phd in data analysis

NYU Center for Data Science

Harnessing Data’s Potential for the World

PhD in Data Science

An NRT-sponsored program in Data Science

• Areas & Faculty
• Admission Requirements
• Medical School Track
• NRT FUTURE Program

Advances in computational speed and data availability, and the development of novel data analysis methods, have birthed a new field: data science. This new field requires a new type of researcher and actor: the rigorously trained, cross-disciplinary, and ethically responsible data scientist. Launched in Fall 2017, the pioneering CDS PhD Data Science program seeks to produce such researchers who are fluent in the emerging field of data science, and to develop a native environment for their education and training. The CDS PhD Data Science program has rapidly received widespread recognition and is considered among the top and most selective data science doctoral programs in the world. It has recently been recognized by the NSF through an NRT training grant.

The CDS PhD program model rigorously trains data scientists of the future who (1) develop methodology and harness statistical tools to find answers to questions that transcend the boundaries of traditional academic disciplines; (2) clearly communicate to extract crisp questions from big, heterogeneous, uncertain data; (3) effectively translate fundamental research insights into data science practice in the sciences, medicine, industry, and government; and (4) are aware of the ethical implications of their work.

Our programmatic mission is to nurture this new generation of data scientists, by designing and building a data science environment where methodological innovations are developed and translated successfully to domain applications, both scientific and social. Our vision is that combining fundamental research on the principles of data science with translational projects involving domain experts creates a virtuous cycle: Advances in data science methodology transform the process of discovery in the sciences, and enable effective data-driven governance in the public sector. At the same time, the demands of real-world translational projects will catalyze the creation of new data science methodologies. An essential ingredient of such methodologies is that they embed ethics and responsibility by design.

These objectives will be achieved by a combination of an innovative core curriculum, a novel data assistantship mechanism that provides training of skills transfer through rotations and internships, and communication and entrepreneurship modules. Students will be exposed to a wider range of fields than in more standard PhD programs while working with our interdisciplinary faculty. In particular, we are proud to offer a medical track for students eager to explore data science as applied to healthcare or to develop novel theoretical models stemming from medical questions.

In short, the CDS PhD Data Science program prepares students to become leaders in data science research and prepares them for outstanding careers in academia or industry. Successful candidates are guaranteed financial support in the form of tuition and a competitive stipend in the fall and spring semesters for up to five years.* We invite you to learn more through our webpage or by contacting  [email protected] .

*The Ph.D. program also offers students the opportunity to pursue their study and research with Data Science faculty based at NYU Shanghai. With this opportunity, students generally complete their coursework in New York City before moving full-time to Shanghai for their research. For more information, please visit the NYU Shanghai Ph.D. page .

DiscoverDataScience.org

PhD in Data Science – Your Guide to Choosing a Doctorate Degree Program

Created by aasif.faizal

Professional opportunities in data science are growing incredibly fast. That’s great news for students looking to pursue a career as a data scientist. But it also means that there are a lot more options out there to investigate and understand before developing the best educational path for you.

A PhD is the most advanced data science degree you can get, reflecting a depth of knowledge and technical expertise that will put you at the top of your field.

This means that PhD programs are the most time-intensive degree option out there, typically requiring that students complete dissertations involving rigorous research. This means that PhDs are not for everyone. Indeed, many who work in the world of big data hold master’s degrees rather than PhDs, which tend to involve the same coursework as PhD programs without a dissertation component. However, for the right candidate, a PhD program is the perfect choice to become a true expert on your area of focus.

If you’ve concluded that a data science PhD is the right path for you, this guide is intended to help you choose the best program to suit your needs. It will walk through some of the key considerations while picking graduate data science programs and some of the nuts and bolts (like course load and tuition costs) that are part of the data science PhD decision-making process.

Data Science PhD vs. Masters: Choosing the right option for you

If you’re considering pursuing a data science PhD, it’s worth knowing that such an advanced degree isn’t strictly necessary in order to get good work opportunities. Many who work in the field of big data only hold master’s degrees, which is the level of education expected to be a competitive candidate for data science positions.

So why pursue a data science PhD?

Simply put, a PhD in data science will leave you qualified to enter the big data industry at a high level from the outset.

You’ll be eligible for advanced positions within companies, holding greater responsibilities, keeping more direct communication with leadership, and having more influence on important data-driven decisions. You’re also likely to receive greater compensation to match your rank.

However, PhDs are not for everyone. Dissertations require a great deal of time and an interest in intensive research. If you are eager to jumpstart a career quickly, a master’s program will give you the preparation you need to hit the ground running. PhDs are appropriate for those who want to commit their time and effort to schooling as a long-term investment in their professional trajectory.

For more information on the difference between data science PhD’s and master’s programs, take a look at our guide here.

Topics include:

• Can I get an Online Ph.D in Data Science?
• Overview of Ph.d Coursework

Preparing for a Doctorate Program

Building a solid track record of professional experience, things to consider when choosing a school.

• What Does it Cost to Get a Ph.D in Data Science?
• School Listings

Data Science PhD Programs, Historically

Historically, data science PhD programs were one of the main avenues to get a good data-related position in academia or industry. But, PhD programs are heavily research oriented and require a somewhat long term investment of time, money, and energy to obtain. The issue that some data science PhD holders are reporting, especially in industry settings, is that that the state of the art is moving so quickly, and that the data science industry is evolving so rapidly, that an abundance of research oriented expertise is not always what’s heavily sought after.

Instead, many companies are looking for candidates who are up to date with the latest data science techniques and technologies, and are willing to pivot to match emerging trends and practices.

One recent development that is making the data science graduate school decisions more complex is the introduction of specialty master’s degrees, that focus on rigorous but compact, professional training. Both students and companies are realizing the value of an intensive, more industry-focused degree that can provide sufficient enough training to manage complex projects and that are more client oriented, opposed to research oriented.

However, not all prospective data science PhD students are looking for jobs in industry. There are some pretty amazing research opportunities opening up across a variety of academic fields that are making use of new data collection and analysis tools. Experts that understand how to leverage data systems including statistics and computer science to analyze trends and build models will be in high demand.

Can You Get a PhD in Data Science Online?

While it is not common to get a data science Ph.D. online, there are currently two options for those looking to take advantage of the flexibility of an online program.

Indiana University Bloomington and Northcentral University both offer online Ph.D. programs with either a minor or specialization in data science.

Given the trend for schools to continue increasing online offerings, expect to see additional schools adding this option in the near future.

Overview of PhD Coursework

A PhD requires a lot of academic work, which generally requires between four and five years (sometimes longer) to complete.

Here are some of the high level factors to consider and evaluate when comparing data science graduate programs.

How many credits are required for a PhD in data science?

On average, it takes 71 credits to graduate with a PhD in data science — far longer (almost double) than traditional master’s degree programs. In addition to coursework, most PhD students also have research and teaching responsibilities that can be simultaneously demanding and really great career preparation.

What’s the core curriculum like?

In a data science doctoral program, you’ll be expected to learn many skills and also how to apply them across domains and disciplines. Core curriculums will vary from program to program, but almost all will have a core foundation of statistics.

All PhD candidates will have to take a qualifying exam. This can vary from university to university, but to give you some insight, it is broken up into three phases at Yale. They have a practical exam, a theory exam and an oral exam. The goal is to make sure doctoral students are developing the appropriate level of expertise.

Dissertation

One of the final steps of a PhD program involves presenting original research findings in a formal document called a dissertation. These will provide background and context, as well as findings and analysis, and can contribute to the understanding and evolution of data science. A dissertation idea most often provides the framework for how a PhD candidate’s graduate school experience will unfold, so it’s important to be thoughtful and deliberate while considering research opportunities.

Since data science is such a rapidly evolving field and because choosing the right PhD program is such an important factor in developing a successful career path, there are some steps that prospective doctoral students can take in advance to find the best-fitting opportunity.

Join professional associations

Even before being fully credentials, joining professional associations and organizations such as the Data Science Association and the American Association of Big Data Professionals is a good way to get exposure to the field. Many professional societies are welcoming to new members and even encourage student participation with things like discounted membership fees and awards and contest categories for student researchers. One of the biggest advantages to joining is that these professional associations bring together other data scientists for conference events, research-sharing opportunities, networking and continuing education opportunities.

Leverage your social network

Be on the lookout to make professional connections with professors, peers, and members of industry. There are a number of LinkedIn groups dedicated to data science. A well-maintained professional network is always useful to have when looking for advice or letters of recommendation while applying to graduate school and then later while applying for jobs and other career-related opportunities.

Kaggle competitions

Kaggle competitions provide the opportunity to solve real-world data science problems and win prizes. A list of data science problems can be found at Kaggle.com . Winning one of these competitions is a good way to demonstrate professional interest and experience.

Internships

Internships are a great way to get real-world experience in data science while also getting to work for top names in the world of business. For example, IBM offers a data science internship which would also help to stand out when applying for PhD programs, as well as in seeking employment in the future.

Demonstrating professional experience is not only important when looking for jobs, but it can also help while applying for graduate school. There are a number of ways for prospective students to gain exposure to the field and explore different facets of data science careers.

Get certified

There are a number of data-related certificate programs that are open to people with a variety of academic and professional experience. DeZyre has an excellent guide to different certifications, some of which might help provide good background for graduate school applications.

Conferences

Conferences are a great place to meet people presenting new and exciting research in the data science field and bounce ideas off of newfound connections. Like professional societies and organizations, discounted student rates are available to encourage student participation. In addition, some conferences will waive fees if you are presenting a poster or research at the conference, which is an extra incentive to present.

It can be hard to quantify what makes a good-fit when it comes to data science graduate school programs. There are easy to evaluate factors, such as cost and location, and then there are harder to evaluate criteria such as networking opportunities, accessibility to professors, and the up-to-dateness of the program’s curriculum.

Nevertheless, there are some key relevant considerations when applying to almost any data science graduate program.

What most schools will require when applying:

• All undergraduate and graduate transcripts
• A statement of intent for the program (reason for applying and future plans)
• Letters of reference
• Application fee
• Online application
• A curriculum vitae (outlining all of your academic and professional accomplishments)

What Does it Cost to Get a PhD in Data Science?

The great news is that many PhD data science programs are supported by fellowships and stipends. Some are completely funded, meaning the school will pay tuition and basic living expenses. Here are several examples of fully funded programs:

• University of Southern California
• University of Nevada, Reno
• Kennesaw State University
• Worcester Polytechnic Institute
• University of Maryland

For all other programs, the average range of tuition, depending on the school can range anywhere from $1,300 per credit hour to $2,000 amount per credit hour. Remember, typical PhD programs in data science are between 60 and 75 credit hours, meaning you could spend up to $150,000 over several years.

That’s why the financial aspects are so important to evaluate when assessing PhD programs, because some schools offer full stipends so that you are able to attend without having to find supplemental scholarships or tuition assistance.

Can I become a professor of data science with a PhD.? Yes! If you are interested in teaching at the college or graduate level, a PhD is the degree needed to establish the full expertise expected to be a professor. Some data scientists who hold PhDs start by entering the field of big data and pivot over to teaching after gaining a significant amount of work experience. If you’re driven to teach others or to pursue advanced research in data science, a PhD is the right degree for you.

Do I need a master’s in order to pursue a PhD.? No. Many who pursue PhDs in Data Science do not already hold advanced degrees, and many PhD programs include all the coursework of a master’s program in the first two years of school. For many students, this is the most time-effective option, allowing you to complete your education in a single pass rather than interrupting your studies after your master’s program.

Can I choose to pursue a PhD after already receiving my master’s? Yes. A master’s program can be an opportunity to get the lay of the land and determine the specific career path you’d like to forge in the world of big data. Some schools may allow you to simply extend your academic timeline after receiving your master’s degree, and it is also possible to return to school to receive a PhD if you have been working in the field for some time.

If a PhD. isn’t necessary, is it a waste of time? While not all students are candidates for PhDs, for the right students – who are keen on doing in-depth research, have the time to devote to many years of school, and potentially have an interest in continuing to work in academia – a PhD is a great choice. For more information on this question, take a look at our article Is a Data Science PhD. Worth It?

Complete List of Data Science PhD Programs

Below you will find the most comprehensive list of schools offering a doctorate in data science. Each school listing contains a link to the program specific page, GRE or a master’s degree requirements, and a link to a page with detailed course information.

Note that the listing only contains true data science programs. Other similar programs are often lumped together on other sites, but we have chosen to list programs such as data analytics and business intelligence on a separate section of the website.

Boise State University  – Boise, Idaho PhD in Computing – Data Science Concentration

The Data Science emphasis focuses on the development of mathematical and statistical algorithms, software, and computing systems to extract knowledge or insights from data.  

In 60 credits, students complete an Introduction to Graduate Studies, 12 credits of core courses, 6 credits of data science elective courses, 10 credits of other elective courses, a Doctoral Comprehensive Examination worth 1 credit, and a 30-credit dissertation.

Electives can be taken in focus areas such as Anthropology, Biometry, Ecology/Evolution and Behavior, Econometrics, Electrical Engineering, Earth Dynamics and Informatics, Geoscience, Geostatistics, Hydrology and Hydrogeology, Materials Science, and Transportation Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $7,236 total (Resident), $24,573 total (Non-resident)

View Course Offerings

Bowling Green State University  – Bowling Green, Ohio Ph.D. in Data Science

Data Science students at Bowling Green intertwine knowledge of computer science with statistics.

Students learn techniques in analyzing structured, unstructured, and dynamic datasets.

Courses train students to understand the principles of analytic methods and articulating the strengths and limitations of analytical methods.

The program requires 60 credit hours in the studies of Computer Science (6 credit hours), Statistics (6 credit hours), Data Science Exploration and Communication, Ethical Issues, Advanced Data Mining, and Applied Data Science Experience.

Students must also complete 21 credit hours of elective courses, a qualifying exam, a preliminary exam, and a dissertation.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,418 (Resident), $14,410 (Non-resident)

Brown University  – Providence, Rhode Island PhD in Computer Science – Concentration in Data Science

Brown University’s database group is a world leader in systems-oriented database research; they seek PhD candidates with strong system-building skills who are interested in researching TupleWare, MLbase, MDCC, Crowd DB, or PIQL.

In order to gain entrance, applicants should consider first doing a research internship at Brown with this group. Other ways to boost an application are to take and do well at massive open online courses, do an internship at a large company, and get involved in a large open-source software project.

Coding well in C++ is preferred.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $62,680 total

Chapman University  – Irvine, California Doctorate in Computational and Data Sciences

Candidates for the doctorate in computational and data science at Chapman University begin by completing 13 core credits in basic methodologies and techniques of computational science.

Students complete 45 credits of electives, which are personalized to match the specific interests and research topics of the student.

Finally, students complete up to 12 credits in dissertation research.

Applicants must have completed courses in differential equations, data structures, and probability and statistics, or take specific foundation courses, before beginning coursework toward the PhD.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,538 per year

Clemson University / Medical University of South Carolina (MUSC) – Joint Program – Clemson, South Carolina & Charleston, South Carolina Doctor of Philosophy in Biomedical Data Science and Informatics – Clemson

The PhD in biomedical data science and informatics is a joint program co-authored by Clemson University and the Medical University of South Carolina (MUSC).

Students choose one of three tracks to pursue: precision medicine, population health, and clinical and translational informatics. Students complete 65-68 credit hours, and take courses in each of 5 areas: biomedical informatics foundations and applications; computing/math/statistics/engineering; population health, health systems, and policy; biomedical/medical domain; and lab rotations, seminars, and doctoral research.

Applicants must have a bachelor’s in health science, computing, mathematics, statistics, engineering, or a related field, and it is recommended to also have competency in a second of these areas.

Program requirements include a year of calculus and college biology, as well as experience in computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,858 total (South Carolina Resident), $22,566 total (Non-resident)

View Course Offerings – Clemson

George Mason University  – Fairfax, Virginia Doctor of Philosophy in Computational Sciences and Informatics – Emphasis in Data Science

George Mason’s PhD in computational sciences and informatics requires a minimum of 72 credit hours, though this can be reduced if a student has already completed a master’s. 48 credits are toward graduate coursework, and an additional 24 are for dissertation research.

Students choose an area of emphasis—either computer modeling and simulation or data science—and completed 18 credits of the coursework in this area. Students are expected to completed the coursework in 4-5 years.

Applicants to this program must have a bachelor’s degree in a natural science, mathematics, engineering, or computer science, and must have knowledge and experience with differential equations and computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $13,426 total (Virginia Resident), $35,377 total (Non-resident)

Harrisburg University of Science and Technology  – Harrisburg, Pennsylvania Doctor of Philosophy in Data Sciences

Harrisburg University’s PhD in data science is a 4-5 year program, the first 2 of which make up the Harrisburg master’s in analytics.

Beyond this, PhD candidates complete six milestones to obtain the degree, including 18 semester hours in doctoral-level courses, such as multivariate data analysis, graph theory, machine learning.

Following the completion of ANLY 760 Doctoral Research Seminar, students in the program complete their 12 hours of dissertation research bringing the total program hours to 36.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $14,940 total

Icahn School of Medicine at Mount Sinai  – New York, New York Genetics and Data Science, PhD

As part of the Biomedical Science PhD program, the Genetics and Data Science multidisciplinary training offers research opportunities that expand on genetic research and modern genomics. The training also integrates several disciplines of biomedical sciences with machine learning, network modeling, and big data analysis.

Students in the Genetics and Data Science program complete a predetermined course schedule with a total of 64 credits and 3 years of study.

Additional course requirements and electives include laboratory rotations, a thesis proposal exam and thesis defense, Computer Systems, Intro to Algorithms, Machine Learning for Biomedical Data Science, Translational Genomics, and Practical Analysis of a Personal Genome.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $31,303 total

Indiana University-Purdue University Indianapolis  – Indianapolis, Indiana PhD in Data Science PhD Minor in Applied Data Science

Doctoral candidates pursuing the PhD in data science at Indiana University-Purdue must display competency in research, data analytics, and at management and infrastructure to earn the degree.

The PhD is comprised of 24 credits of a data science core, 18 credits of methods courses, 18 credits of a specialization, written and oral qualifying exams, and 30 credits of dissertation research. All requirements must be completed within 7 years.

Applicants are generally expected to have a master’s in social science, health, data science, or computer science. 

Currently a majority of the PhD students at IUPUI are funded by faculty grants and two are funded by the federal government. None of the students are self funded.

IUPUI also offers a PhD Minor in Applied Data Science that is 12-18 credits. The minor is open to students enrolled at IUPUI or IU Bloomington in a doctoral program other than Data Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $9,228 per year (Indiana Resident), $25,368 per year (Non-resident)

Jackson State University – Jackson, Mississippi PhD Computational and Data-Enabled Science and Engineering

Jackson State University offers a PhD in computational and data-enabled science and engineering with 5 concentration areas: computational biology and bioinformatics, computational science and engineering, computational physical science, computation public health, and computational mathematics and social science.

Students complete 12 credits of common core courses, 12 credits in the specialization, 24 credits of electives, and 24 credits in dissertation research.

Students may complete the doctoral program in as little as 5 years and no more than 8 years.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,270 total

Kennesaw State University  – Kennesaw, Georgia PhD in Analytics and Data Science

Students pursuing a PhD in analytics and data science at Kennesaw State University must complete 78 credit hours: 48 course hours and 6 electives (spread over 4 years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship.

Prior to dissertation research, the comprehensive examination will cover material from the three areas of study: computer science, mathematics, and statistics.

Successful applicants will have a master’s degree in a computational field, calculus I and II, programming experience, modeling experience, and are encouraged to have a base SAS certification.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,328 total (Georgia Resident), $19,188 total (Non-resident)

New Jersey Institute of Technology  – Newark, New Jersey PhD in Business Data Science

Students may enter the PhD program in business data science at the New Jersey Institute of Technology with either a relevant bachelor’s or master’s degree. Students with bachelor’s degrees begin with 36 credits of advanced courses, and those with master’s take 18 credits before moving on to credits in dissertation research.

Core courses include business research methods, data mining and analysis, data management system design, statistical computing with SAS and R, and regression analysis.

Students take qualifying examinations at the end of years 1 and 2, and must defend their dissertations successfully by the end of year 6.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $21,932 total (New Jersey Resident), $32,426 total (Non-resident)

New York University  – New York, New York PhD in Data Science

Doctoral candidates in data science at New York University must complete 72 credit hours, pass a comprehensive and qualifying exam, and defend a dissertation with 10 years of entering the program.

Required courses include an introduction to data science, probability and statistics for data science, machine learning and computational statistics, big data, and inference and representation.

