Rundeheuristik für Ganzzahlige Programme Adviser: Alexander Martin, Armin Fügenschuh
• Anwendung von Neuronalen netzen zur Beschleunigung von Branchen & Bound – Verfahren der Kombinatorischen Optimierung Adviser: M. Grötschel, Alexander Martin, K. Obermayer

Master’s Program in Data Science, Statistics and Decision Analysis

• 120 credits cr.
• Gå till denna sida på svenska webben

The programme focuses on how to draw smart conclusions from large amounts of data with the purpose of making well-informed decisions.

In today's society, massive amounts of data are generated at high speed. The data is also characterised by high variety and is becoming increasingly complex. We are constantly connected with computers and smart phones, while being surrounded by cameras and sensors that monitor and measure our lives constantly.

Automation and digitisation are becoming increasingly important in a large number of areas and industries, and most companies and authorities store large amounts of data about their customers, users and processes. For example, the analysis of large amounts of medical data is becoming increasingly important in healthcare.

The programme consists of courses in the following three subfields: data science, statistics and decision analysis. The program is a collaboration with the Department of Statistics.

Important about selection The selection is made from the following three criteria:

• Grades of academic courses,
• mandatory motivation letter and
• the relevance of previous studies in relation to the programme.

It is therefore very important to submit a motivation letter . Find instructions for the motivation letter under “How to apply” below.

Information for admitted students autumn 2024

Congratulations! You have been admitted at Stockholm University and we hope that you will enjoy your studies with us.

In order to ensure that your studies begin as smoothly as possible we have compiled a short checklist for the beginning of the semester.

Follow the instructions on whether you have to reply to your offer or not. universityadmissions.se

Checklist for admitted students

Activate your university account

The first step in being able to register and gain access to all the university's IT services.

Register at your department

Registration can be done in different ways. Read the instructions from your department below.

Read all the information on this page

Here you will find what you need to know before your course or programme starts.

Your seat may be withdrawn if you do not register according to the instructions provided by your department.

Information from the department - programmes

Welcome to DSV!

We hope that you will enjoy your studies with us. Follow the link below for information about how to start your studies at DSV and how and when to enrol.

New student at DSV

Welcome activities

Stockholm University organises a series of welcome activities that stretch over a few weeks at the beginning of each semester. The programme is voluntary (attendance is optional) and includes Arrival Service at the airport and an Orientation Day, see more details about these events below. Your department may also organise activities for welcoming international students. More information will be provided by your specific department. 

su.se/welcomeactivities  

Find your way on campus

Stockholm University's main campus is in the Frescati area, north of the city centre. While most of our departments and offices are located here, there are also campus areas in other parts of the city.

New student

During your studies

Student unions

For new international students

Pre-departure information

New in Sweden

Programme overview

Courses of 30 credits are given within each subfield. Courses from the three subfields alternate so that at least one course from each subfield is given each semester, for the first three semesters. The program ends with a master thesis in the fourth semester.

Areas within data science include:

• basic methods and algorithms in data analysis and data mining,
• advanced methods and algorithms in machine learning and deep learning,
• reinforcement learning and optimization,
• ethical aspects of data science with a focus on explainable models and
• programming and implementation of various algorithms with a focus on their application to various domains.

Areas within statistics include:

• introduction to data analysis, descriptive statistics, collection and handling of data,
• the process of statistical analysis i.e., statistical modelling and inference,
• Bayesian inference,
• forecasting and decision making under uncertainty,
• relationship between variables and how they can be used for prediction and
• statistical programming in R.

Areas within decision analysis include:

• formal methods for handling bases for decisions in a structured way and with respect to uncertainty, finding decision alternatives and comparing the consequences of the decision alternatives even when there are several criteria and stakeholders and
• risk analysis where possible negative consequences are identified and analysed within a business or organisation.

All courses are either within the main field of computer and systems science (DSV) or statistics (STAT).