Applicants must have an undergraduate or master’s degree in fields such as mathematics, statistics, computer science, engineering, or other scientific disciplines. Experience with calculus, probability, statistics, and computer programming is also required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,332 per year

View Course Offering

Northcentral University  – San Diego, California PhD in Data Science-TIM

Northcentral University offers a PhD in technology and innovation management with a specialization in data science.

The program requires 60 credit hours, including 6-7 core courses, 3 in research, a PhD portfolio, and 4 dissertation courses.

The data science specialization requires 6 courses: data mining, knowledge management, quantitative methods for data analytics and business intelligence, data visualization, predicting the future, and big data integration.

Applicants must have a master’s already.

Delivery Method: Online GRE: Required 2022-2023 Tuition: $16,794 total

Stevens Institute of Technology – Hoboken, New Jersey Ph.D. in Data Science

Stevens Institute of Technology has developed a data science Ph.D. program geared to help graduates become innovators in the space.

The rigorous curriculum emphasizes mathematical and statistical modeling, machine learning, computational systems and data management.

The program is directed by Dr. Ted Stohr, a recognized thought leader in the information systems, operations and business process management arenas.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $39,408 per year

University at Buffalo – Buffalo, New York PhD Computational and Data-Enabled Science and Engineering

The curriculum for the University of Buffalo’s PhD in computational and data-enabled science and engineering centers around three areas: data science, applied mathematics and numerical methods, and high performance and data intensive computing. 9 credit course of courses must be completed in each of these three areas. Altogether, the program consists of 72 credit hours, and should be completed in 4-5 years. A master’s degree is required for admission; courses taken during the master’s may be able to count toward some of the core coursework requirements.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,310 per year (New York Resident), $23,100 per year (Non-resident)

University of Colorado Denver – Denver, Colorado PhD in Big Data Science and Engineering

The University of Colorado – Denver offers a unique program for those students who have already received admission to the computer science and information systems PhD program.

The Big Data Science and Engineering (BDSE) program is a PhD fellowship program that allows selected students to pursue research in the area of big data science and engineering. This new fellowship program was created to train more computer scientists in data science application fields such as health informatics, geosciences, precision and personalized medicine, business analytics, and smart cities and cybersecurity.

Students in the doctoral program must complete 30 credit hours of computer science classes beyond a master’s level, and 30 credit hours of dissertation research.

The BDSE fellowship requires students to have an advisor both in the core disciplines (either computer science or mathematics and statistics) as well as an advisor in the application discipline (medicine and public health, business, or geosciences).

In addition, the fellowship covers full stipend, tuition, and fees up to ~50k for BDSE fellows annually. Important eligibility requirements can be found here.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $55,260 total

University of Marylan d  – College Park, Maryland PhD in Information Studies

Data science is a potential research area for doctoral candidates in information studies at the University of Maryland – College Park. This includes big data, data analytics, and data mining.

Applicants for the PhD must have taken the following courses in undergraduate studies: programming languages, data structures, design and analysis of computer algorithms, calculus I and II, and linear algebra.

Students must complete 6 qualifying courses, 2 elective graduate courses, and at least 12 credit hours of dissertation research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $16,238 total (Maryland Resident), $35,388 total (Non-resident)

University of Massachusetts Boston  – Boston, Massachusetts PhD in Business Administration – Information Systems for Data Science Track

The University of Massachusetts – Boston offers a PhD in information systems for data science. As this is a business degree, students must complete coursework in their first two years with a focus on data for business; for example, taking courses such as business in context: markets, technologies, and societies.

Students must take and pass qualifying exams at the end of year 1, comprehensive exams at the end of year 2, and defend their theses at the end of year 4.

Those with a degree in statistics, economics, math, computer science, management sciences, information systems, and other related fields are especially encouraged, though a quantitative degree is not necessary.

Students accepted by the program are ordinarily offered full tuition credits and a stipend ($25,000 per year) to cover educational expenses and help defray living costs for up to three years of study.

During the first two years of coursework, they are assigned to a faculty member as a research assistant; for the third year students will be engaged in instructional activities. Funding for the fourth year is merit-based from a limited pool of program funds

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $18,894 total (in-state), $36,879 (out-of-state)

University of Nevada Reno – Reno, Nevada PhD in Statistics and Data Science

The University of Nevada – Reno’s doctoral program in statistics and data science is comprised of 72 credit hours to be completed over the course of 4-5 years. Coursework is all within the scope of statistics, with titles such as statistical theory, probability theory, linear models, multivariate analysis, statistical learning, statistical computing, time series analysis.

The completion of a Master’s degree in mathematics or statistics prior to enrollment in the doctoral program is strongly recommended, but not required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,814 total (in-state), $22,356 (out-of-state)

University of Southern California – Los Angles, California PhD in Data Sciences & Operations

USC Marshall School of Business offers a PhD in data sciences and operations to be completed in 5 years.

Students can choose either a track in operations management or in statistics. Both tracks require 4 courses in fall and spring of the first 2 years, as well as a research paper and courses during the summers. Year 3 is devoted to dissertation preparation and year 4 and/or 5 to dissertation defense.

A bachelor’s degree is necessary for application, but no field or further experience is required.

Students should complete 60 units of coursework. If the students are admitted with Advanced Standing (e.g., Master’s Degree in appropriate field), this requirement may be reduced to 40 credits.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $63,468 total

University of Tennessee-Knoxville  – Knoxville, Tennessee The Data Science and Engineering PhD

The data science and engineering PhD at the University of Tennessee – Knoxville requires 36 hours of coursework and 36 hours of dissertation research. For those entering with an MS degree, only 24 hours of course work is required.

The core curriculum includes work in statistics, machine learning, and scripting languages and is enhanced by 6 hours in courses that focus either on policy issues related to data, or technology entrepreneurship.

Students must also choose a knowledge specialization in one of these fields: health and biological sciences, advanced manufacturing, materials science, environmental and climate science, transportation science, national security, urban systems science, and advanced data science.

Applicants must have a bachelor’s or master’s degree in engineering or a scientific field. 

All students that are admitted will be supported by a research fellowship and tuition will be included.

Many students will perform research with scientists from Oak Ridge national lab, which is located about 30 minutes drive from campus.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,468 total (Tennessee Resident), $29,656 total (Non-resident)

University of Vermont – Burlington, Vermont Complex Systems and Data Science (CSDS), PhD

Through the College of Engineering and Mathematical Sciences, the Complex Systems and Data Science (CSDS) PhD program is pan-disciplinary and provides computational and theoretical training. Students may customize the program depending on their chosen area of focus.

Students in this program work in research groups across campus.

Core courses include Data Science, Principles of Complex Systems and Modeling Complex Systems. Elective courses include Machine Learning, Complex Networks, Evolutionary Computation, Human/Computer Interaction, and Data Mining.

The program requires at least 75 credits to graduate with approval by the student graduate studies committee.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $12,204 total (Vermont Resident), $30,960 total (Non-resident)

University of Washington Seattle Campus – Seattle, Washington PhD in Big Data and Data Science

The University of Washington’s PhD program in data science has 2 key goals: training of new data scientists and cyberinfrastructure development, i.e., development of open-source tools and services that scientists around the world can use for big data analysis.

Students must take core courses in data management, machine learning, data visualization, and statistics.

Students are also required to complete at least one internship that covers practical work in big data.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $17,004 per year (Washington resident), $30,477 (non-resident)

University of Wisconsin-Madison – Madison, Wisconsin PhD in Biomedical Data Science

The PhD program in Biomedical Data Science offered by the Department of Biostatistics and Medical Informatics at UW-Madison is unique, in blending the best of statistics and computer science, biostatistics and biomedical informatics. 

Students complete three year-long course sequences in biostatistics theory and methods, computer science/informatics, and a specialized sequence to fit their interests.

Students also complete three research rotations within their first two years in the program, to both expand their breadth of knowledge and assist in identifying a research advisor.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,728 total (in-state), $24,054 total (out-of-state)

Vanderbilt University – Nashville, Tennessee Data Science Track of the BMI PhD Program

The PhD in biomedical informatics at Vanderbilt has the option of a data science track.

Students complete courses in the areas of biomedical informatics (3 courses), computer science (4 courses), statistical methods (4 courses), and biomedical science (2 courses). Students are expected to complete core courses and defend their dissertations within 5 years of beginning the program.

Applicants must have a bachelor’s degree in computer science, engineering, biology, biochemistry, nursing, mathematics, statistics, physics, information management, or some other health-related field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $53,160 per year

Washington University in St. Louis – St. Louis, Missouri Doctorate in Computational & Data Sciences

Washington University now offers an interdisciplinary Ph.D. in Computational & Data Sciences where students can choose from one of four tracks (Computational Methodologies, Political Science, Psychological & Brain Sciences, or Social Work & Public Health).

Students are fully funded and will receive a stipend for at least five years contingent on making sufficient progress in the program.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $59,420 total

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

The PhD in data science at Worcester Polytechnic Institute focuses on 5 areas: integrative data science, business intelligence and case studies, data access and management, data analytics and mining, and mathematical analysis.

Students first complete a master’s in data science, and then complete 60 credit hours beyond the master’s, including 30 credit hours of research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $28,980 per year

Yale University – New Haven, Connecticut PhD Program – Department of Stats and Data Science

The PhD in statistics and data science at Yale University offers broad training in the areas of statistical theory, probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods. Students complete 12 courses in the first year in these topics.

Students are required to teach one course each semester of their third and fourth years.

Most students complete and defend their dissertations in their fifth year.

Applicants should have an educational background in statistics, with an undergraduate major in statistics, mathematics, computer science, or similar field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $46,900 total

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PhD in Data Analytics Programs

On This Page:

You’re an analytics professional with a talent for research. You’re considering a PhD in Data Analytics as the next logical step in your career, but you’d like to know more about the practicals. Explore different types of analytics doctorates . Dig into details on timelines , coursework , and the dissertation process . Learn about admissions requirements and funding options , including fully-funded doctorates. Find answers to questions about online degrees and employment avenues after graduation. Or skip ahead to our listings of all the PhD in Data Analytics programs in the country.

What Are PhD in Data Analytics Programs?

A PhD in Data Analytics or a closely related field is an interdisciplinary doctorate that focuses on cutting-edge research in the realms of advanced analytics, statistical computing, big data, and data science. Doctoral students in analytics:

• Push the boundaries of analytics in order to solve complex societal & organizational problems and transform decision-making
• Train to be expert practitioners in big data technologies, newly developed statistical methods, and “out of the box” analytical thinking
• Become analytics & data science professors at universities, senior analytics consultants in industry, and government advisors

Can You Earn a PhD in Data Analytics?

Yes. Doctoral programs in data analytics are available, but they are rare. The most popular title for a degree in the realm of data is the PhD in Data Science . Data science is a highly inventive field that builds on analytical foundations, so it makes sense to consider a doctoral program that focuses on innovation & self-guided discoveries.

When you do find a PhD with the word “analytics” in the title , you’re still going to be looking at a doctorate that intersects with the field of data science. Massive data sets, complicated analytics processes, sophisticated predictive models—doctoral students in analytics are schooled in all of these areas (and more).

Note: PhD programs are focused on original research and high-level thinking. If you want a workplace qualification, consider a Master’s in Data Analytics .

Types of Data Analytics Doctorate Programs

We’ve listed some common titles for doctorates in analytics, but we recommend you check the curriculum links in our listings and learn which department/s are offering the program. You should also look up the faculty’s research interests to see if they align with your own ideas for PhD projects. For example:

• If the degree is offered by the Department of Computer Science, a PhD in Data Analytics might be heavy on research into ethics, bias, AI, and building intelligent systems.
• If the degree is offered in partnership with the School of Business, a PhD in Data Analytics could be preoccupied with Machine Learning (ML), risk analysis, and econometrics.

The title of the PhD plays second fiddle to the department.

PhD in Analytics

A PhD in Analytics can often cut across multiple data-driven domains. Think of fields like Business Analytics, Data Science, Operations Research, and more. For instance, at the University of Notre Dame , doctoral students in analytics are able to access a large number of analytics research labs (e.g. gaming, human behavior, data & society, business, etc.) and collaborate with all kinds of partners.

PhD in Big Data Analytics

Doctorates in Big Data Analytics tend to focus on advanced systems & technologies that deal with processing big data (e.g. statistical computing, data mining, etc.), as well as their applications to real-world problems. Some universities, like the University of South Florida , are also interested in examining the human & social implications of analytics (e.g. ethical usage).

PhD in Analytics & Data Science

A PhD in Analytics and Data Science or a PhD in Data Science, Analytics & Engineering is a way for universities to combine data expertise from multiple departments. Yes, advanced analytics & big data processes will be addressed in the curriculum. But you’ll also find a strong emphasis on programming, algorithm creation, and systems development.

PhD in Data Science

Doctoral programs in data science may have more of a “design & develop” feel than analytics doctorates. In addition to exploring advanced analytics & big data applications, PhD in Data Science students are often interested in designing new information systems & tools (e.g. dashboards), creating their own algorithms & models, and exploring the boundaries of AI & Machine Learning (ML).

Note: Interested in industry & corporate analytics applications? Check out the guide to the PhD in Business Analytics .

How Doctorates in Data Analytics Work: Curriculum & Dissertation

Degree structure.

PhD programs in data analytics contain 6 key elements that take 4-5 years to complete on a full-time schedule. You will have to tackle each stage (e.g. core coursework) before you can proceed to the next one (e.g. qualifying exam).

Core Coursework

Qualifying/comprehensive exam, dissertation proposal, dissertation, dissertation defense.

• Year 1: Core coursework and first-year research papers. Assignment of a faculty mentor.
• Year 2: Core coursework, electives, second-year research papers, and the qualifying exam.
• Year 3: Any remaining coursework. Preparing research projects for publication. Dissertation proposal.
• Year 4: Dissertation work under the guidance of a dissertation advisor and advisory committee.
• Year 5: Dissertation work. Research papers & conference submissions. Dissertation defense.

Sample Curriculum

A PhD in Data Analytics or a closely related field will always contain a set of courses in advanced analytics & data science subjects. These courses can come from multiple departments (e.g. Computer Science, Mathematics & Statistics, Industrial Engineering, Psychology, etc.). Examples include:

• Big Data Analytics
• Data Mining
• Theoretical Statistics
• Statistical Computing
• Machine Learning
• Database Systems
• Information Assurance & Security

These are just a few sample course titles! Use the curriculum links in our listings to get a feel for each program’s unique flavor.

Once you’ve tackled the fundamentals of core coursework , you’ll usually be able to choose high-level electives in your particular research interests. For instance, the University of Central Florida offers electives in:

• Advanced computing (e.g. Parallel & Cloud Computation)
• Sophisticated analytics applications (e.g. Interactive Data Visualization)
• Industries (e.g. Industrial Engineering Analytics for Healthcare)

With some programs, you can customize your doctorate to a remarkable extent.

A qualifying exam is designed to test your knowledge of core coursework . It might take the form of a traditional exam, a paper and/or a project. For example, at the University of South Florida , PhD students are required to report on the results of a real-world, big data analytics project and include codes & systems that were developed in the process.

You’ll be required to develop an original idea for a research- or project-based dissertation and present your dissertation proposal to a dissertation advisory committee—experienced faculty members and (occasionally) outside experts who are interested in your area of work.

• A research-based dissertation will explore new realms of analytics research and potential applications.
• A project-based dissertation will involve work on a real-life project—this may be created at a research center or be suggested by an industry partner.

The dissertation proposal often takes the form of a written outline and an oral defense/presentation. If the committee accepts your proposal, you can get to work on your dissertation.

A PhD dissertation is a piece of original research that makes a significant contribution to the theory & practice of a field. In the world of data analytics & data science, dissertations can be research-based or project-based.

Dissertation Titles

Examples of real-life PhD in Data Analytics & Data Science dissertation titles include:

• A Credit Analysis of the Unbanked and Underbanked: An Argument for Alternative Data
• Novel Statistical and Machine Learning Methods for the Forecasting and Analysis of Major League Baseball Player Performance
• Optimal Analytical Methods for High Accuracy Cardiac Disease Classification and Treatment Based on ECG Data
• The Intelligent Management of Crowd-Powered Machine Learning
• Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models
• Classification with Large Sparse Datasets: Convergence Analysis and Scalable Algorithms

While you are writing up your dissertation, many universities will also expect you to be submitting related research papers to peer-reviewed journals & industry conferences.

The final step in the PhD process is the dissertation defense. You’ll be required to present your dissertation findings to your dissertation advisory committee and defend your research ideas in an oral & visual presentation. This will be followed by questions and a discussion.

It’s not as intimidating as it sounds. By this stage in your education, you will know your research inside-out and will have brainstormed many of the potential questions with your dissertation advisor. You can prepare for a defense by observing other student defenses, practicing with mock presentations, and reading up on the work of committee members.

PhD in Data Analytics: Admissions

Doctorate in data analytics: what it takes to get in.

Every PhD program in data analytics is going to have a unique set of admissions requirements! When you’re putting together a shortlist of doctorates, use the admissions links in our listings to save yourself time & trouble. You can decide if the program suits your level of expertise and education.

Doctoral programs in tech-driven disciplines—especially ones that are fully funded —are extremely competitive. You can stand out from the crowd by:

• Examining your entire application to see if you can make up for weaknesses (e.g. lower grades) with strengths (e.g. real-world projects)
• Matching your research interests to the university, department & research labs offering the program
• Collaborating with experienced analytics practitioners to co-author papers & publications
• Attending industry events and making connections that will help in your research
• Earning professional certificates to fill in any skills gaps

Degree Requirements

Your degree should be in a discipline that’s relevant to your area of research interest in the PhD. For a data analytics doctorate, that might mean a degree in statistics, data analytics, computer science, economics, or similar. The standard GPA requirement is 3.0 GPA or higher.

• Bachelor’s Degree Entry: Some doctoral programs in data analytics & data science are willing to consider applicants with a bachelor’s degree.
• Master’s Degree Entry:  Some doctoral programs are only looking for candidates with a master’s degree.

If you’re an undergraduate and you like the look of a PhD that only accepts master’s candidates, ask the program coordinator if you can earn an MS through the same university. Most doctoral programs have a “Master’s Along the Way” option.

Skills & Proficiencies

PhD candidates in analytics must be ready to tackle advanced coursework and high-level research. So universities will usually want to see evidence of proficiency/course credits in:

• Statistics, calculus & linear algebra
• Common analytical programming languages (e.g. R, Python, SAS, etc.)
• Analytics fundamentals (e.g. database management systems)

If you don’t have an undergraduate or master’s degree in analytics or a closely related field, universities will be poring over your transcripts & résumé to make sure you can handle any technical coursework.

General Requirements

In addition to your degree transcripts, almost all PhD programs in data analytics & data science fields will want to see:

• GRE or GMAT scores
• Letters of recommendation
• Statement of purpose
• TOEFL scores for non-English speaking international applicants

PhD in Data Analytics: Tuition & Funding

How to fund the phd.

Doctoral programs in data analytics & data science fall into 2 broad categories:

• Fully funded PhD programs
• Tuition-driven PhD programs

As you might expect, fully funded doctorate programs at strong universities are hard to get into!

Fully Funded PhD Programs

A number of STEM doctorates at research universities are fully funded. The university will waive all tuition costs and provide you with a living stipend as compensation for teaching & research activities. Many PhD students work as Teaching Assistants (TAs) and Research Assistant (RAs) during their doctoral studies.

Talk to the PhD program coordinator and check the fine print when you’re considering these programs.

• You may (or may not) qualify for on-campus housing and university health insurance.
• You may (or may not) qualify for conference stipends, overseas internships, and other perks.
• You may (or may not) be expected to pay for miscellaneous university fees.
• You may receive funding for Years 1-4 of your degree, but Year 5 support could be conditional on strong academic performance.

Tuition-Driven PhD Programs

You’ll also find doctoral programs in analytics & data science that do not offer any funding. They’ll expect you to pay for the degree out of your own pocket. At a private university, a PhD could cost upwards of $60,000-$80,000 in tuition alone.

So tread carefully! If you don’t qualify for fully funded PhD programs and you believe that a doctorate is  essential for your career goals, consider applying to a PhD program at a public university in your state—UCF’s in-state tuition for a PhD in Big Data Analytics is very reasonable.

You will also need to look into postgraduate loans, private scholarships & fellowships, employer reimbursement, and teaching & research job opportunities to offset your costs.

Online PhD in Data Analytics Programs

Can you earn an online phd in data analytics.

Yes—but we would caution against them. There are a few universities that offer online doctorates in data analytics, but they tend to be for-profit (e.g. Colorado Tech) or focused on executive-level training instead of research (e.g. DBA in Data Analytics from the University of the Southwest).

You’ll have a little more luck in finding online doctorates in data science, but they still won’t be offered by top-tier universities.

Why Are Online PhD Programs in Analytics Hard to Find?

Prestigious research universities & high-ranking schools are very cautious about maintaining their reputation for quality. They want doctoral students in data analytics & data science to:

• Attend classes in advanced topics, ask questions, and follow-up with faculty
• Have unfettered access to the university’s research centers, labs, and technical facilities
• Be able to teach undergraduates and conduct research in-person
• Meet with their dissertation advisor on a regular basis
• Network with visiting experts and fellow students

We agree with them. At this level, we highly recommend you choose an on-campus doctoral degree.

Career Prospects for PhD in Data Analytics Graduates

A PhD in Data Analytics or a closely related field is a super-specialized degree. You don’t need a doctorate to pursue a career in analytics & data science. Many senior-level practitioners simply have a degree like a Master’s in Data Analytics (or a similar title) and a lot of on-the-job experience.

However, a doctorate in analytics is an excellent choice for aspiring:

• University Professors: If you wish to teach analytics & data science at a college or university, you will probably need a research-focused doctorate. At the University of Notre Dame, 80% of its PhD in Analytics graduates go into academia.
• High-Level Researchers:  PhD graduates work in think tanks, industry research labs, and university research centers where exciting discoveries are taking place.
• Data Science & Analytics Consultants: You may wish to act in an advisory capacity for Wall Street, Silicon Valley, and other major centers of industry.
• Senior Research Positions: Some jobs in major tech companies, data-intensive businesses & financial companies (e.g. Senior Statistician) will require top-level research skills.

PhD Data Analytics FAQs

What should i look for in a data analytics doctoral program.

When you’re starting to put together a shortlist of doctoral programs, consider the following aspects:

• Funding Options: The best choice is going to be a fully funded PhD from a highly ranked & highly regarded university that includes teaching & research assistantships.
• Departmental Reputation: Which schools & departments are offering the degree? What kinds of unique benefits do they offer students? How much research funding do they receive?
• Faculty Expertise: Faculty profiles will be posted on the PhD program website. Read their bios, meet them for a virtual coffee, and learn more about their research & industry work. These people will become your advisors & mentors.
• Access to Resources: Will you have access to top-of-the-line analytics tools, commercial resources, and large-scale infrastructures? Can you work on projects within a major analytics research lab or center?
• Career Preparation: A strong PhD program will prepare you for the job market after graduation. Does the curriculum include opportunities for you to submit research papers to peer-reviewed journals? Does it offer stipends for conference travel? Does it bring in visiting experts for seminars?

What is a STEM Doctorate?

STEM stands for Science, Technology, Engineering & Mathematics. A STEM doctorate is any PhD—including the PhD in Data Analytics and the PhD in Data Science—that contains at least 50% of coursework in these fields.

• Are you an international student? Ask if the doctoral program has a “STEM designation” from the U.S. Department of Homeland Security (DHS). Students on an F-1 Visa can apply for Optional Practical Training (OPT) /temporary employment after graduation. Having a STEM-designated degree extends the OPT period from 12 months to 36 months.
• STEM programs often receive a fair amount of funding from the government and private industries. That means universities may be able to offer fully funded PhD programs to multiple students.

Is a PhD in Data Analytics Worth It?

Only if you have a specific career goal in mind. A PhD in Data Analytics or a closely related field is going to be time-consuming, challenging, and heavy on research. At least 4-5 years of your life will be devoted to earning it, so you and your family need to be prepared for the journey.

Unsure about your decision? Talk to analytics professionals who have already gone through the PhD gauntlet. You’ll find doctoral graduates on LinkedIn, at industry conferences , and within faculty directories on university websites. Be prepared to talk to them about your research interests and your goals.

All Phd in Data Analytics Programs

Arizona state university.

School of Computing and Augmented Intelligence

Tempe, Arizona

PhD in Data Science, Analytics, and Engineering

University of arizona.

Department of Biosystems Engineering

Tucson, Arizona

PhD in Biosystems Analytics & Technology

University of central florida.

College of Sciences

Orlando, Florida

University of South Florida-Main Campus

Muma College of Business

Tampa, Florida

Georgia State University

Robinson College of Business

Atlanta, Georgia

PhD in Business Administration & Digital Innovation - Data Science & Analytics

Kennesaw state university.

School of Data Science and Analytics

Kennesaw, Georgia

Doctor of Philosophy in Analytics and Data Science

University of notre dame.

Mendoza College of Business

Notre Dame, Indiana

University of Kansas

School of Business

Lawrence, Kansas

PhD in Analytics and Operations

Central michigan university.

College of Science and Engineering

Mount Pleasant, Michigan

PhD in Statistics and Analytics

North carolina, north carolina state university at raleigh.

Center for Geospatial Analytics

Raleigh, North Carolina

PhD in Geospatial Analytics

Pennsylvania, pennsylvania state university-main campus.

College of the Liberal Arts

University Park, Pennsylvania

PhD in Human Development and Family Studies and Social Data Analytics

Phd in informatics and social data analytics, phd in political science and social data analytics, phd in psychology and social data analytics, phd in social data analytics, phd in sociology and social data analytics, phd in statistics and social data analytics.

Doctor of Philosophy in Data Science

Developing future pioneers in data science

The School of Data Science at the University of Virginia is committed to educating the next generation of data science leaders. The Ph.D. in Data Science is designed to impart the skills and knowledge necessary to enable research and discovery in data science methods. Because the end goal is to extract knowledge and enable discovery from complex data, the program also boasts robust applied training that is geared toward interdisciplinary collaboration. Doctoral candidates will master the computational and mathematical foundations of data science, and develop competencies in data engineering, software development, data policy and ethics. 

Doctoral students in our program apprentice with faculty and pursue advanced research in an interdisciplinary, collaborative environment that is often focused on scientific discovery via data science methods. By serving as teaching assistants for the School’s undergraduate and graduate programs, they learn to be adroit educators and hone their critical thinking and communication skills.

LEARNING OUTCOMES

Pursuing a Ph.D. in Data Science will prepare you to become an expert in the field and work at the cutting edge of a new discipline. According to LinkedIn’s most recent Emerging Jobs Report, data science is booming and data scientist is one of the top three fastest growing jobs. A Ph.D. in Data Science from the University of Virginia opens career paths in academia, industry or government. Graduates of our program will:

• Understand data as a generic concept, and how data encodes and captures information
• Be fluent in modern data engineering techniques, and work with complex and large data sets
• Recognize ethical and legal issues relevant to data analytics and their impact on society 
• Develop innovative computational algorithms and novel statistical methods that transform data into knowledge
• Collaborate with research teams from a wide array of scientific fields 
• Effectively communicate methods and results to a variety of audiences and stakeholders
• Recognize the broad applicability of data science methods and models 

Graduates of the Ph.D. in Data Science will have contributed novel methodological research to the field of data science, demonstrated their work has impactful interdisciplinary applications and defended their methods in an open forum.

A Week in the Life: First-Year Ph.D. Student

Ph.D. Student Profile: Jade Preston

Ph.D. Student Profile: Beau LeBlond

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DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. Cross-disciplinary work is encouraged. The PhD program prepares students for careers as university teachers and researchers and as research statisticians or data scientists in industry, government and the non-profit sector.

Requirements

Students are required to fulfill the Department requirements in addition to those specified by The Graduate School (TGS).

From the Graduate School’s webpage outlining the general requirements for a PhD :

In order to receive a doctoral degree, students must:

• Complete all required coursework. .
• Gain admittance to candidacy.
• Submit a prospectus to be approved by a faculty committee.
• Present a dissertation with original research. Review the Dissertation Publication page for more information.
• Complete the necessary teaching requirement
• Submit necessary forms to file for graduation
• Complete degree requirements within the approved timeline

PhD degrees must be approved by the student's academic program. Consult with your program directly regarding specific degree requirements.

The Department requires that students in the Statistics and Data Science PhD program:

• Meet the department minimum residency requirement of 2 years
• STAT 344-0 Statistical Computing
• STAT 350-0 Regression Analysis
• STAT 353-0 Advanced Regression (new 2021-22)
• STAT 415-0 I ntroduction to Machine Learning
• STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3
• STAT 430-1, STAT 430-2, STAT 440 (new courses in 2022-23 on probability and stochastic processes for statistics students)
• STAT 457-0 Applied Bayesian Inference

Students generally complete the required coursework during their first two years in the PhD program. *note that required courses changed in the 2021-22 academic year, previous required courses can be found at the end of this page.

• Pass the Qualifying Exam. This comprehensive examination covers basic topics in statistics and is typically taken in fall quarter of the second year.

Pass the Prospectus presentation/examination and be admitted for PhD candidacy by the end of year 3 . The statistics department requires that students must complete their Prospectus (proposal of dissertation topic) before the end of year 3, which is earlier than The Graduate School deadline of the end of year 4. The prospectus must be approved by a faculty committee comprised of a committee chair and a minimum of 2 other faculty members. Students usually first find an adviser through independent studies who will then typically serve as the committee chair. When necessary, exceptions may be made upon the approval of the committee chair and the director of graduate studies, to extend the due date of the prospectus exam until the end of year 4.

• Successfully complete and defend a doctoral dissertation. After the prospectus is approved, students begin work on the doctoral dissertation, which must demonstrate an original contribution to a chosen area of specialization. A final examination (thesis defense) is given based on the dissertation. Students typically complete the PhD program in 5 years.
• Attend all seminars in the department and participate in other research activities . In addition to these academic requirements, students are expected to participate in other research activities and attend all department seminars every year they are in the program.

Optional MS degree en route to PhD

Students admitted to the Statistics and Data Science PhD program can obtain an optional MS (Master of Science) degree en route to their PhD. The MS degree requires 12 courses: STAT 350-0 Regression Analysis, STAT 353 Advanced Regression, STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3, STAT 415-0 I ntroduction to Machine Learning , and at least 6 more courses approved by the department of which two must be 400 level STAT elective courses, no more than 3 can be non-STAT courses. For the optional MS degree, students must also pass the qualifying exam offered at the beginning of the second year at the MS level.

*Prior to 2021-2022, the course requirements for the PhD were:

• STAT 351-0 Design and Analysis of Experiments
• STAT 425 Sampling Theory and Applications
• MATH 450-1,2 Probability 1, 2 or MATH 450-1 Probability 1 and IEMS 460-1,2 Stochastic Processes 1, 2
• Six additional 300/400 graduate-level Statistics courses, at least two must be 400 -level

University of South Florida

Muma College of Business

Tampa | St. Petersburg | Sarasota-Manatee

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Phd in big data analytics.

The PhD in Big Data Analytics is an interdisciplinary STEM PhD program focusing on systems and technologies for processing data and information. Unlike pure data science programs, this program includes the human and social implications of information and technology, bringing in critical components of cognition, ethics, biases and storytelling into a strong, big data analytics curriculum.

This program will graduate advanced big data practitioners, researchers and scientists who can work with large data, write code, develop models and build systems, and do so while acutely aware of potential biases and ethical uses issues. Students will develop theoretical and applied skills, including how to design, implement and evaluate information-focused big data technologies that support decision-making across social and organizational contexts.

Why the focus on an interdisciplinary program? Many existing PhD programs offer training in all of the stand-alone scientific fields such as statistics, mathematics, computer science, or information systems, but they do not unify the salient ideas from these fields.

In that sense, graduates become experts in a relatively narrow area in, for example, statistical modeling of data, but are inexperienced and unaware of how to parse and store data or how to code “apps” and develop solutions that automatically make decisions based on the data models or how to evaluate the human and societal impact of the developed data solutions and systems.

This PhD program combines human and technical skills with analytical abilities required to support decision-making by today’s leaders and innovators. A big data analytics PhD will introduce students to a truly interdisciplinary program and diverse perspective to important problems and opportunities for society that are driven by the availability of big data.

Why now? An increasing number of companies are looking for professionals with experience in big data analytics, and they are hard to find, especially in the educational spectrum, i.e. at the expert/PhD level. In addition, there is an increasing demand for faculty with PhDs in this field. This program is timely as we are seeing deep problems in society  (polarization of society, fake news, algorithmic bias) where analytics-driven solutions alone struggle to be sufficient — problems where the broader perspective of building intelligent systems by being aware of broader issues and human aspects becomes increasingly important as well.

Programs that bring together curriculum and faculty expertise from multiple areas (such as information systems, mathematics, psychology, computer science, etc.) will play a critical role in this broader context.

Application deadline is February 1. 

Ph.D. Specialization in Data Science

The ph.d. specialization in data science is an option within the applied mathematics, computer science, electrical engineering, industrial engineering and operations research, and statistics departments..

Only students already enrolled in one of these doctoral programs at Columbia are eligible to participate in this specialization. Students should fulfill the requirements below in addition to those of their respective department's Ph.D. program. Students should discuss this specialization option with their Ph.D. advisor and their department's director for graduate studies.

Applied Mathematics Doctoral Program

Computer Science Doctoral Program

Decision, Risk, and Operations (DRO) Program

Electrical Engineering Doctoral Program

Industrial Engineering and Operations Research Doctoral Program

Statistics Doctoral Program

The specialization consists of either five (5) courses from the lists below, or four (4) courses plus one (1) additional course approved by the curriculum committee. All courses must be taken for a letter grade and students must pass with a B+ or above. At least three (3) of the courses should come from outside the student’s home department. At least one (1) course has to come from each of the three (3) thematic areas listed below.

Specialization Requirements

• COMS 4231 Analysis of Algorithms I
• COMS 6232 Analysis of Algorithms II
• COMS 4111 Introduction to Databases
• COMS 4113 Distributed Systems Fundamentals
• EECS 6720 Bayesian Models for Machine Learning
• COMS 4771 Machine Learning
• COMS 4772 Advanced Machine Learning
• IEOR E6613 Optimization I
• IEOR E6614 Optimization II
• IEOR E6711 Stochastic Modeling I
• EEOR E6616 Convex Optimization
• STAT 6301 Probability Theory I
• STAT 6201 Theoretical Statistics I
• STAT 6101 Applied Statistics I
• STAT 6104 Computational Statistics
• STAT 5224 Bayesian Statistics
• STCS 6701 Foundations of Graphical Models (joint with Computer Science) 

Information Request Form

Ph.d. specialization committee.

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• Faculty of Arts and Sciences Professor of Statistics
• The Fu Foundation School of Engineering and Applied Science Professor of Computer Science

Richard A. Davis

• Faculty of Arts and Sciences Howard Levene Professor of Statistics

Vineet Goyal

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Industrial Engineering and Operations Research

Garud N. Iyengar

• The Fu Foundation School of Engineering and Applied Science Vice Dean of Research
• Tang Family Professor of Industrial Engineering and Operations Research

Gail Kaiser

Rocco a. servedio, clifford stein.

• Data Science Institute Interim Director
• The Fu Foundation School of Engineering and Applied Science Wai T. Chang Professor of Industrial Engineering and Operations Research and Professor of Computer Science

John Wright

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Electrical Engineering
• Data Science Institute Associate Director for Academic Affairs

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Data Analytics in United States

5  Data Analytics PhDs in United States

Business Administration - Data Analytics (Qualitative Research) The Business Administration - Data Analytics (Qualitative Research) program at Grand Canyon University... Grand Canyon University Phoenix, Arizona, United States

Business Administration - Data Analytics (Quantitative Research) The Bridge (Doctor of Business Administration with an Emphasis in Data Analytics) program at Grand Canyon... Grand Canyon University Phoenix, Arizona, United States

Data Science and Analytics The graduates of the Data Science and Analytics PhD program at University of Oklahoma will be prepared to... University of Oklahoma Norman, Oklahoma, United States

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Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.

Choosing a Field of Study

There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.

There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .

Residency Requirements

Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.

Your Advisor and Special Committee

The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.

The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.

Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.

The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage . 

Statistics PhD Travel Support

The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information. 

Completion of the PhD Degree

In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.

Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.

Big Data Analytics (PhD)

Program at a glance.

• In State Tuition
• Out of State Tuition

Learn more about the cost to attend UCF.

Big Data Analytics will train researchers with a statistics background to analyze massive, structured or unstructured data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.

The program will provide a strong foundation in the major methodologies associated with Big Data Analytics such as predictive analytics, data mining, text analytics and statistical analysis with an interdisciplinary component that combines the strength of statistics and computer science. It will focus on statistical computing, statistical data mining and their application to business, social, and health problems complemented with ongoing industrial collaborations. The scope of this program is specialized to prepare data scientists and data analysts who will work with very large data sets using both conventional and newly developed statistical methods.

The Ph.D. in Big Data Analytics requires 72 hours beyond an earned Bachelor's degree. Required coursework includes 42 credit hours of courses, 15 credit hours of restricted elective coursework, and 15 credit hours of dissertation research.

Total Credit Hours Required: 72 Credit Hours Minimum beyond the Bachelor's Degree

Program Tracks/Options

• Statistics Track

Application Deadlines

• International

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Program Prerequisites

Students must have the following background and courses completed before applying to the Big Data Analytics PhD program. These courses are: MAC 2311C: Calculus with Analytic Geometry I, MAC 2312: Calculus with Analytic Geometry II, MAC 2313: Calculus with Analytic Geometry III, MAS 3105: Matrix and Linear Algebra or MAS 3106: Linear Algebra , COP 3503C - Computer Science II. These pre-required courses are basic undergraduate courses from the Math and Computer Science departments. Students without background in COP 3503C can still apply for admission but they will need to take that course sometime after admission in the PhD program. COP 3503C serves as pre-requisite for COP 5711, which is required for the qualifying exam.

Degree Requirements

• All Ph.D. students must have an approved Plan of Study (POS) developed by the student and advisor that lists the specific courses to be taken as part of the degree. Students must maintain a minimum GPA of 3.0 in their POS, as well as a "B" (3.0) in all courses completed toward the degree and since admission to the program.

Required Courses

• STA5104 - Advanced Computer Processing of Statistical Data (3)
• STA5703 - Data Mining Methodology I (3)
• STA6106 - Statistical Computing I (3)
• STA6236 - Regression Analysis (3)
• STA6238 - Logistic Regression (3)
• STA6326 - Theoretical Statistics I (3)
• STA6327 - Theoretical Statistics II (3)
• STA6329 - Statistical Applications of Matrix Algebra (3)
• STA6704 - Data Mining Methodology II (3)
• STA7722 - Statistical Learning Theory (3)
• STA7734 - Statistical Asymptotic Theory in Big Data (3)
• STA6714 - Data Preparation (3)
• CNT5805 - Network Science (3)
• COP5711 - Parallel and Distributed Database Systems (3)

Restricted Electives (at least 9 credit hours must be STA coursework)

• Other courses may be included in a Plan of Study with departmental approval. Other electives can be used at the discretion of the student advisor and/or Graduate Coordinator.
• STA6107 - Statistical Computing II (3)
• STA6226 - Sampling Theory and Applications (3)
• STA6237 - Nonlinear Regression (3)
• STA6246 - Linear Models (3)
• STA6346 - Advanced Statistical Inference I (3)
• STA6347 - Advanced Statistical Inference II (3)
• STA6507 - Nonparametric Statistics (3)
• STA6662 - Statistical Methods for Industrial Practice (3)
• STA6705 - Data Mining Methodology III (3)
• STA6707 - Multivariate Statistical Methods (3)
• STA6709 - Spatial Statistics (3)
• STA6857 - Applied Time Series Analysis (3)
• STA7239 - Dimension Reduction in Regression (3)
• STA7719 - Survival Analysis (3)
• STA7935 - Current Topics in Big Data Analytics (3)
• CAP5610 - Machine Learning (3)
• CAP6307 - Text Mining I (3)
• CAP6315 - Social Media and Network Analysis (3)
• CAP6318 - Computational Analysis of Social Complexity (3)
• CAP6737 - Interactive Data Visualization (3)
• COP5537 - Network Optimization (3)
• COP6526 - Parallel and Cloud Computation (3)
• COP6616 - Multicore Programming (3)
• COT6417 - Algorithms on Strings and Sequences (3)
• COT6505 - Computational Methods/Analysis I (3)
• ECM6308 - Current Topics in Parallel Processing (3)
• EEL5825 - Machine Learning and Pattern Recognition (3)
• EEL6760 - Data Intensive Computing (3)
• FIL6146 - Screenplay Refinement (3)
• ESI6247 - Experimental Design and Taguchi Methods (3)
• ESI6358 - Decision Analysis (3)
• ESI6418 - Linear Programming and Extensions (3)
• ESI6609 - Industrial Engineering Analytics for Healthcare (3)
• ESI6891 - IEMS Research Methods (3)
• STA5825 - Stochastic Processes and Applied Probability Theory (3)
• STA7348 - Bayesian Modeling and Computation (3)
• COP6731 - Advanced Database Systems (3)

Dissertation

• Earn at least 15 credits from the following types of courses: STA 7980 - Dissertation Research The student must select a dissertation adviser by the end of the first year. In consultation with the dissertation adviser, the student should form a dissertation advisory committee. The dissertation adviser will be the chair of the student's dissertation advisory committee. In consultation with the dissertation advisor and with the approval of the chair of the department, each student must secure qualified members of their dissertation committee. This committee will consist of at least four faculty members chosen by the candidate, three of whom must be from the department and one from outside the department or UCF. Graduate faculty members must form the majority of any given committee. A dissertation committee must be formed prior to enrollment in dissertation hours. The dissertation serves as the culmination of the coursework that comprises this degree. It must make a significant original theoretical, intellectual, practical, creative or research contribution to the student's area within the discipline. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor. The dissertation will be completed through a minimum of 15 hours of dissertation research credit.