1st semester Foundations of Data Science 7,5 credits (DSV) Decision Analysis I 7,5 credits (DSV) Statistics and data analysis for computer and systems sciences 15 credits  (STAT)

2nd semester Data mining 7,5 credits (DSV) Decision Analysis II 7,5 credits  (DSV) Statistical theory and modelling 7,5 credits  (STAT) Machine learning 7,5 credits (DSV)

3rd semester Risk Analysis 7,5 credits  (DSV) Bayesian learning 7,5 credits (STAT) Reinforcement learning 7,5 credits (DSV) Business Analytics 7,5 credits (DSV)

4th semester Master thesis 30 credits

How to apply

Required supporting documentation.

Motivation letter The letter shall include:

• Tell us something about yourself. Who are you?
• Motivate why you want to study this programme.
• Describe how you fulfil the entry requirements of 7,5 credits in programming. How do I fulfil the specific requirements in programming? Find description below.

Maximum one A4 page. Save the letter as “Motivation letter SDSBO”. Submit the letter together with your application at universityadmissions.se

How do I fulfil the specific requirements in programming? Any programming language course (Python, C, C++, Java, JavaScript etc.) is acceptable as long as it includes hands-on programming (writing actual code). Any programming language type is acceptable (object-oriented, procedural, logic-based etc.) provided that the course content includes at least the basic knowledge to understand and use the language in practice.

Please note again that the course needs to involve code implementation so courses which only include mark-up languages (i.e. HTML, XML), style sheet languages (i.e. CSS) are not counted in.

Note: If you wish to include independent courses, remember that they need to be offered by an accredited University. Courses offered by online learning platforms (Coursera, Udemy etc.) are not counted in.

More information

Admission round.

This program starts each autumn semester. 

Please note that it is only possible to apply for this programme in the first admission round (mid-October to mid-January). The programme is not open for admission in the second admission round.

Find answers to the most common questions regarding application and requirements. FAQ Master's programmes

Find the degree awarded for this programme in the syllabus, either in the right sidebar (desktop) or below (mobile device).

Please note, that you can only be awarded one bachelor’s degree, one master’s degree (60 credits) and one master’s degree (120 credits) in computer and systems sciences from our department.

Research subjects at the participating departments with relevance to the program:

AI and Data Science Bayesian Inference Risk and Decision Analysis Time series analysis

Career opportunities

This program will provide opportunities in a variety of fields. For example, your future career could be as a:

• Data Scientist: Apply your analytical skills to extract valuable insights from large datasets and develop machine learning models to solve complex problems.
• Data Analyst: Use statistical techniques to interpret data, generate reports on your results and findings, and provide actionable recommendations to support business decisions.
• Machine Learning Engineer: Design, implement, and optimize machine learning algorithms and models to enable automated decision-making and business analytics.
• Research Scientist: Conduct advanced research in data science, statistics, and decision analysis, contributing to academic studies or pushing the boundaries of industry knowledge.
• Data Consultant: Provide expert guidance to clients on data-driven strategies, assist in implementing data-driven solutions, and drive business growth through analytics.

[email protected]

Know what you want to study?

What can I study?

Selected reading

Discover Stockholm and Sweden

As a student at Stockholm University you get the benefit of living in the Swedish capital.

Step-by-step guide

Here we explain all the different steps you need to go through when applying to a course or programme. Please read the instructions carefully.

Meet us online and around the world

Stockholm University arranges regular webinars and participates in educational fairs and events virtually and around the world to meet students and inform them about our study programmes. Meet us and ask what it's like to study with us!

Students of Stockholm University

Every year Stockholm University welcomes thousands of international students from all over the world. Each bringing their own backpack full of experiences, expectations and dreams. Each with their own story.

Our researchers. Your teachers

As a student at Stockholm University, you will have direct contact with leading researchers in your field and access to the most recent scientific findings. Our researchers. Your teachers. Meet a few of them here.

Hear from our alumni

Are you wondering what former students thought about your programme? Are you curious about what your education can lead to after graduation? Hear from our alumni!

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