Examinations

• After passing candidacy, students will enroll into dissertation hours (STA7980) with their dissertation advisor. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor.

Qualifying Examination

• The qualifying examination is a written examination that will be administered by the doctoral exam committee at the start of the fall term (end of the summer) once a year. The courses required to prepare for the examination are STA 5703, STA 6704, CNT 5805, STA 6326, STA 6327 and COP 5711. Students must obtain permission from the Graduate Program Coordinator to take the examination. Students normally take this exam just before the start of their third year and are expected to have completed the exam by the start of their fourth year. To be eligible to take the Ph.D. qualifying examination, the student must have a minimum grade point average of 3.0 (out of 4.0) in all the coursework for the Ph.D. The exam may be taken twice. If a student does not pass the qualifying exam after the second try, he/she will be dismissed from the program. It is strongly recommended that the student select a dissertation adviser by the completion of 18 credit hours of course work, and it is strongly recommended that the student works with the dissertation adviser to form a dissertation committee within two semesters of passing the Qualifying Examination.

Candidacy Examination

• The candidacy exam is administered by the student's dissertation advisory committee and will be tailored to the student's individual program to propose either a research‐ or project‐based dissertation. The candidacy exam involves a dissertation proposal presented in an open forum, followed by an oral defense conducted by the student's advisory committee. This committee will give a Pass/No Pass grade. In addition to the dissertation proposal, the advisory committee may incorporate other requirements for the exam. The student can attempt candidacy any time after passing the qualifying examination, after the student has begun dissertation research (STA7919, if necessary), but prior to the end of the second year following the qualifying examination. The candidacy examination can be taken no more than two times. If a student does not pass the candidacy exam after the second try, he/she will be removed from the program

Admission to Candidacy

• The following are required to be admitted to candidacy and enroll in dissertation hours. Completion of all coursework, except for dissertation hours Successful completion of the qualifying examination Successful completion of the candidacy examination including a written proposal and oral defense The dissertation advisory committee is formed, consisting of approved graduate faculty and graduate faculty scholars Submittal of an approved program of study

Masters Along the Way

• PhD Students can obtain their Master's degree in Statistics & Data Science - Data Science Track along the way to their PhD degree. To satisfy the requirements for the MS degree, the student must complete the requirement for the MS degree. The student has the option of choosing between thesis option or non-thesis option.

Independent Learning

• As will all graduate programs, independent learning is an important component of the Big Data Analytics doctoral program. Students will demonstrate independent learning through research seminars and projects and the dissertation.

Grand Total Credits: 72

Application requirements, financial information.

Graduate students may receive financial assistance through fellowships, assistantships, tuition support, or loans. For more information, see the College of Graduate Studies Funding website, which describes the types of financial assistance available at UCF and provides general guidance in planning your graduate finances. The Financial Information section of the Graduate Catalog is another key resource.

Fellowship Information

Fellowships are awarded based on academic merit to highly qualified students. They are paid to students through the Office of Student Financial Assistance, based on instructions provided by the College of Graduate Studies. Fellowships are given to support a student's graduate study and do not have a work obligation. For more information, see UCF Graduate Fellowships, which includes descriptions of university fellowships and what you should do to be considered for a fellowship.

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PhD in Data Analytics Programs

A PhD in data analytics prepares professionals to work in data-driven fields, including research, business, healthcare, and government.

The most common reason people pursue a PhD in a data-related field is that they are passionate about data and would like to have a career that involves research and making discoveries, usually within a sub-field. 

Data analytics PhD programs allow students to get an in-depth knowledge of research methods and topics they will use throughout their careers. Like other research-oriented doctoral degrees, a PhD in analytics is most often pursued by people interested in academic careers.

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Although, increasingly, data analytics PhDs are also employed by technology companies to help improve products, services, or business opportunities.

If you are curious to know more about research in the field of data and analytics, these research interests can be divided majorly into two different areas — methods and applications.

Applications of a PhD in Analytics

Examples of research that improves methods and techniques of data utilization.

Method-based data analytics PhD research focuses on gaining a deeper understanding of algorithms used in analytics. This method of research can involve any of the following:

• Research involving understanding algorithms has led to tremendous growth in analytical tools with improved deep-learning performances on large-scale data.
• Researchers have also been investing their time in understanding methods to collect data with a low signal-to-noise ratio, working with incomplete data, or generating synthetic data to understand natural phenomena where data is not readily available or rare. Few others involve researching methods of combining data from sources that aren’t of the same type, e.g., voice data with self-reported psychiatric questionnaires to understand mood and emotions.
• As people and organizations are highly aware of how crucial data can be, there have been increasing reports of data thefts and fraud, which leaves vulnerable people at a loss. One area of research crucial in the data world is ethics and data privacy.
• With the explosive growth of data, ongoing research has made tremendous growth in developing storage systems to improve data availability with consistency in real-time analysis. 

Examples of research that utilize data-related techniques to improve or create applications in a given field

Another common data analytics PhD research area involves understanding how other scientists, researchers, and practitioners apply data analytics to other fields. These areas of applications range widely, not just limited to finance or medicine but also “social good” projects. 

Examples of research in social good projects solve specific crisis-related challenges, such as responses to natural and human-made disasters in search and rescue missions and the outbreak of disease. Other examples include using analytics to solve environmental challenges, education, criminal justice, etc.

Best PhD in Data Analytics Degree Programs for 2024

Florida atlantic university, grand canyon university, kennesaw state university, university of central florida, university of massachusetts boston, university of south florida, university of the southwest.

These rankings were compiled from data accessed in December 2023 from Integrated Post-Secondary Education Data System (IPEDS) and College Navigator (both services National Center for Education Statistics). Tuition data was pulled from individual university websites and is current as of December 2023. If available, we also use additional criteria such as accreditation or designations by outside organizations or agencies.

PhD in Data Analytics Curriculum

A PhD in data analytics has an intensive academic workload, generally completed between four and five years. Since the data industry has emerged only in the last decade, institutions that provide Ph.D. solely in data analytics are hard to find. Data analytics-related specialization is tied to either STEM or business-related research programs.

Components of PhD in Data Analytics

Here is a general overview of the requirements that are needed to complete this degree program:

Credit Requirements

Every PhD program has requirements to complete a certain amount of credits. These credits could be related to foundational or advanced-level qualitative and quantitative methods in statistics.

Based on your interest and flexibility in the program, the institution may offer you an option of cognate courses. The course curriculum is similar to the master’s-level program with few additions of research-related classes.

Pre-Candidacy Research Projects

The first one or two years in the program prepare you for admission to candidacy by working on research projects. These research projects also help you develop the skills necessary to frame questions and solve real-world data problems.

Preliminary or Qualifying Examination

Every PhD program requires its students to go through a qualifying exam. These exams test their skills to meet candidacy requirements. These pre-candidacy exams assist in fulfilling the requirement of having the theoretical and practical knowledge needed to work on your research project.

Teaching Requirement

Almost all PhD programs require the students to teach undergraduate-level courses or assist the professor in their teaching classes. These opportunities and experiences prepare you for an academic career.

Dissertation Proposal

The dissertation proposal contains the hypothesis of your research that should meet the standards of publications in data analytics. The proposal needs to be approved by the committee of faculty members before any proceedings to work on it.

Successful Dissertation Defense

Students are expected to present their original work on the dissertation proposal. They are expected to be experts in their data-related dissertation topic and defend their analysis. This is an important aspect of your PhD in analytics as it signifies that the student has successfully grasped all the necessary skills required to conduct their own independent research post-degree completion. 

Optional Requirements

A Ph.D. is not just about taking credits and completing qualifying exams. During this program, there are many opportunities that a student is likely to benefit from. Attending data analytics conferences and getting internships during school breaks help students exchange research knowledge and form social connections necessary for job search.

Since the data field evolves at a much faster rate, it keeps students abreast of the latest trends in the data industry. Conferences are likely to provide students with discounted academic prices to attend them. Online platforms like Kaggle give opportunities to network, form teams, and participate in online challenges to showcase your skills.

Some institutions can provide you with a data analytics master’s degree if you can complete more than two years of your program but cannot continue further.

PhD in Data Analytics Online

There are many online educational opportunities available, especially in higher education. Like a PhD in data analytics online, online degrees offer a wide range of flexibility in terms of timing, workflow, and geographic location. 

Leading universities offer programs that can bring the best of their faculty research to the masses. Many great data analytics master’s programs are now entirely online.

But, there are fewer than 100 percent PhD in data analytics programs online (although more are being created and launched to meet the uptick in demand and because educational formats are changing rapidly). 

One of the main reasons doctoral programs are still taught mainly in traditional settings is that they require much collaborative research. Most data analytics PhD programs also require some teaching component, which is not primarily handled in person.

But the world is changing fast, and colleges and universities are adapting quickly to both the needs of students and the needs of an evolving workforce. So stay tuned, and keep track of updates to your favorite data analytics programs. Be sure to ask about remote or online options and possibilities when contacting traditional in-person programs.

PhD in Big Data Analytics

Big data is a term that was popularized in the last decade and refers to the classification and organization of massive data sets. 

The reason experts or PhDs can wrangle big data is that the world continues to produce new data at an exponential rate. 

By way of illustration, consider this statistic about creating new data; according to the site Statista , “The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 64.2 zettabytes in 2020.

Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes. In 2020, the amount of data created and replicated reached a new high.” For reference, the amount of data collected worldwide in 2010 was estimated to be two zettabytes. 

There is a lot of enthusiasm about the trends and patterns found within massive data sets. Researchers in healthcare and agriculture are working with big data to find answers to questions ranging from cancer outcomes to crop outputs. 

Given the new research opportunities made possible by big data, it makes sense that a specialty Ph.D. in big data analytics is emerging in university analytics graduate programs.

Career Paths for Data Analytics PhDs

As we mentioned initially, there are many potential career pathways for data analytics PhDs. This kind of degree often has a home in academia, but businesses and organizations are increasingly looking for researchers and practitioners of data analytics. 

Academic Positions 

• Appointed to the research staff, whose primary goals are to extend their education and experience. Although they hold a doctoral degree, they are not considered independent researchers and cannot serve as principal investigators. Some teaching duties may also be required. Positions are often for a fixed term ranging from six months to three years.
• Average Salary: $85,959
• Typically the first step to tenure and conducting independent research. Once they complete tenure, they may be given the title of a professor. The tenure track is often a long journey of evaluating an associate professor’s publications, research, and teaching. The tenure track lasts somewhere between five to seven years.
• Average Salary: $80,057

Industry Positions 

• By wrangling with data to develop meaningful insights, data scientists help organizations find and solve problems related to products or services. Combining computer science, statistics, and business knowledge, data scientists assist organizations in making objective decisions using data-driven strategies.
• Average Salary: $122,738
• Unlike data scientists or data engineers, research scientists don’t work on product development. Instead, they design and conduct experiments by developing hypotheses and measuring the outcome of their experiments.
• Average Salary: $119,165
• A chief analytics officer leads an organization’s data analytics strategy, driving data-related business changes and working with data scientists in developing data-related products.
• Average Salary: $151,203

Frequently Asked Questions

Many top-tier universities require professors, researchers, and principal investigators to have a doctoral degree. A PhD is relevant if you are looking for a career in academia. However, it is not necessary to have a PhD to gain entry into data analytics unless you are looking into specific research roles in the industry. There is a minimal difference in the salary outcome of an individual getting a PhD versus someone who has a master’s degree in analytics.

If the institution cannot fund your PhD program, checking out external funding sources and scholarships before admission is highly recommended.

Most institutions need you to have a bachelor’s degree in a quantitative field. Work experience may also be preferred by not necessary. Strong research interest is recommended to gain admission.

Since PhD degree programs are research-oriented, an applicant’s GPA does play an essential role in the admissions process. Some universities have a minimum GPA cutoff, while others request that applicants complete undergraduate-level mathematics and statistics courses with a minimum grade.

This question is tricky to answer. Some universities encourage getting in touch with the professor to see if they are open to admitting new PhD students for the upcoming academic year. Other university programs clearly state that contacting professors during the admissions process is unnecessary. You can still express your desire to work with a specific professor in your statement of purpose during the application process if contacting professors directly is not allowed.

Related Resources

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Statistics & Data Science

Dietrich college of humanities and social sciences, ph.d. programs, our ph.d. programs enable students to pursue a wide range of research opportunities, including constructing and implementing advanced methods of data analysis to address crucial cross-disciplinary questions, along with developing the fundamental theory that supports these methods..

Unique opportunities for our Ph.D. students include:

• We host four cross-disciplinary joint Ph.D. programs for students who want to specialize in machine learning , public policy , neuroscience , and the link between engineering and policy .
• Our faculty have deep involvement in a range of important, data-rich scientific collaborations, including in the areas of genetics, neuroscience, astronomy, and the social sciences. This allows students to have easy access to both the crucial questions in these fields, and to the data that can provide the answers.
• Students begin work on their Advanced Data Analysis Project in the second semester. This year-long, faculty/student collaboration, distinct from the thesis, provides an immediate intensive research experience.
• Carnegie Mellon is home to the first Machine Learning Department . Many of our faculty maintain joint appointments with this Department and they (and our students) have strong connections to this exciting and growing area of research.

The programs leading to the degree of   Doctor of Philosophy in Statistics   seek to strike a balance between theoretical and applied statistics. The Ph.D. program prepares students for university teaching and research careers, and for industrial and governmental positions involving research in new statistical methods. Four to five years are usually needed to complete all requirements for the Ph.D. degree.

These pages present the requirements for each of our Ph.D. programs.

The page   "Core Ph.D. Requirements"   lays out the requirements for all Ph.D. students, while each of the four joint programs are described under the Joint Ph.D. Degrees pages. Our Ph.D. students can also earn a   Master of Science in Statistics   as an intermediate step towards their ultimate goal.

Joint Ph.D. Programs

Statistics/machine learning, statistics/public policy, statistics/engineering and public policy, statistics/neural computation  .

Table of Contents

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New technologies are constantly being developed in the field of data analysis. Data analysts have a variety of job opportunities. These people might operate in many different industries. Technical know-how is needed for this vocation to evaluate and interpret data to enhance business performance. Getting a PhD in Data Analytics can be really beneficial in such cases.

This article discusses everything you need to know about PhD in Data Analytics.

PhD in data analytics allows students to learn in-depth methodological approaches and subjects that will be useful to them during their careers. A PhD in analytics is typically pursued by those involved in academic jobs, similar to other research-focused doctoral degrees.

Data science and data analytics has been a source of confusion for many people. When considering getting a doctorate, which field should you choose?

• Data analysis involves analyzing large data sets and interpreting them to make strategic choices. When pursuing PhD, you will learn about the techniques and tools involved in data analysis. You will also have to research the same and develop new suggestions.
• Whereas data science uses the analyzed data to create solutions for business problems. 

Someone who is interested in learning more about analytical techniques should pursue a Ph.D. in data analytics. The PhD in Data Science is the program of choice for professionals who enjoy understanding the ins and outs of intricate machine learning and big data methodologies.

The general distinctions between PhD in Data Analytics and a Master’s degree are: 

PhD in Data Analytics: A doctorate program is the highest form of qualification an institute offers. This degree can take up to 4-5 years. A PhD is extremely based on deep research for a specific field.

Master’s: A master’s degree is a degree that a student needs to complete before applying for PhD. This program usually takes up 1-2 academic years. 

Those who are enthusiastic about data and want to have a profession that involves the study and creation of discoveries, typically within a subfield, are the most likely candidates for a PhD in a topic linked to data. A PhD in data analytics can help anyone upgrade their status in data science and analysis.

There are many advantages to earning a PhD in data analytics.

• You can contribute significantly to the field with a PhD in data analytics.
• You will be able to carry out original research as opposed to just reiterating previously published material.
• You'll be able to explore other areas of study thanks to it as well.
• In addition to exploring new academic fields, you will be able to broaden your knowledge outside of data analytics.

The following are some disadvantages of pursuing a PhD in data analytics.

• A Doctorate can be a solitary endeavor.
• You can miss out on an important professional experience.

Become a Data Science & Business Analytics Professional

• 11.5 M Expected New Jobs For Data Science And Analytics
• 28% Annual Job Growth By 2026
• $46K-$100K Average Annual Salary

Post Graduate Program in Data Analytics

• Post Graduate Program certificate and Alumni Association membership
• Exclusive hackathons and Ask me Anything sessions by IBM

Data Analyst

• Industry-recognized Data Analyst Master’s certificate from Simplilearn
• Dedicated live sessions by faculty of industry experts

Here's what learners are saying regarding our programs:

Felix Chong

Project manage , codethink.

After completing this course, I landed a new job & a salary hike of 30%. I now work with Zuhlke Group as a Project Manager.

Gayathri Ramesh

Associate data engineer , publicis sapient.

The course was well structured and curated. The live classes were extremely helpful. They made learning more productive and interactive. The program helped me change my domain from a data analyst to an Associate Data Engineer.

Career options for PhD in Data Science holders:

• Senior Data Scientist
• Chief Data Officer
• Market Research Analyst
• Senior Data Analyst
• Data Engineer
• Business Intelligence Developer

A general PhD in Data Analytics curriculum looks like this:

• Each PhD program has a minimum number of credits that must be earned.
• A qualifying exam is a requirement for enrollment in any PhD program.
• The majority of PhD courses call for students to instruct undergraduate courses or help the professor in instructing classes.
• Before any steps are taken to begin working on the plan, the panel of academic staff must approve it.

When applying for PhD in Data Analytics, you might want to take into account the following factors.

• Faculty: Look over the teachers and see their professional and academic backgrounds.
• Flexibility: Several PhD schools allow part-time enrollment, but many prefer full-time students.
• Fund: It is crucial to start planning how you will pay for your research as soon as feasible.
• Department: Finding departments that specialize in your field of study is extremely crucial.
• Eligibility: You must hold a master’s degree before you apply for a Doctorate.

Before deciding to get a PhD in Data Science, consider doing a certificate course to build skills that will help you in your research.

• Data Analyst Course by Simplilearn : You will become a data analytics specialist after taking this course on data analysis, which was created in partnership with IBM. You will discover the most recent analytics tools and methods in this course on data analytics.
• Google Data Analytics Professional Certificate: The participants in their data analytics course are beginners with no prior knowledge of data analytics or similar technical domains.
• CCA Data Analyst Exam: The exam establishes a benchmark that makes it simple for future employers to verify and evaluate your real-world data analysis abilities.

A yearly cost of between $35,000 to $50000 is typical for a PhD in data analytics. Students pursuing doctorates frequently receive financial aid. Programs frequently provide financial assistance and tuition breaks in exchange for conducting research or teaching.

PhD in data analytics is the right choice if you want to enhance your career mark in the field. If you want to prepare for a PhD or are in quest of enhancing analysis skills, consider doing Simplilearn’s Professional Certificate Program In Data Analytics .

1. Is a PhD in data analytics worth it?

You may master all the skills and knowledge required to succeed in data analysis by earning a PhD in data analytics.

2. How long does a PhD in data analytics take?

It usually requires 4-5 years to finish courses, fieldwork, and a dissertation for a Ph. D. in data analytics.

3. Can I do PhD in data science after an MBA?

You can apply for a PhD in data analytics after earning your MBA. Ensure that you get your MBA degree from a reputed college. Getting MBA in Business Management might help you while pursuing PhD in data analytics.

4. Which PhD is most in demand?

The in-demand PhD degrees include data science and data analytics since data is the new truth of the world. Companies produce a lot of data, and they need scholars who have education in data science or data analytics field.

5. Do PhD students get paid enough?

PhD in data analytics can help you land a job that pays you well. An average salary of $115,428 per year can be earned by a senior data analyst, according to Glassdoor.

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

Learn from Industry Experts with free Masterclasses

Data science & business analytics.

How Can You Master the Art of Data Analysis: Uncover the Path to Career Advancement

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Career Masterclass: How to Get Qualified for a Data Analytics Career

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Data Analytics Basics: A Beginner’s Guide

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What is Data Analytics and its Future Scope in 2024

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What’s the Difference Between Data Analytics and Business Analytics

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PhD in Data Science and Analytics

Degrees & Programs

• Doctoral Degree in Data Science and Analytics
• Certificates

We launched the first formal PhD program in Data Science in 2015.  Our program sits at the intersection ofcomputer science, statistics, mathematics, and business.  Our students engage in relevant research with faculty from across our eleven colleges.  As one of the institutions on the forefront of the development of data science as an academic discipline, we are committed to developing the next generation of Data Science leaders, researchers, and educators. Culturally, we are committed to the discipline of Data Science, through ethical practices, attention to fairness, to a diverse student body, to academic excellence, and research which makes positive contributions to our local, regional, and global community.   

Herman Ray , Director, Ph.D. in Data Science and Analytics

About the Doctoral Degree in Data Science and Analytics

This degree will train individuals to translate and facilitate new innovative research, structured and unstructured, complex data into information to improve decision making. This curriculum includes heavy emphasis on programming, data mining, statistical modeling, and the mathematical foundations to support these concepts. Importantly, the program also emphasizes communication skills – both oral and written – as well as application and tying results to business and research problems.

Because this degree is a Ph.D., it creates flexibility. Graduates can either pursue a position in the private or public sector as a "practicing" Data Scientist – where continued demand is expected to greatly outpace the supply - or pursue a position within academia, where they would be uniquely qualified to teach these skills to the next generation.

Information Sessions for Fall 2025 Admission

To be announced

Data Science and Analytics PhD Curriculum

Stage One: Pre-Program Requirements

• Successful applicants will have completed a masters degree in a computational field (e.g., engineering, computer science, statistics, economics, finance, etc.)
• Applicants are expected to have deep proficiency in at least one analytical programming language (e.g., SAS, R, Python). SQL and Java are helpful but not required.
• Interested applicants who have earned an undergraduate degree are encouraged to apply to the Ph.D. Program with the embedded MS in Computer Science or with the MS in Applied Statistics.

Stage Two: Coursework

The Ph.D. in Data Science and Analytics requires 78 total credit hours spread over four years of study. Example Program of Study: 

• CS 8265  - Big Data Analytics
• CS 8267  - Machine Learning
• MATH 8010  - Theory of Linear Models (optional)
• MATH 8020  - Graph Theory
• MATH 8030  - Applied Discrete and Combinatorial Mathematics 
• STAT 8240  - Data Mining I
• STAT 8250  - Data Mining II
• Comprehensive Exam 
• 21 credit hours of electives/concentration

Students take up to 9 credit hours of 6000- or 7000-level courses in DS, STAT, or CS with permission of the program director. Students take any 8000- or 9000-level course in DS, STAT, MATH, CS or IT, or the HHS courses in the mHealth concentration.

• at least 15 credit hours in CS courses at 8000 or 9000 levels (except CS 9900)
• at least 15 credit hours in STAT courses at 8000 or 9000 levels
• HHS 8000 - Introduction to mHealth
• HHS 8010 - Ethical Issues in mHealth, Healthcare and Human Subjects Research
• STAT 8235 - Advanced Longitudinal Data Analysis
• HHS 8050 - Advanced Research in mHealth
• HHS 8020 - mHealth Applications or HHS 8030 - Advanced Special Topics in mHealth
• Develop Dissertation Research Proposal 
• DS 9700 Doctoral Internship/Research Lab
• DS 9900 Dissertation
• Dissertation Proposal Defense
• DS 9900 DissertationFinal Dissertation Defense

Stage Three: Project Engagement and Research/Dissertation

Relevant, interdisciplinary research forms the foundation of the Ph.D. in Data Science and Analytics. While students are encouraged to engage in research from their first semester, the last two years of the program are structured to help students transition into becoming independent, lead researchers. In this last stage of the program, students will work with research faculty, including their advisor, in one of our data science research labs.

Program Student Learning Outcomes

At the end of the program, students will be able to:

• Demonstrate their understanding of the research process
• Demonstrate mastery of core concepts relevant to three key areas in mathematics, statistics and computer science
• Develop themselves as professionals prepared for work as a doctoral-educated individual beyond graduation

Admission Requirements and Application

Frequently Asked Questions (FAQ)

How long will the program take?

How much does the program cost?

Who would be successful in the program?

Where do these graduates work after graduation?

What are the publication/research requirements?

What did Science Doctoral Students Study?

• Applied Computer Science
• Applied Economics and Statistics
• Applied Statistics
• Applied Mathematics
• Bioinformatics
• Business Analytics
• Chemical Biology
• Computer Science
• Data Science
• Forecasting & Strategic Management
• Integrative Biology
• Public Admin in Economic Policy Mgmt
• Mathematics
• Mechanical Engineering
• Software Engineering

What is the Project Engagement requirement?

Can I pursue the program part- time while I am working full-time?

Can I live on campus?

Are the courses online?

Do I have to have a masters degree to apply?

Where did Data Doctoral Students Study?

• Ajou University, South Korea
• Albert-Ludwigs University of Freiburg
• Auburn University
• Bowling Green State University
• Clemson University
• Columbia University
• Columbus State University
• Florida State University
• Georgia Southern University
• Georgia State
• Georgia Tech
• Iran University of Science and Technology
• Kennesaw State University
• Marshall University
• Michigan State University
• Murray State University
• North Carolina State University
• St. Petersburg State University, Russia
• University of KwaZulu-Natal, South Africa
• University of Michigan
• University of North Carolina
• University of Toledo

Ph.D. in Data Science and Analytics Student Cohorts

Royce Alfred

Bachelor's Degree:   Psychology, Kennesaw State University

Master's Degree:   Applied Statistics and Analytics, Kennesaw State University

Work History:   4 years as a Data Scientist at Equifax

Professional Objective:   Work as a research data scientist in the corporate environment

Venkata Abhiram Chitty

Bachelor's Degree:   Mathematics, Statistics and Computer Science, Osmania University, Telangana, India

Master's Degree:   Data Science, VIT-AP University, Amaravati, Andhra Pradesh, India

Professional Objective:   To apply my Data Science skills in public health domain and help the society

Caleb Greski

Bachelor's Degree: 

Master's Degree: 

Work History: 

Courses Taught: 

Publications: 

Professional Objective: 

Moukthika Kadaparthi

Bachelor's Degree:   Electrical and Electronics Engineering, SASTRA Deemed University

Master's Degree:   Computers and Information Science, Cleveland State University

Work History:  

• Business Intelligence Analyst, Philips Healthcare, Georgia
• Graduate Research Assistant, Cleveland State University, Ohio 

Professional Objective:   My objective is to enter academia with the aim of sharing the practical applications of data science in diverse domains and its potential positive impacts. With my unique blend of academic rigor and industry experience, I am driven to analyze complex data sets using cutting-edge data science techniques, to provide actionable insights and support data-driven decision-making.

Bachelor's Degree:   Civil Engineering, Huazhong University of Science and Technology, China

Master's Degree:   Business Analytics, Syracuse University

• Credit Modeling Analyst, Agricultural Development Bank of China
• Research Assistant, Changjiang Securities
• Graduate Assistant, Syracuse University

Courses Taught:  Calculus I, Marketing Analytics, Data Mining

Awards:   Merit-Based Scholarship, Syracuse University

Professional Objective:   To secure a challenging position in a reputable organization to expand myself within the field of Artificial Intelligence.

Kausar Perveen

Bachelor's Degree:   Bachelor in Engineering Software Engineering, National University of Sciences and Technology, Pakistan

Master's Degree:   Masters in Data Science, Illinois Institute of Technology, Chicago

• Fullstack Developer at ItRunsInMyFamily, Charleston, South Carolina
• Software Engineer II , Xgrid Pakistan
• Senior Research Coordinator, Aga Khan University Pakistan
• Machine Learning Engineer, Agoda Thailand

Publications:  National cervical cancer burden estimation through systematic review and analysis of publicly available data in Pakistan 

Service and Awards:

• Fulbright Scholarship award for Master’s degree in Data Science
• Aga Khan Education Service Pakistan, merit cumulative need based scholarship for Bachelors in Software Engineering 

Professional Objective:  My main motivation behind getting a degree in Data Science is to receive and perform qualified research experience in Data Science and public health

Bachelor's Degree:   Statistics, University of Dhaka, Dhaka, Bangladesh

Master's Degree:   Mathematics (Statistics Concentration), University of Toledo, Ohio

• Analytics Engineer Intern, Cooper Smith, Toledo, Ohio
• Business AnalystAkij Food and Beverage Limited, Dhaka, Bangladesh

Courses Taught:   Introduction to Statistics

Professional Objective:   I am interested to work as a data scientist in the industry

Ayomide Isaac Afolabi

Bachelor's Degree:  Chemical Engineering, Ladoke Akintola University of Technology 

Master's Degree:  Data Science, Auburn University 

Work History:   Graduate Research Assistant, Auburn University 

Courses Taught:   Python Programming 

Publications:   Larson EA, Afolabi A, Zheng J, Ojeda AS. Sterols and sterol ratios to trace fecal contamination: pitfalls and potential solutions. Environ Sci Pollut Res Int. 2022 Jul;29(35):53395-53402.  doi: 10.1007/s11356-022-19611-2 . Epub 2022 Mar 14. PMID: 35287190

Professional Objective:  To work as a research data scientist in the industry

Dinesh Chowdary Attota

Bachelor's Degree:   Computer Science, Jawaharlal Nehru Technological University Kakinada (JNTUK), India

Master's Degree:   Computer Science, Kennesaw State University

Work History:   Associate Consultant, SL Techknow Solutions India Pvt Ltd, India  2018 - 2020

Publications:  

• An Ensemble Multi-View Federated Learning Intrusion Detection for IoT
• A Conversational Recommender System for Exploring Pedagogical Design Patterns
• An Ensembled Method For Diabetic Retinopathy Classification using Transfer Learning  

Professional Objective:   I'd like to be a faculty member at a university so that I can continue to do research.

Nzubechukwu Ohalete

Bachelor's Degree:   Mathematics,University of Nigeria, Nsukka

Master's Degree:   Applied Statistics, Bowling Green State University

Work History:   Graduate Assistant/Data Analyst, Federal University of Technology, Owerri - Mathematics Department

Courses Taught:  Elementary Mathematics, Mathematical Methods

Awards:   James A. Sullivan Outstanding Graduate Student Award, Applied Statistics and Operations Research Department, April 2022

Professional Objective:   To use data science techniques to solve problems which makes our lives better and also makes our world a better place

Ryan Parker

Bachelor's Degree:  Microbiology, University of Tennessee - Knoxville

Master's Degree:   Integrative Biology, Kennesaw State University

Work History:  Instructor of Biology, Kennesaw State University

Courses Taught:   Nursing Microbiology Lectures and Labs, Introductory Biology Labs, Biotechnology Lectures and Labs

• Parker RA, Gabriel KT, Graham K, Cornelison CT. Validation of methylene blue viability staining with the emerging pathogen Candida auris. J Microbiol Methods. 2020 Feb;169:105829.   doi: 10.1016/j.mimet.2019.105829 . Epub 2019 Dec 27. PMID: 31884053.
• Parker RA, Gabriel KT, Graham KD, Butts BK, Cornelison CT. Antifungal Activity of Select Essential Oils against Candida auris and Their Interactions with Antifungal Drugs. Pathogens. 2022 Jul 22;11(8):821.   doi: 10.3390/pathogens11080821 . PMID: 35894044; PMCID: PMC9331469.

Awards:   Best Graduate Poster: Symposium for Student Scholars hosted by Kennesaw State University (Fall 2018) for Poster: "Antifungal Activity of Select Essential Oils and Synergism with Antifungal Drugs against Candida auris"

Professional Objective : To apply Data Science techniques to large scientific datasets, such as genomic and astronomical data, and to help bridge the gap between disparate fields by working in an interdisciplinary space to offer integrative and data-driven solutions to the increasingly complex problems presented to the traditional Sciences.

Askhat Yktybaev

Bachelor's Degree:   Forecasting and Strategic Management, Saint-Petersburg State University of Economics and Finance, Russia

Master's Degree:   Forecasting and Strategic Management, Saint-Petersburg State University of Economics and Finance, Russia; Public Administration in Economic Policy Management, School of International and Public Affairs, Columbia University

Work History:

• from Data Analyst to Head of Research Unit, Central Bank of Kyrgyz Republic
• Sr. Data Scientist in OJSC, Aiyl Bank, Kyrgyzstan
• Consultant, The World Bank, Washington D.C.

Courses Taught:   Financial Programing in the Central Bank, Monetary Policy Transmission Mechanism

Service and Awards:   Winner of the Joint Japan/World Bank Graduate Scholarship Program, National Bank Silver Medal for Best Forecast

Professional Objective:   I want to found a successful Fintech startup one day.

Sanad Biswas

Bachelor's Degree:   Statistics, Biostatistics and Informatics, University of Dhaka, Bangladesh

Master's Degree:   Statistics, University of Toledo, OH

• Research Assistant: US Army Research Lab, Kennesaw State University
• Consultant, Statistical Consulting Service, University of Toledo
• Graduate Teaching Assistant, University of Toledo

Courses Taught:   Calculus and Business Calculus, Facilitated students’ study of Statistics courses at the University of Toledo.

Professional Objective:   To work as a researcher in the industry or as a faculty. I am primarily interested in the application of machine learning in different fields.

Mallika Boyapati

Bachelor's Degree:  Electronics and Computer Engineering, K L University, India

Master's Degree:  Applied Computer Science, Columbus State University

• T-Mobile, Seattle, WA, USA: Sr. Data analyst, 2018- 2021
• UITS, Columbus State University, Columbus, GA, USA: Data Analyst -Graduate assistant, 2016-2018
• Menlo Technologies, India: Jr. Data Analyst, Intern, 2014- 2016

Courses Taught:   DATA 4310 - Statistical Data Mining

Publications:

• Anti-Phishing Approaches in the Era of the Internet of Things. In: Pathan, AS.K. (eds) Towards a Wireless Connected World: Achievements and New Technologies. Springer, Cham -   https://doi.org/10.1007/978-3-031-04321-5_3
• An empirical analysis of image augmentation against model inversion attack in federated learning -   https://doi.org/10.1007/s10586-022-03596-1
• M. Boyapati and R. Aygun, "Phishing Web Page Detection using Web Scraping," SoutheastCon 2023, Orlando, FL, USA, 2023, pp. 167-174, doi: 10.1109/SoutheastCon51012.2023.10115148.
• M. Boyapati and R. Aygun, "Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering," 2023 IEEE 17th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 2023, pp. 139-142, doi: 10.1109/ICSC56153.2023.00029.
• Boyapati, M., Aygun, R. (2023) Explainable Machine Learning for Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering. In Encyclopedia with Semantic Computing and Robotic Intelligence VOL. 0 https://doi.org/10.1142/S2529737623500119
• Winners of Dataiku March Madness Bracket-thon, 2021 in predicting the NBA bracket
• Winners of 2021 Analytics Day Ph.D. level research poster presentation 

Professional Objective:   To leverage strong analytical and technical abilities to research and develop effective data models, visualize data, and uncover insights that makes an impact in field of data science

Nina Grundlingh

Bachelor's Degree:   Applied Mathematics and Statistics, University of KwaZulu-Natal, South Africa

Master's Degree:   Statistics, University of KwaZulu-Natal, South Africa

Courses Taught:   Introduction to Statistics, University of KwaZulu-Natal

• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling diabetes in South Africa. The 61st conference of the South African Statistical Association, 27-29 November 2019, Nelson Mandela University, South Africa.
• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling diabetes in the South African population. College of Agriculture, Engineering and Science Postgraduate Research & Innovation Symposium 2019, 17 October 2019, University of KwaZulu-Natal, Westville, South Africa (the award for best MSc presentation was also received for this).
• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling risk factors of diabetes and pre-diabetes in South Africa. IBS SUSAN-SSACAB 2019 Conference, 8-11 September 2019, Cape Town, South Africa.
• University of KwaZulu-Natal Postgraduate Research & Innovation Symposium 2019 – Best Masters oral presentation
• South African Statistical Association Honours Project Competition 2018/2019 – 2nd place and special prize for best use of SAS

Professional Objective:   To work in a teaching position – sharing how data science can be applied to different fields and the positive impact it could have. I would like to use my theological background and passion to bring insight, clarity, and wisdom to data science problems. 

Namazbai Ishmakhametov

Bachelor's Degree:   Specialist in Mathematical Methods in Economics, Kyrgyz-Russian Slavic University

Master's Degree:   Analytics, Institute for Advanced Analytics at North Carolina State University

• Expert at the Centre for Economic Research, National bank of the Kyrgyz Republic
• Consultant in World Bank project dedicated to strengthening the regulatory practices in Kyrgyz Republic
• Consultant at Deloitte Consulting LLP, Science Based Services group, Analytics & Cognitive offering
• Macroeconomic modeling expert in the Economic Department, National bank of the Kyrgyz Republic

Courses Taught:   Introductory statistics and econometrics (cross-sections, times series and panels) lecturer at Ata-Turk Alatoo International University, Kyrgyzstan

• Ishmakhametov Namazbai, Abdygulov Tolkunbek, Jenish Nurbek. 2020. “ Impact of 2014-2015 shocks on economic behavior of the households in the Kyrgyz Republic ". Working Paper of the National Bank of the Kyrgyz Republic
• Sherrill W. Hayes, Jennifer L. Priestley, Namazbai Ishmakhametov, Herman E. Ray. 2020. “ I’m not Working from Home, I’m Living at Work ”: Perceived Stress and Work-Related Burnout before and during COVID-19”. PsyArxiv Preprints
• Ishmakhametov Namazbai, Arykov Ruslan. 2016. “ Credit Risk Model on the Example of the Commercial Banks of the Kyrgyz Republic ”. Working Paper of the National Bank of the Kyrgyz Republic
• Namazbai Ishmakhametov, Anvar Muratkhanov.2015. “Modeling strategy of the Bank of the Kyrgyz Republic”. National bank of Poland – Swiss National bank joint seminar. Zurich, Switzerland

Professional Objective:   To apply my quantitative skills in the field of biotech either in corporate or government sector

Symon Kimitei

Bachelor's Degrees:   Mathematics, Kennesaw State University, and Computer Science,  Kennesaw State University

Master's Degree:   Mathematics (Scientific Computing Concentration), Georgia State University 

Work History:   Senior Lecturer and Math Department Coordinator of Supplemental Instruction, Kennesaw State University

Courses Taught:   Calculus 1, Precalculus, Applied Calculus & College Algebra 

• Haskin, S., Kimitei, S., Chowdhury, M., Rahman, F., Longitudinal Predictive Curves of Health-Risk Factors for American Adolescent Girls. Journal of Adolescent Health.  JAH-2021-00601R1
• Symon K Kimitei,   Algorithms for Toeplitz Matrices with Applications to Image Deblurring . 2008. Georgia State University, Masters thesis. ScholarWorks 

Poster Presentations:

• Kimitei, Symon & Sammie Haskin. "Nadaraya-Watson Kernel Regression Longitudinal Analysis of Healthcare Risk Factors of African American and Caucasian American Girls." Kennesaw State University R Day Presentation.  11 Nov. 2019. Poster presentation.
• Kimitei, Symon. " Social Network Analysis in Supreme Court Case Rulings by Precedence Using SAS Optgraph/Python." 23rd Annual Symposium of Scholars. Kennesaw State University.  19 April. 2018. Poster presentation.

Professional Objective:   As a Ph.D. student in Analytics & Data Science, I hope to gain skills in the program that will propel me into a Data Scientist / Machine Learning Engineer with a specialization in the design and implementation of deep learning & machine learning algorithms.

Jitendra Sai Kota

Bachelor's Degree:   Computer Science & Engineering, Amrita Vishwa Vidyapeetham, India

Master's Degree:   Computer Science, Florida State University

Work History:   Teaching Assistant Professor in Computer Science at an Engineering College in India

Courses Taught:   Problem Solving & Program Design through C, Artificial Intelligence, Data Mining

Publications:  Kota, Jitendra Sai, Vayelapelli, Mamatha. 2020. "Predicting the Outcome of a T20 Cricket Game Based on the Players' Abilities to Perform Under Pressure". IEIE Transactions on Smart Processing and Computing 9(3):230-237.   DOI: 10.5573/IEIESPC.2020.9.3.230

Professional Objective:   to work in Data Science in a Corporate Environment

ResearchGate

Catrice Taylor

Bachelor's Degree:   Economics, Clemson University 

Master's Degrees:  Applied Economics and Statistics, Clemson University, and Applied Statistics, Kennesaw State University 

Professional Objective:   To work as an industry data scientist in a corporate environment 

Sahar Yarmohammadtoosky

Bachelor's Degree:   Applied Mathematics, Sheikh Bahaei University, Isfahan, Iran 

Master's Degree:   Applied Mathematics, Iran University of Science & Technology, Tehran, Iran

Courses Taught:  Numerical Analysis and Linear Algebra, Iran University of Science & Technology

Publications:   Noah, G., Sahar, Y., Anthony P. & Hung, C.C. "ISODS: An ISODATA-Based Initial Centroid Algorithm". Accepted to: 10th International Conference on Information, March 6 - 8, 2021, Hosei University, Tokyo, Japan

Professional Objective:   My goal is to become a competent Data Science specialist capable of using my skills to bring meaning to data, getting a faculty position at a university

Martin Brown

Graduation Date: Spring 2024

Dissertation: A Holistic and Collaborative Behavioral Health Detection Framework Using Sensitive Police Narratives

Dissertation Advisors: Dr. Dominic Thomas and Dr. Md Abdullah Al Hafiz Khan

Inchan Hwang

Bachelor’s Degree: Computer Science, Georgia Southwestern State University

Master’s Degree: Software Engineering, Ajou University, South Korea

Courses Tutored: Precalculus, College Algebra, Calculus I at Georgia Southwestern State University

Tutoring College Algebra, Calculus I and II at Academic Skills Center, Georgia Southwestern State University Research Assistant at Intelligence of HyperConnected Systems Lab of Ajou University Fullstack web developer, windows system programmer in the cybersecurity industry Professional Objective: To work in big data analytics, and research and development of machine learning in engineering, and security

Duleep Prasanna Rathgamage Don

Bachelor's degree:   Physics and Mathematics, The Open University of Sri Lanka

Master's degree:   Mathematics, Georgia Southern University

• Graduate Teaching Assistant, Georgia Southern University, 2016 - 2018
• Graduate Teaching Assistant, University of Wyoming, 2019 - 2020

Courses Taught:   Trigonometry, and Calculus I & II

Publications/Presentations:

• Don, R. D. and Iacob, I. E., ‘DCSVM: Fast Multi-class Classification using Support Vector Machines’,   International Journal of Machine Learning and Cybernetics .
• Rathgamage Don, D., Iacob, E., ‘Divide and Conquer Support Vector Machine for Multiclass Classification’, Research Symposium (2018), Georgia Southern University.
• Rathgamage Don, D., Iacob, E., ‘Multiclass Classification using Support Vector Machines’, MAA Southeastern Section Meeting (2018), Clemson University.

Professional Objective:   To work in big data analytics, and research and development of machine learning in engineering, and medicine

Linglin Zhang

Bachelor’s Degree:   Biological Sciences, Hubei University, China

Master’s Degree:   Chemical Biology, University of Michigan and Bioinformatics, Georgia Institute of Technology

Selected Publications:   Rebecca Shen, Zhi Li, Linglin Zhang, Yingqi Hua, Min Mao, Zhicong Li, Zhengdong Cai, Yunping Qiu, Jonathan Gryak, Kayvan Najarian. (2018). Osteosarcoma Patients Classification Using Plain X-Rays and Metabolomic Data. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 690-693, 2018.

Professional Objective:  To become a researcher in industry or academia. My background in Biology and Bioinformatics could provide me strong theoretical support on a research role in the health industry. The experience of doing an internship at Equifax equipped me of certain knowledge on business cases. 

Yihong Zhang

Bachelor’s Degree:   Psychology Mathematics Interdisciplinary, Chatham University

Master’s Degree:   Mathematics and Statistics Allied with Computer Science, Georgia State University

• Research Assistant - Collaborated with biomedical department to analyze and visualize microarray gene expression data, Facilitated in data pre-processing and machine learning modeling of clinical liver cirrhosis image data, Assisted in feature engineering of image analysis in deep learning for pathology diagnosis with Mayo Clinic’s pilot project.
• Graduate Lab Assistant - Tutored students with statistics and math subjects.

Professional Objective:   Make better use of data in healthcare and bioinformatic industry as a data scientist.

2019 - 2020

Trent Geisler

Graduation Date:   Summer 2022

Dissertation:   Novel Instance-Level Weighted Loss Function for Imbalanced Learning

Dissertation Advisor:   Dr. Herman Ray

Current Position:   Assistant Professor, Department of Systems Engineering, United States Military Academy West Point

Srivatsa Mallapragada

Dissertation: Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap

Dissertation Advisor: Dr. Ying Xie

Current Position: Data Scientist, Rue Gilt Groupe (RGG)

Sudhashree Sayenju

Graduation Date:   Spring 2023

Dissertation:   Quantification and Mitigation of Various Types of Biases in Deep NLP Models

Dissertation Advisor:   Dr. Ramazan Aygun

Current Position: Lecturer, Data Science and Analytics, Kennesaw State University

Christina Stradwick

Bachelor’s Degree:  Music Performance and Mathematics, Marshall University

Master’s Degree:  Mathematics with Emphasis in Statistics, Marshall University

Courses Taught:  Prep for College Algebra at Marshall University

Selected Presentations:

• Stradwick, C. Exploring the Variance of the Sample Variance. Spring Meeting of the Mathematical Association of America Ohio Section, University of Akron, 2019.
• Stradwick, C., Vaughn, L., Hanan Khan, A. Data Modeling on Insurance Beneficiary Dataset. College of Science Research Expo 2018, Marshall University, 2018. Poster Presentation.
• Stradwick, C. Disease modeling on networks. The 13th Annual UNCG Regional Mathematics and Statistics Conference, University of North Carolina at Greensboro, 2017. Poster Presentation.

Professional Objectives:  To work as a researcher in industry or in a laboratory setting. I would like to use my background in mathematics and statistics to develop novel solutions that address limitations in current data science techniques and to apply known data science methods to solve real-world problems.

2018 - 2019

Md Shafiul Alam

Graduation Date:   Fall 2022

Dissertation:   Appley:   App roximate Shap ley   Values for Model Explainability in Linear Time

Dissertation Advisor:   Dr. Ying Xie

Current Position:   AI Framework Engineer, Intel Corporation

Jonathan Boardman

Dissertation:   Ethical Analytics: A Framework for a Practically-Oriented Sub-Discipline of AI Ethics

Current Position:   Data Scientist, Equifax

Tejaswini Mallavarapu

Bachelor’s Degree:   Pharmacy, Acharya Nagarjuna University, India

Master’s Degree:   Computer Science, Kennesaw State University

• Graduate Research Assistant, Kennesaw State University, 2017-present
• Research Analyst, Divis Laboratories, 2013-2014

Selected Publications:

• T. Mallavarapu, Y. Kim, J.H. Oh, and M. Kang, "R-PathCluster: Identifying Cancer Subtype of Glioblastoma Multiforme Using Pathway-Based Restricted Boltzmann Machine," Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2017), International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics, Accepted, 2017.
• M.R. Shivalingam, K.S.G. Arul Kumaran, D. Jeslin, Ch. MadhusudhanaRao, M. Tejaswini, "Design and Evaluation of Binding Properties of Cassia roxburghii Seed Galacto mannan and Moringa oleifera Gum in the Formulation of Paracetamol Tablets," Research Journal of Pharmacy and Technology(RJPT). 3(1): Jan.-Mar. 2010; Page 254-256.
• M.R. Shivalingam, K.S.G. Arul Kumaran, D. Jeslin, Y.V. Kishore Reddy, M. Tejaswini, Ch. MadhusudhanaRao, V. Tejopavan, "Cassia roxburghii Seed Galacto manna— a potential binding agent in the tablet formulation," Journal of Biomedical Science and Research(JBSR), Vol 2 (1), 2010, 18-22

Professional Objective:   To be a data scientist in the field of health care or bioinformatics where I can leverage my analytical skills and knowledge towards the advancement of the research field.

Seema Sangari

Dissertation:   Debiasing Cyber Incidents - Correcting for Reporting Delays and Under-reporting

Dissertation Advisor:   Dr. Michael Whitman

Current Position:   Principal Modeler, HSB 

Srivarna Settisara Janney

Bachelor’s Degree:   Mechanical Engineering, Visveswaraiah Technological University, India

• Graduate Research Assistant, Kennesaw State University, 2016-2018
• Senior Software Engineer, Torry Harris Business Solutions (THBS), United Kingdom, 2010-2012 and India, 2012-2014
• Software Engineer, Torry Harris Business Solutions (THBS), India, 2007-2010

Selected Publications/Presentations:

• S.S. Janney, S. Chakravarty, “New Algorithms for CS – MRI: WTWTS, DWTS, WDWTS”, One-page research paper, 40th International Conference of IEEE Engineering in Medicine and Biology Society (IEEE EMBC), Jul 2018
• Master thesis presented at Southeast Symposium on Contemporary Engineering Topics (SSCET), UAH Engineering Forum, Alabama, Aug 2018
• Master thesis poster is accepted to be presented at Biomedical Engineering Society (BMES) 2018 Annual Meeting, Oct 2018
• Submitted draft copy for book chapter contribution on “Bioelectronics and Medical Devices”, Elsevier Publisher, May 2018
• Showcased 3MT, Georgia Council of Graduate Schools (GCGS), Apr 2018
• Master thesis presented in workshop for “Medical Signal and Image Processing” at Department of Biotechnology & Medical Engineering, NIT Rourkella, Feb 2018
• S.S. Janney, I. Karim, J. Yang, C.C Hung, Y. Wang, “Monitoring and Assessing Traffic Safety Using Live Video Images”, GDOT project showcase, 4th Annual Transportation Research Expo, Sept 2016
• 1st Place Winner, Graduate Research Project, C-day Poster Presentation, Kennesaw State University, Spring 2018
• People's Choice Award, 3 Minute Thesis (3MT), Apr 2018
• CCSE Dean’s 4.0 Club, Jan 2018
• 3rd Place Winner, Hackathon 2017 - HPCC Systems Big Data
• Foundation of Computer Science, Certified by Kennesaw State University, Jun 2016
• Fundamental of RESTful API Design, Certified by APIGEE, Nov 2014
• Member of HandsOnAtlanta, since 2014
• SOA Associate, Certified by IBM, Jun 2008

Professional Objective:   I would like to be a researcher in Data Science and Analytics in medical imaging technologies contributing to advancements that would help medical and healthcare professionals provide value-based and personalized health care. I would like to look at career opportunities in industry and academia that fuel my interest in research.

2017 - 2018

Graduation Date: Summer 2021

Dissertation: Incentive-based Data Sharing and Exchanging Mechanism Design

Dissertation Advisor: Dr. Meng Han

Current Position: Assistant Professor, Saint Joseph's University - Erivan K. Haub School of Business

Mohammad Masum

Dissertation: Integrated Machine Learning Approaches to Improve Classification Performance and Feature Extraction Process for EEG Dataset

Dissertation Advisor: Dr. Hossain Shahriar

Current Position: Assistant Professor, San Jose State University

Lauren Staples

Graduation Date: Fall 2021

Dissertation: A Distance-Based Clustering Framework for Categorical Time Series: A Case Study in the Episodes of Care Healthcare Delivery System

Dissertation Advisor: Dr. Joseph DeMaio

Current Position: Senior Data Scientist, Microsoft

2016 - 2017

Shashank Hebbar

Dissertation: Tree-BERT - Advanced Representation Learning for Relation Extraction

Current Position: Data Scientist, Credigy

Jessica Rudd

Graduation Date: Summer 2020

Dissertation: Quantitatively Motivated Model Development Framework: Downstream Analysis Effects of Normalization Strategies

Dissertation Advisor: Dr. Herman Ray

Current Position: Senior Data Engineer, Intuit Mailchimp

Graduation Date: Spring 2020

Dissertation: Data-driven Investment Decisions in P2P Lending: Strategies of Integrating Credit Scoring and Profit Scoring

Dissertation Advisor: Dr. Sherry NI

Current Position: Applied Scientist II, Amazon

Dissertation: A Novel Penalized Log-likelihood Function for Class Imbalance Problem

Current Position: Data Scientist/Research Engineer, Hewlett Packard Enterprise

Dissertation: Attack and Defense in Security Analytics

Dissertation Advisor: Dr. Selena He

Current Position: NLP Data Scientist, NBME

2015 - 2016

Edwin Baidoo

Graduation Date:  Spring 2020

Dissertation: A Credit Analysis of the Unbanked and Underbanked: An Argument for Alternative Data

Dissertation Advisor:  Dr. Stefano Mazzotta

Current Position: Assistant Professor, Business Analytics, Tennessee Technological University

Bogdan Gadidov

Graduation Date:  Summer 2019

Dissertation: One- and Two-Step Estimation of Time Variant Parameters and Nonparametric Quantiles

Dissertation Advisor: Dr. Mohammed Chowdhury

Current Position: Data Scientist, Variant

Dissertation:  Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis

Dissertation Advisor:  Dr. Mingon Kang

Current Position:  Assistant Professor, Chinese Academy of Medical Sciences, Peking Union Medical College

Graduation Date:  Spring 2019

Dissertation:  Deep Embedding Kernel

Current Position: Assistant Professor, Information Technology, Kennesaw State University

Bob Venderheyden

Graduation Date: Fall 2019

Dissertation:  Ordinal Hyperplane Loss

Dissertation Advisor:  Dr. Ying Xie

Current Position:  Principal Data Scientist, Microsoft

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Ph.D. in Statistics

Our doctoral program in statistics gives future researchers preparation to teach and lead in academic and industry careers.

Program Description

Degree type.

approximately 5 years

The relatively new Ph.D. in Statistics strives to be an exemplar of graduate training in statistics. Students are exposed to cutting edge statistical methodology through the modern curriculum and have the opportunity to work with multiple faculty members to take a deeper dive into special topics, gain experience in working in interdisciplinary teams and learn research skills through flexible research electives. Graduates of our program are prepared to be leaders in statistics and machine learning in both academia and industry.

The Ph.D. in Statistics is expected to take approximately five years to complete, and students participate as full-time graduate students.  Some students are able to finish the program in four years, but all admitted students are guaranteed five years of financial support.  

Within our program, students learn from global leaders in statistics and data sciences and have:

20 credits of required courses in statistical theory and methods, computation, and applications

18 credits of research electives working with two or more faculty members, elective coursework (optional), and a guided reading course

Dissertation research

Coursework Timeline

Year 1: focus on core learning.

The first year consists of the core courses:

• SDS 384.2 Mathematical Statistics I
• SDS 383C Statistical Modeling I
• SDS 387 Linear Models
• SDS 384.11 Theoretical Statistics
• SDS 383D Statistical Modeling II
• SDS 386D Monte Carlo Methods

In addition to the core courses, students of the first year are expected to participate in SDS 190 Readings in Statistics. This class focuses on learning how to read scientific papers and how to grasp the main ideas, as well as on practicing presentations and getting familiar with important statistics literature.

At the end of the first year, students are expected to take a written preliminary exam. The examination has two purposes: to assess the student’s strengths and weaknesses and to determine whether the student should continue in the Ph.D. program. The exam covers the core material covered in the core courses and it consists of two parts: a 3-hour closed book in-class portion and a take-home applied statistics component. The in-class portion is scheduled at the end of the Spring Semester after final exams (usually late May). The take-home problem is distributed at the end of the in-class exam, with a due-time 24 hours later. 

Year 2: Transitioning from Student to Researcher

In the second year of the program, students take the following courses totaling 9 credit hours each semester:

• Required: SDS 190 Readings in Statistics (1 credit hour)
• Required: SDS 389/489 Research Elective* (3 or 4 credit hours) in which the student engages in independent research under the guidance of a member of the Statistics Graduate Studies Committee
• One or more elective courses selected from approved electives ; and/or
• One or more sections of SDS 289/389/489 Research Elective* (2 to 4 credit hours) in which the student engages in independent research with a member(s) of the Statistics Graduate Studies Committee OR guided readings/self-study in an area of statistics or machine learning. 
• Internship course (0 or 1 credit hour; for international students to obtain Curricular Practical Training; contact Graduate Coordinator for appropriate course options)
• GRS 097 Teaching Assistant Fundamentals or NSC 088L Introduction to Evidence-Based Teaching (0 credit hours; for TA and AI preparation)

* Research electives allow students to explore different advising possibilities by working for a semester with a particular professor. These projects can also serve as the beginning of a dissertation research path. No more than six credit hours of research electives can be taken with a single faculty member in a semester.

Year 3: Advance to Candidacy

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. At the end of the second year or during their third year, students are expected to present their plan of study for the dissertation in an Oral candidacy exam. During this exam, students should demonstrate their research proficiency to their Ph.D. committee members. Students who successfully complete the candidacy exam can apply for admission to candidacy for the Ph.D. once they have completed their required coursework and satisfied departmental requirements. The steps to advance to candidacy are:

• Discuss potential candidacy exam topics with advisor
• Propose Ph.D. committee: the proposed committee must follow the Graduate School and departmental regulations on committee membership for what will become the Ph.D. Dissertation Committee
•   Application for candidacy

Year 4+: Dissertation Completion and Defense

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. Moreover, they are expected to present part of their work in the framework of the department's Ph.D. poster session.

Students who are admitted to candidacy will be expected to complete and defend their Ph.D. thesis before their Ph.D. committee to be awarded the degree. The final examination, which is oral, is administered only after all coursework, research and dissertation requirements have been fulfilled. It is expected that students will be prepared to defend by the end of their fifth year in the doctoral program.

General Information and Expectations for All Ph.D. students

• 2023-24 Student Handbook
• Annual Review At the end of every year (due May 1), students are expected to fill out the Annual Progress Review . 
• Seminar Series All students are expected to attend the SDS Seminar Series
• SDS 189R Course Description (when taken for internship)
• Internship Course Registration form
• Intel Corporation
• Berry Consultants

Attending Conferences 

Students are encouraged to attend conferences to share their work. All research-related travel while in student status require prior authorization.

• Request for Travel Authorization (both domestic and international travel)
• Request for Authorization for International Travel  

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Current Students

• Mendoza Alumni
• The Notre Dame MBA
• Dual Degrees
• Accountancy (MSA)
• Business Analytics (MSBA)
• Finance (MSF)
• Management (MSM)
• Nonprofit Administration (MNA)
• Sports Analytics (MSBA-SA)
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Ph.D. in Analytics

• PhD in Management
• Undergraduate (BBA)

The core mission of the Mendoza PhD in Analytics is to develop thought-leaders in the analytics space that are engaged in impactful, cutting-edge scholarly research that considers the ethical dimension of data and its usage. Graduates of the PhD program are well-positioned to attain academic jobs at top business schools, where they can pursue successful careers in data analytics intensive domains such as business analytics, data science, information systems, operations, and computational social science, conducting research that is impactful and supports human flourishing.

Why Attain a PhD in Analytics?

The PhD degree is intended for those interested in the pursuit of knowledge – creating knowledge through research and disseminating new knowledge to students in the classroom. The field of analytics is without question one that is having a profound impact on business and society. There is a need for new professors capable of pursuing knowledge related to themes such as leadership in an AI-enabled world , ethical human-centered analytics , impactful computational social science , and next generation digital experimentation . These are just a few examples – we encourage our doctoral students to pursue whatever topics they’re passionate about and support them throughout their journey.

Why Notre Dame?

The Department of IT, Analytics, and Operations ( ITAO) is one of the premiere analytics departments, with world-class faculty, cutting-edge research labs, unparalleled industry connections, and access to a large network of Notre Dame alumni that are eager to support analytics thought-leadership.

Faculty Productivity and Reputation

The ITAO department encompasses a diverse set of faculty with significant research capabilities and extensive editorial board experience. ITAO faculty members currently serve in 10+ editorial roles at major journals related to analytics, information systems, and operations; and others have served in similar positions at quality journals previously. In recent years, ITAO faculty have won research awards at top journals and associations such as AIS, INFORMS, POM Society, and the IEEE.

Research Labs and Centers

The ITAO department has multiple analytics-focused research labs, including the Gaming Analytics Lab ( GAMA ) and the Human-centered Analytics Lab ( HAL ). Department faculty are also actively involved with the Notre Dame Technology Ethics Center ( ND-TEC ) and the Lucy Family Institute for Data and Society. Additionally, the Mendoza College of Business has a full-time dedicated data science team that supports data acquisition, collection, and wrangling as part of the Mendoza Behavioral Lab ( MBL ).

Partnerships with Industry

Our faculty routinely collaborate with various industry partners and federal agencies, including Electronic Arts, Ubisoft, eBay, Oracle, and NASA. The department is also actively involved with Notre Dame California ( ND California ), iNDustry Labs , and the Applied Analytics and Emerging Technology Lab (AeTL).

Cutting-edge Curriculum

It is essential that Ph.D. programs equip their graduates with the thorough, current training demanded by today’s market. Our analytics PhD program is well-positioned to produce “T-shaped” scholars that receive a foundation comprising select theories and ethics coursework, and depth via analytics methods courses and seminars. We see an opportunity to develop multi-dimensional scholars well-versed in contemporary analytics methods while also being adept at framing problems, thinking critically about the logic and flow between a problem and proposed solution, and capable of extrapolating their work to the bigger picture.

Institutional Prestige

Notre Dame is a Top 20 US News university with an international reputation and brand. A PhD from Notre Dame therefore sets our graduates up for success in academia at elite private schools or flagship state universities. Some of our graduates are also well-positioned for industry-oriented research roles.

The IT, Analytics, and Operations (ITAO) faculty use contemporary analytics methods such as machine learning, econometrics, statistics, and analytical modeling to study an array of research topics including ethics and privacy, health, sports and gaming, AI business applications, digital experimentation methods, and e-commerce:

• Ahmed Abbasi (AI, machine learning, text analytics, user modeling)
• Corey Angst (health analytics, ethics, privacy, security)
• Nicholas Berente (digital innovation, managing AI, institutional change)
• Francis Bilson Darku (sequential analysis, nonparametric statistics, econometrics)
• Jeff Cai (statistical learning, network analysis, data science)
• Sarv Devaraj (business analytics, healthcare management, supply chains)
• Rob Easley (economic modeling, Internet auctions, e-commerce)
• Ken Kelley (psychometrics, statistical methods, human-centered analytics)
• John Lalor (machine learning, natural language processing)
• Junghee Lee (innovation/technology in supply chains, healthcare operations)
• Kirsten Martin (technology ethics, privacy, business responsibility)
• Alfonso Pedraza-Martinez (humanitarian operations, disaster management, analytical modeling)
• Xinxue (Shawn) Qu (innovation diffusion, data management, predictive analytics)
• Sriram Somanchi (machine learning, event and pattern detection)
• Yoon Seock Son (econometrics, mobile strategy, AI business strategies)
• Daewon Sun (pricing strategies, resource management, economics of IS)
• Margaret Traeger (computational social science, social networks, health analytics)
• Katie Wowak (supply chains, traceability in global networks)
• Yang Yang (machine learning, network analysis, computational social science)
• Zifeng Zhao (statistical methods, large-scale forecasting, risk monitoring)

Program Structure

The program is designed to be five-years, full-time, in-residence. Click below for a year-by-year breakdown of how the program is structured.

In the first year, you will learn foundational theories, concepts, and methods related to analytics. ITAO seminar courses will include Human-centered AI, Philosophy of Science, and Computational Social Science. Methods related coursework will include classes related to machine learning, data science, statistics, and/or econometrics. Based on prior coursework, some students might be able to opt out of certain courses. In consultation with the program director, you will form a plan of study for methods courses and electives that align with your research interests.

At the beginning of the first year, you will also be assigned a faculty mentor that will guide your efforts related to the first-year research paper – the purpose of the first-year paper is to demonstrate the potential to produce high-quality scholarly manuscripts.

In year 2, you will continue to broaden and deepen your understanding of the analytics space with ITAO seminars related to Human-centered Statistics, Mathematical Modeling for Consumer Analytics, Operations and Prescriptive Analytics, and Data and Technology Ethics. At the end of the second year, you will have an examination requirement (in the form of an exam or paper). This examination will test your knowledge of ITAO seminar courses taken over the first two years. Your second-year faculty mentor will offer guidance on the paper.

You will wrap up any remaining coursework and turn your attention to pushing research projects towards publication.

In addition to managing your research portfolio, you’ll focus on finalizing your dissertation topic and defending your proposal.

The final year will involve interviewing for open positions, completing dissertation chapters, and having your final defense. And then, onward and upward into your exciting new career!

Marialena Bevilacqua received a BA in Math with a minor in Statistics from the College of Holy Cross in Massachusetts, where she was class president and captain of the field hockey and lacrosse teams. She attained an MS in Business Analytics from Georgetown University. Marialena was a brand operations analyst and manager plus “rookie of the year” at Thrasio.

Ryan Cook received a BS in Analytics with a minor in Philosophy from Notre Dame, and an MS in Computer Science from the University of Pennsylvania. He worked as a research scientist in Notre Dame’s Human-centered Analytics Lab and Center for Computer Assisted Synthesis, supporting projects related to NLP and network analysis. Ryan was also previously an analyst at EY in Chicago.

Jiehui Luo attained dual bachelor’s degrees from Dartmouth College (Computer Engineering) and Mount Holyoke College (Computer Science with a minor in Math). She received her MPhil in Computer Science and Engineering from HKUST. Her research interests relate to human-computer interaction and human-centered AI. Jiehui was previously a product manager at Tencent.

Alyona Nefedova majored in Math at the Higher School of Economics. Her thesis examined classification and discovery of R-matrices. She received the Governor’s medal for academic excellence. Alyona explored trading models at the Center for Mathematical Finance, and assisted with cognitive science projects at the fMRI Lab. She previously taught middle school math and volunteers at Canada/USA Mathcamp.

Kezia Oketch attained a BS in Computer Science from Spelman College and an MS in Engineering, Science, and Technology Entrepreneurship from Notre Dame. She was a Gold Scholar at the Grace Hopper Conference and co-founded a research startup focused on technology-based solutions to the cancer crisis in Kenya. Kezia was also a software engineer at an Ohio-based tech company.

Sunan Qian double majored in Economics and Math, and minored in French, at Mount Holyoke College. She received an MS in Finance with a minor in Quantitative Methods from Carnegie Mellon University – her thesis explored the impact of environmental regulation on firms’ carbon emissions and market value. Sunan was a digital consultant for Accenture in Tokyo.

Will Stamey was a double major in Economics and Math at Baylor University, with a minor in Philosophy. He was a Baylor Fellow and Crane Scholar, and completed the health economics sequence. Will’s honors thesis explored the impact of online education on academic outcomes. He was also a researcher at the Colorado Summer Institute in Biostatistics.

Becky Zhang double majored in Computer Science & Economics, and Applied Math, at Washington University in St. Louis. She received an MS in Computational and Applied Math from the University of Chicago – her thesis explored methods for stochastic non-linear optimization. Becky’s industry experience includes internships as a research analyst and data scientist at major software and technology companies.

Xinyuan Zhang completed her undergraduate coursework from the University of Sydney, where she double majored in Finance and Statistics and researched sentiment analysis in the Computing Finance Lab. Xinyuan received an MS in Statistics from UCLA – her thesis explored preference models for two-sided platforms. She was also a researcher in the Trusted AI Systems Lab at Nankai University.

As Director of the Analytics PhD program, I’m happy to answer any questions you might have about our program (or a PhD in general). Feel free to email me at [email protected].  I look forward to connecting with you.

Our application deadline for the Fall 2025 incoming cohort is January 7, 2025 . You can apply using the “APPLY” button in the side menu (also appearing in the menu at the top of the page).

Ahmed Abbasi

Frequently Asked Questions

All students who are admitted to the program will be given a full tuition waiver. So the program is essentially tuition-free, with the only direct costs being miscellaneous university fees. In addition, all PhD students are paid a stipend of $42,000 a year. That stipend serves as compensation for your research activities (and for the teaching you would do in years three and four).

We require either the GRE or the GMAT, and have no preference between the two. If you’ve previously taken one of those tests, we require a score that is less than five years old. Unfortunately, the admissions committee will not waive the GRE/GMAT requirement under any circumstances.

It’s hard to say, as that is a function of a given application cycle, along with the rest of an applicant’s admissions portfolio. Most years, however, verbal and quantitative percentiles in the 80’s or above will be needed to advance to the short list.

Yes, if English is not your native language, or if English was not your language of college instruction. We accept either the TOEFL or the IELTS. If you’ve previously taken one of those tests, we require a score that is less than two years old.

You’ll fill out an online application form that will be linked on this site. And you’ll provide your resume, a statement of purpose/intent, three letters of recommendation, and unofficial transcripts of college (and any masters) degrees.

No. This sort of degree is best thought of as a research apprenticeship—where you are learning research skills in collaboration with faculty. That sort of collaboration requires a full-time, five-year, in-residence commitment.

Yes. While the program will prepare graduates to work in teaching institutions, government, and industry, the priority will be to prepare students for faculty roles so that they can be thought-leaders involved in teaching the next generation of analytics students and working to advance analytics-oriented research. Typically 80-90% of PhDs in Analytics take academic positions, while 10-20% pursue careers in industry (e.g., Silicon Valley, Wall Street, Think Tanks, etc.).

No. This is—first and foremost—a research degree. Teaching is part of the degree, as teaching is an important part of a professor’s career. But, if teaching or administration are your main focus, you might do a search for teaching-oriented PhD programs or Doctor of Business Administration (DBA) programs, which are sometimes also called Executive Doctorate programs.

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Doctor of Philosophy (PhD) in Business Analytics and Data Science

• Request Information

Earn a doctorate degree in business analytics and data science, advance both your scholarly research and your career

In a competitive world, success depends on finding and maintaining an edge – and that requires making data-driven decisions. Organizations in an ever-expanding array of industries – from sports, to health care, to cybersecurity – are seeking out professionals with the expertise needed to utilize data effectively.

The Ph.D. in Business Analytics and Data Science program is designed to prepare accomplished professionals for senior positions in either public or private sectors. The mission of the Doctor of Philosophy in Business Analytics and Data Science degree is to enable professionals from the field to understand and evaluate the scope and impact of decision sciences and associated technology from the institutional as well as from an industry and global perspective. The program will provide the student an academic environment to support the development of high-level critical thinking and leadership skills as they relate to management and decision sciences, to develop high-level decision science technical skills, and to provide doctoral level research experience allowing innovative and practical contributions to the management and decision sciences body of knowledge.

Why Capitol?

Learn around your busy schedule

Our low residency requirement takes in consideration the time commitment of your established career.

Online format

The majority of our business analytics and data science doctorate is offered online.

Our classes are taught by working professionals

Capitol’s faculty are working professionals in the field – subject matter experts who apply their knowledge on a daily basis and are up to speed on emerging developments.

Key Faculty

Adjunct Professor

Dissertation Chair

Career Opportunities

Market demand for business analytics and data science (PhD)

You’ll have the necessary credentials to lead local, national or global organizations & provide expert guidance on using organizational information assets.

A degree that is relevant to any and every organization

This doctorate will teach how analytics helps shape strategic plans in whatever business you are involved in.

Degree Details

This program may be completed with a minimum of 60 credit hours, but may require additional credit hours, depending on the time required to complete the dissertation/publication research. Students who are not prepared to defend after completion of the 60 credits will be required to enroll in RSC-899, a one-credit, eight-week continuation course. Students are required to be continuously enrolled/registered in the RSC-899 course until they successfully complete their dissertation defense/exegesis.

The Doctorate degree in Business Analytics and Data Science is a total of 54-66 credits, which covers a literature review, professional research and theory, professional ethics and leadership, dissertation preparation, and other topics. Students can select from several electives based on their personal focus.

Doctor of Philosophy - 54 credits

Please check with an Academic Advisor to confirm degree requirements and program electives.

Educational Objectives:

• Students will integrate alternate, divergent, or contradictory perspectives or ideas fully within Decision Science.
• Students will present scholarly data presentations via appropriate communication channels.
• Students will demonstrate advanced knowledge and competencies in data handling.
• Students will analyze various information to draw data-supported conclusions.
• Students will execute a plan to complete a significant piece of scholarly research in data analytics.
• Students will synthesize various sources of data to produce robust conclusions.
• Students will apply data analysis to determine the validity of data
• Students will compare data to determine the trends and spurious results

Learning Outcomes:

Upon graduation:

• Graduates will evaluate the legal, social, economic, environmental, and ethical impact of actions within business and data science, and demonstrate advanced knowledge and competency to integrate the results in the leadership decision-making process.
• Graduates will demonstrate the highest mastery of traditional and technological techniques of communicating ideas effectively and persuasively.
• Graduates will evaluate complex problems, synthesize divergent/alternative/contradictory perspectives and ideas fully, and develop advanced solutions to business analytic and decision science challenges.
• Graduates will contribute to the body of knowledge through data analysis.
• Graduates will apply leading edge theories to data analysis.
• Graduates will disseminate knowledge to their peers from research.
• Graduates will lead the development of data analysis to the broader community.

Tuition & Fees

Tuition rates are subject to change.

The following rates are in effect for the 2024-2025 academic year, beginning in Fall 2024 and continuing through Summer 2025:

• The application fee is $100
• The per-credit charge for doctorate courses is $950. This is the same for in-state and out-of-state students.
• Retired military receive a $50 per credit hour tuition discount
• Active duty military receive a $100 per credit hour tuition discount for doctorate level coursework.
• Information technology fee $40 per credit hour.
• High School and Community College full-time faculty and full-time staff receive a 20% discount on tuition for doctoral programs.

Find additional information for 2024-2025 doctorate tuition and fees.

I was drawn to the practical and goal-oriented approach, which also seemed like the natural next step in my academic career. Ultimately, my aim is to pursue teaching and research work in the future.

-James Tankard PhD in Business Analytics and Data Science

Need more info, or ready to apply?

Big Data Analytics Ph.D. Program

First ph.d. of its kind in the state of florida and only one of a few across the globe..

The Big Data Analytics Ph.D. program aims to train students and researchers to analyze massive structured and unstructured data and uncover hidden patterns, actionable associations and other useful information for better decision making. This program combines the strength of statistical science, data science and machine learning.

The Ph.D. program intends to prepare students to fill the need for skilled positions, including leadership positions, in business and industry, as well as for positions in academia to conduct research and teach data analytics at the graduate level. Our award-winning Data Mining Program, the nation’s oldest data mining program, offers an established educational environment complemented with ongoing industrial collaborations with industrial clients such as the Walt Disney Company, the CFE Federal Credit Union, Sodexo CitiGroup Inc, Johnson & Johnson.

Upon successful completion of this program, our students will have advanced knowledge in data management, algorithm development, inferential statistics and predictive analytics to deal with big data problems.

Curriculum includes:

• Big data architecture, such as distributed storage and processing
• Apache Hadoop
• Cloud storage and computing
• Parallel processing
• Programming languages such as SAS, R and Python
• Interpreting and communicating findings

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Master of Science

Online Master's in Data Analytics

Lead the analytics lifecycle with a master's degree in data analytics.

Make your next career move and maximize your earning potential with WGU’s Master of Science, Data Analytics. This online degree program provides a depth of skills for your data analytics career in the following areas:

• Data Analytics Lifecycle 
• Data Science and Advanced Analytics Applications
• Data Mining Techniques
• Machine Learning 
• Data Management
• Business Influence

We use cutting-edge technology to help you learn about machine learning, modern analytic tools & languages (Python, R, SQL, and Tableau) , and more! Additionally, this unique program allows you to choose from a range of datasets representing different industry-specific themes. Learn exactly what you want and need for your career!

Choose Your Specialization

The WGU data analytics program features 3 specialization options for students to choose from. The concentration areas allow students to dive deeper into their area of interest, better preparing them for their career goals. 

• Data Science:  This specialization focuses on machine learning, optimization of data, and advanced analytics understanding.
• Data Engineering:  This specialization focuses on cloud engineering, the processing of data, and analytics at scale.
• Decision Process Engineering: This concentration focuses on project management, process optimization, and decision intelligence related to data analytics.

At WGU, we use a ‘three-lever’ approach to data analytics, surgically incorporating programming, math, and business influence skills throughout the program. Different roles in analytics will have different combinations of these levers. You will navigate to the role that best fits your interests & passions by selecting the concentration of your choice. Our game-changing approach to data analytics means that you will be equipped with the experience you need to create change and make an impact in any industry.

NOTE: The Data Analytics master's degree is currently NOT available to students who have a permanent residence in Missouri while the accreditation is pending approval.

Unsure Which Specialization to Choose?

• The data science specialization  is ideal for those who want to focus on statistical and programming approaches in machine learning, neural networks, and numerical optimization. 
• The data engineering specialization  is ideal for those who want to support analytics through cloud-native databases, data processing, and analysis approaches. 
• The decision process engineering specialization is ideal for those who want to implement business changes through project management, business process engineering, and decision intelligence. 

This data analytics degree program focuses on both theory and application , allowing you to “learn by doing” as you complete data analytics projects in stages, known as the data analytics lifecycle .

The Master of Science in Data Analytics program's core courses dive deeply into each of these lifecycle stages  (sourcing data, cleaning data, data mining, descriptive & predictive analytics, and visualization). 

70% of graduates finish within 

WGU lets you move more quickly through material you already know and advance as soon as you're ready. The result: You may finish faster.

*WGU Internal Data

Tuition per six-month term is

Tuition charged per term—rather than per credit—helps students control the ultimate cost of their degree. Finish faster, pay less!

Certifications that may transfer

Your Oracle SQL Expert certification may waive course requirements.

Ready to Start Your WGU Journey?

Next Start Date: August 1

Start Dates the 1st of Every Month

Data Analytics Courses

Program consists of 11 courses

At WGU, we design our curriculum to be timely, relevant, and practical—all to help you show that you know your stuff.

The WGU M.S. Data Analytics degree program was designed, and is regularly updated, with input from the experts on our Data Analytics Advisory Board. This ensures that you learn best practices for the latest developments in data analytics.

This data analytics degree program is composed of the following courses. You will typically complete them one at a time as you make your way through your program, working with your Program Mentor each term to build your personalized Degree Plan. You’ll work through each course as quickly as you can study and learn the material. As soon as you’re ready, you’ll pass the assessment, complete the course, and move on. This means you can finish as many courses as you're able in a term at no additional cost.

The M.S. Data Analytics degree program is an all-online program that you will complete through independent study with the support of WGU faculty. You will be expected to complete at least 8 competency units (WGU's equivalent of the credit hour) each 6-month term. (Each course is typically 3 or 4 units). There’s no limit on the number of units you can complete each term, so the more courses you complete, the quicker you can finish your program.

This program features 3 specializations for you to choose from, with unique courses and capstone in each focus area. This allows you to gain specific skills and gain experience in your chosen area, preparing you to enhance your résumé and meet your career goals. 

Foundational Courses Taken Across All Specializations

Analytics is the creative use of data and statistical modeling to tell a compelling story that not only drives strategic action but also results in business value. The Data Analytics Journey uses the analytics life cycle to conceptualize the processes, tools, and techniques for implementing data analysis, data engineering, and analytics product management. Learners gain fluency in gathering requirements, asking business questions, establishing evaluation metrics, identifying communication models, and aligning the analytics project outcomes to business goals. It presents an overview of the various tracks offered in the program and the career options in these specializations.

Data Management builds proficiency in using both relational and non-relational databases. Topics include selection of a data storage architecture, data types, data structures, normalization and denormalization, and querying databases. Structured Query Language (SQL) topics including Data Definition Language (DDL) and Data Manipulation Language (DML) are covered, including joins, aggregations, and transactions. Non-relational approaches to organizing and querying data are contrasted with relational approaches to build competency in adapting data storage architectures to business needs.

Analytics Programming builds algorithmic thinking using both the Python and R programming languages. This course builds from the foundations of programming. Learners use libraries and packages to perform common analytics tasks, including acquiring, organizing, and manipulating datasets. The course also presents methods for applying statistical functions and graphical user interfaces to perform basic analysis and to present findings.

Data Preparation and Exploration applies analytical programming skills to the early steps of the data analytics life cycle. This course covers cleaning data to ensure the structure, accuracy, and quality of the data; interpretation of descriptive and inferential statistics as well as visualizations of data; and wrangling data to prepare it for further analysis. The course introduces hypothesis testing, focusing on application for parametric tests, and addresses communication skills and tools to explain an analyst’s findings to others within an organization. The following courses are prerequisites: The Data Analytics Journey, Data Management, and Analytics Programming.

Statistical Data Mining focuses on concepts in data preparation and supervised and unsupervised machine learning techniques. The course helps students gain basic knowledge in statistics, data preparation, regression, and dimensional reduction. Learners implement supervised models—specifically classification and prediction data mining models—to unearth relationships among variables that are not apparent with more surface-level techniques. The course also explains when, how, and why to use unsupervised models to best meet organizational needs. The following course is prerequisite: Data Preparation and Exploration.

Data Storytelling for Diverse Audiences focuses on communicating observations and patterns to diverse stakeholders, a key aspect of the data analytics life cycle. This course helps learners gain communication and storytelling skills in order to motivate change and answer business problems. It also covers data visualizations, audio representations, interactive dashboards, interpersonal communication, and presentation skills.

Deployment is the practice of operationalizing data analysis within a business environment. Given an analysis, learners determine the business functional and non-functional requirements for wider use and implement pipelines and functions to deploy analyses at scale. Topics including security, scalability, usability, and availability are discussed. Prerequisites for this course are Analytical Programming, Data Management, Data Preparation, and Statistical Data Mining.

Courses in the Data Science Specialization

Advanced Analytics extends analytics techniques from machine learning to artificial intelligence more broadly, including topics in neural networks, deep learning, and natural language processing. The course covers approaches to developing these models including PyTorch and TensorFlow. Students learn to apply a combination of techniques to solve complex business challenges including computer vision and sentiment analysis.

Optimization is a large class of business problems requiring the iterative algorithmic maximization or minimization or one or more variables. Students in this course will select and use a variety of optimization approaches to address various business needs. The course covers classes of optimization problems at a foundational level (continuous/discrete, linear/nonlinear, and bounded/unbounded) and the solving of linear optimization problems in both Python and R through the use of gradient and non-gradient-based algorithms. Analytics Programming is a prerequisite. 

Machine Learning is the broad discipline of developing algorithms and statistical models to predict, classify, or cluster data and that iteratively improve over time. Machine Learning focuses on building, training, running, and testing supervised and unsupervised models and quantifying the accuracy and precision of those models to determine which may best be used in a particular business situation. Supervised methods covered include k-nearest neighbors, logistic regression, decision trees, and support vector machines. Unsupervised models covered include k-means clustering, hierarchical clustering, and t-distributed stochastic neighbor embedding (t-SNE). Ensemble methods are also presented. Prerequisites are Analytics Programming and Statistical Data Mining.

The Data Science Capstone integrates the learning in the MSDA core and the three courses within the specialization. The student evaluates various needs and opportunities in an organization or marketplace; identifies the business requirements; translates the business requirements into technical requirements; and creates a comprehensive project plan to solve the problem in a way that satisfies the customer or business needs. Projects within this specialization include the design and construction of machine learning approaches, optimization, and/or advanced analytics techniques as the project requires.

Courses in the Data Engineering Specialization

Cloud Databases covers the application of cloud architectures to large-scale data systems. The differences between cloud-native approaches to data architectures and smaller scale systems are discussed and learners apply cloud computing concepts to address specific business scenarios.

The Data Engineering Capstone has learners utilize the skills learned throughout the MDSA core courses and the data engineering courses to examine a problem where data engineering is a solution and to build a cloud-native infrastructure that allows for data processing. Learners are asked to implement their solutions and tell a story using the data. Course material introduces the project and reminds learners of relevant learning resources from previous courses that will prove helpful in completing the performance assessment.

Data Analytics at Scale builds on previous data engineering courses and discusses approaches for analyzing large data sets. The course discusses map/reduce approaches, Apache Spark, and cloud–native solutions for developing, automating, and scaling data analytics. Also discussed are methods for integrating data processing pipelines and data stores to create comprehensive data analytics architectures.

Data Processing is the practice of automating data flow into and out of components of an analytics system and comprises a major part of the analytics life cycle in modern organizations. Data Processing covers concepts in Extract, Transform, and Load (ETL) pipeline operations on data at scale and variations of ETL (Extract, Transform, and Load) as a function of data repositories including data warehouses and data lakes. Streaming and batch data operations and their differences are discussed, and learners implement pipeline solutions in cloud-native environments.

Courses in the Decision Process Engineering Specialization

Processes form the core of any organization and involve both manual and automated steps. Business Process Engineering introduces how to identify processes, visualize them, and how to design and implement operational methods that promote organizations’ overall efficiency. The course covers common process engineering frameworks, the stages of process engineering present in common frameworks, and introduces tools used to conduct business process reengineering.

Decision Intelligence is a domain that optimizes decision-making by balancing technology, processes, and people. In this course students learn the core principles of Decision Intelligence, exploring the augmentation of decision processes with machine learning, comprehensive decision modeling, and the pivotal role of a “human-in-the-loop” design.  Students will navigate decision theories and multi-criteria decision analysis, gaining insight into how biases and heuristics influence decision outcomes. The course emphasizes framing decisions using causal decision diagrams (CDD), implementing decision intelligence, evaluating the outcome using key performance indicators and determining the return on investment of the change, and using change management techniques to help the organization adapt to new decision making strategies.

The Decision Process Engineering capstone integrates the learning in the MSDA core and the three courses within the specialization. The learner evaluates various needs and opportunities in an organization or marketplace; identifies the business requirements; translates the business requirements into technical requirements; and creates a comprehensive project plan to solve the problem in a way that satisfies the customer or business needs.  Projects within this specialization include a project management plan, decision intelligence plan, or process engineering plan to deliver on the business need or opportunity.

Project Management is a thorough exploration of the inputs, tools, techniques, and outputs across the five process groups and 10 knowledge areas identified in the Project Management Body of Knowledge (PMBOK) Guide. The essential concepts and practical scenarios included enable students to build the competencies required to successfully complete the CAPM certification exam. There is no prerequisite for this course.

At WGU, we design our curriculum to be timely, relevant, and practical—all to help you show that you are competent in your area of study. In this program you will have unique course options depending on your specialization choice.

Capstone Project

Special requirements for this program

At the end of your unique program, you will complete a capstone project that represents the culmination of all your hard work—this project consists of a technical work proposal, the proposal’s implementation, and a post-implementation report that describes the graduate’s experience. 

Skills For Your Résumé

As part of this program, you will develop a range of valuable skills that employers are looking for. 

• Communication: Utilized storytelling techniques to effectively influence, inform, or motivate target audiences.
• Python (Programming Language) : Developed modular scripts using Python.
• SQL (Programming Language): Manipulated data effectively using structured query language (SQL) statements, facilitating data retrieval, manipulation, and analysis.
• Data Analysis: Proposed innovative data analytics approaches for resolving complex problems, leveraging data-driven insights to inform decision-making and achieve desired outcomes.
• Critical Thinking: Applied logical reasoning skills to address real-world problem-based inquiries.
• Data Engineering: Engineered efficient and scalable data pipeline solutions.

“I just completed my master's program at WGU, and overall I had a very positive experience. The online degree program was extremely flexible, and that was critical for my success in the program because of other work and family demands. The program mentors were just as flexible and provided helpful guidance along the way, and I feel like I learned a lot during the program. Also, the cost for the program was very reasonable, and they even worked with my employer to provide a tuition discount and deferral program. I would definitely recommend WGU!!”

—Andrew M.S. Data Analytics

WGU vs. Traditional Universities Compare the Difference

Traditional Universities

AVG. cost For 3RD PARTY IT CERTIFICATIONS

Included with your tuition cost

TUITION STRUCTURE

Per credit hour

Flat rate per 6-month term

Schedule and wait days or even weeks to meet with one of many counselors

Simply email or call to connect with your designated Program Mentor who supports you from day one

Scheduled time

Whenever you feel ready

Professor led lectures at a certain time and place

Courses available anytime, from anywhere

TIME TO FINISH

Approximately 2 years, minimal acceleration options

As quickly as you can master the material, typically less than 2 years

*The cost of valuable industry certification exams can range from $150 to $400. At WGU, we offer vouchers for certification exams, so the cost is included in your tuition price. Students may have to pay some fees for membership to complete their certification requirements.

Earning Potential

According to a 2023 Harris Poll , just two years after graduation, WGU grads report earning $22,200 more per year, and that amount increases to $30,300 four years after graduation.

On Your Schedule

No class times, no assignment deadlines. You are in charge of your learning and schedule. You can move through your courses as quickly as you master the material, meaning you can graduate faster.  

Entirely Online

The data analytics master's degree at WGU is 100% online, which means it works wherever you are. You can do your coursework at night after working at your full-time job, on weekends, while you're traveling the world or on vacation—it's entirely up to you.

Accredited, Respected, Recognized™

One important measure of a degree’s value is the reputation of the university where it was earned. When employers, industry leaders, and academic experts hold your alma mater in high esteem, you reap the benefits of that respect. WGU is a pioneer in reinventing higher education for the 21st century, and our quality has been recognized.

“Analytics is the creative use of data and statistical modeling to tell a compelling story that not only drives strategic action, but also results in business value”

-Joe Dery, Dean of Data Analytics, Computer Science, and Software Engineering | WGU

CERTIFICATIONS

Data Analytics Certificates Included in this Degree

Completing this degree program includes several WGU certificates, including one in each of our specialization areas. Earning certificates on the path to your degree gives you the knowledge, skills, and credentials that will immediately boost your résumé—even before you complete your degree program.

• Data Analytics Professional Certificate

• Data Operations Certificate

• Data Science Professional Specialization (in the Data Science concentration)

• Decision Process Engineering Professional Specialization (in the Decision Process Engineering concentration)

• Data Engineering Professional Specialization (in the Data Engineering concentration)

COST & TIME

An Affordable Online Data Analytics Degree

By charging per six-month term rather than per credit—and empowering students to accelerate through material they know well or learn quickly—WGU helps students control the ultimate cost of their degrees. The faster you complete your program, the less you pay for your degree.

A College Degree Within Reach

There is help available to make paying for school possible for you:

The average student loan debt of WGU graduates in 2022 (among those who borrowed) was less than half* the national average.

Most WGU students qualify for financial aid, and WGU is approved for federal financial aid and U.S. veterans benefits. 

Many scholarship opportunities are available. Find out what you might be eligible for.

* WGU undergraduate students have approximately half the debt at graduation compared to the national average, according to the Institute for College Access and Success (2022).

FLEXIBLE SCHEDULE

A Different Way to Learn: Degree Programs Designed to Fit Your Life—and All the Demands on Your Time

Professional responsibilities. Family obligations. Personal commitments. At WGU, we understand schedules are tight and often unpredictable for adult students. That’s why we offer a flexible, personalized approach to how education should be. No rigid class schedules. Just a solid, career-focused teaching program that meshes with your current lifestyle. You'll be challenged. You'll work hard. But if you commit yourself and put in the hours needed, WGU makes it possible for you to earn a highly respected degree as a busy working adult.

"I have dreamed about completing my master's degree for many years now and it became a reality only because of WGU. I am so thankful to WGU administration. Many thanks to my mentors and course instructors along the way. Thank you so much WGU!!!”

—Naga Satya Srivani M.S. Data Analytics

CAREER OUTLOOK

Data Leadership Careers Start with a Master's Degree in Data Analytics

When properly analyzed, every transaction—commercial, medical, social, or academic—can help lead to better business decisions and outcomes in your industry. WGU is key in helping you gain the critical skills and experience you need to thrive in your professional sector. Increase your earning potential, boost your résumé with valuable credentials, and find a career you love with the help of a data analytics master's degree.

From healthcare to entertainment, every industry is going digital. Organizations need experts who can maximize that data. When you’ve completed your data analytics degree program online, your skills will already be in high demand . The knowledge and techniques you’ll gain as you complete your degree will provide you with all the tools necessary for a successful career.

Return on Your Investment

On average, wgu graduates see an increase in income post-graduation.

Average income increase from all degrees in annual salary vs. pre-enrollment salary. Source:  2023 Harris Poll Survey  of 1,655 WGU graduates.

Survey was sent to a representative sample of WGU graduates from all colleges. Respondents received at least one WGU degree since 2017.

The number of positions for data scientists is projected to grow by an astounding 36% from 2021 to 2031.

—U.S. Bureau of Labor Statistics

Learn About All the Job Opportunities in Data Analytics

Students who earn a master’s degree in data analytics will be prepared to maximize leadership opportunities in a variety of careers. Choosing a specialization will allow students to focus on their specific career goals, gaining skills and experience that will prepare them to meet industry needs. Learn more about specific career opportunities within each specialization.

• Data Scientist
• Data Analyst
• Machine Learning Data Scientist
• Machine Learning Engineer
• Optimization Analyst
• Data Analytics Architect
• Analytics Engineer
• Data Quality Analyst
• Data Engineer 
• Business Analyst
• Data Analytics Consultant
• Decision Analyst
• Program Management Analyst

WGU Grads Hold Positions With Top Employers

Data analytics admissions requirements.

To be considered for enrollment in this program, you must:

1. Possess a bachelor’s degree in a STEM field (see refined list)

2. Possess any bachelor’s degree plus ONE of the following:

• Completed college-level coursework in statistics and computer programming with a grade of B- or better
• At least two years of work experience in a data analytics, data science, data engineering, or database administration role
• CompTIA Data+
• DASCA Associate Big Data Engineer
• DASCA Senior Big Data Engineer
• Udacity Data Analyst Nanodegree
• Udacity Data Scientist Nanodegree
• Udacity Data Engineering with AWS Nanodegree
• AWS Certified Data Analyst
• Associate Certified Analytics Professional (aCAP)
• Certified Analytics Professional (CAP)
• Cloudera Data Platform (CDP) Data Analyst
• Microsoft Certified Data Science Associate
• SAS Certified Advanced Analytics Professional

NOTE: You do not need to take the GRE or GMAT to be admitted to this program.  Learn why we don't require these tests.

Get Your Enrollment Checklist

Download your step-by-step guide to enrollment.

Get Your Questions Answered

Talk to an WGU Enrollment Counselor.

Transfer Credits

FAQs about Master's in Data Analytics Programs

• General IT Program Questions
• Master in Data Analytics Questions
• Data Analytics Advisory Board

What if I can't meet the eligibility requirements to enroll in the IT program I am interested in?

You should speak with an Enrollment Counselor. WGU can often provide advice or resources to help a prospective student fulfill enrollment prerequisites.

Why are certifications and other prerequisites required?

When you enroll in a WGU degree program, our goal is to see you through to graduation. Admission requirements are designed to increase your likelihood of success. Years of data and experience with the nontraditional students WGU serves have shown us how various types of academic and professional experience can be highly important in helping a student persist to graduation. Industry certifications are one of many ways a student can meet eligibility.

Why doesn't WGU accept certifications that are older than five years?

WGU has an obligation to our graduates—and their current and future employers—to ensure WGU alumni have mastered the most up-to-date, current competencies and skills needed in the workplace. Recency of certifications helps us ensure that students have demonstrated competency in skills as they are needed in today's working world.

Is this program truly "at your own pace"?

As a full-time student, you will be required to maintain a minimum pace of 12 competency units (CUs) per term for undergraduate programs or 8 CUs per term for graduate programs. However, there is no maximum speed—once you complete a course, you move immediately to the next, and you complete a course not by waiting for the syllabus, the professor, or the rest of the class. You progress by learning the material and proving it—so you can move through your coursework at the speed of your own learning and experience.

If there aren't classes or lectures, what role do Instructors play?

Instructors are highly educated, experienced experts in the subject matter of a course. Unlike in a traditional university where going to class means listening to an instructor lecture while you take notes and try to learn in a group setting, WGU's Instructors provide one-on-one instruction and support when you need it—tailoring the instruction to your precise needs when you need it. Instructors also provide additional resources, lead topical discussions in online communities, and find countless other ways to bring a specific course to life for students.

Are master's in data analytics worth it?

Absolutely. Data makes our world go round, and every business in every industry needs data to help make decisions. You can work in any field with the help of a degree in data analytics. A master's in data analytics will prepare you to convert raw data into meaningful information that can help business leaders make decisions. Become a great influence with the help of an master's degree in data analytics.

What is MS in data analytics?

An MS in data analytics is a Master of Science in Data Analytics. This degree helps students learn how to do the many tasks associated with taking raw data and turning it into meaningful information. This includes data mining, programming, analyzation, and more.

Will a master’s degree in data science help to start my career?

A master's degree in data science or data analytics is a great option for IT professionals who are looking to take their career to the next level. Experience in the IT field will be extremely helpful as you pursue a master's degree in data analytics. If you're looking to start a career in IT, a bachelor's degree or certifications can help you begin.

What jobs can you get with master's in data analytics?

Common careers for those with a master's degree in data analytics include:

• Data analyst
• Business analyst
• Data engineer
• Business intelligence analyst
• Information research scientist
• Advanced analytics expert

Is MS in Data Science tough?

A master's degree in data science or data analytics can be challenging, but if you have a solid background and understanding in IT, you'll be able to excel. Experience, education, and certifications in IT can help you be better prepared for this master's degree.

Data Analytics Advisory Board Members

• Boaz Hillebrand,  Senior Data Manager, Talent Acquisition – Expedia Group
• John Smits , VP of Worldwide Revenue Operations – Juniper Networks
• Ken Yu Zhang, PhD , Executive Director of Research Technology Data Science – Morgan Stanley
• Mandy Plante, former Senior Director of Global Analytics – eBay
• Mohican Laine , Customer Engineer, Google
• Nolan Hill,  SVP of HR Analytics & Data Governance – Bank of America
• Stephen Gatchell,  Director of Data Advisory – BigID
• Martijn Theuwissen,  CCO and Co-Founder of DataCamp

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Master's in Data Science

The Data Science master's program, jointly led by the  Computer Science  and  Statistics  faculties, trains students in the rapidly growing field of data science. 

Data Science lies at the intersection of statistical methodology, computational science, and a wide range of application domains.  The program offers strong preparation in statistical modeling, machine learning, optimization, management and analysis of massive data sets, and data acquisition.  The program focuses on topics such as reproducible data analysis, collaborative problem solving, visualization and communication, and security and ethical issues that arise in data science.

To earn the Master of Science in Data Science, students must complete 12 courses. This requires students to be on campus for at least 3 semesters (one and a half academic years). Some students will choose to extend their studies for a fourth semester to take additional courses or complete a master’s thesis research project.

SEAS will be hosting virtual information sessions this Fall for students interested in the Data Science program. Registration for these sessions is available on the  Admissions Events page for prospective graduate students .

What should a graduate of the Data Science program be able to do?

The design of the program is based on eleven learning outcomes developed through discussions between the computer science and statistics faculty:

Build statistical models and understand their power and limitations

Design an experiment

Use machine learning and optimization to make decisions

Acquire, clean, and manage data

Visualize data for exploration, analysis, and communication

Collaborate within teams

Deliver reproducible data analysis

Manage and analyze massive data sets

Assemble computational pipelines to support data science from widely available tools

Conduct data science activities aware of and according to policy, privacy, security and ethical considerations

Apply problem-solving strategies to open-ended questions

Financing Your Degree

Students typically finance their master’s degree program with a combination of loans, savings, family support, grants (from governments, foundations and companies), fellowships and scholarships. We recommend you visit the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences (Harvard Griffin GSAS)  Funding and Financial Aid  website prior to your application to learn more about your options.

Teaching Fellowships

Approximately 15% of our students are paid Teaching Fellows, usually in the second year. TFing in the first semester is highly unusual. Teaching compensation is paid out at Harvard graduate student rates.

Master's in Data Science Leadership

In master's in data science.

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	University of Delaware
	Jun 15, 2024   2024-2025 Graduate Catalog     2024-2025 Graduate Catalog Program Educational Goals: Students will demonstrate the ability to identify strengths and limitations of different nutrition assessment tools, and choose appropriate ones for different settings. Students will demonstrate written proficiency of the literature in a nutrition-related content area. Students will demonstrate oral proficiency of the literature in a nutrition-related content area. The student will demonstrate the ability to: (1) generate and code summary variables; (2) code and run appropriate descriptive, bivariate, and multivariable models; (3) interpret output for descriptive, bivariate, and multivariable models. Students will demonstrate the ability to identify and answer a novel research question. Students will demonstrate the ability to disseminate written scientific literature. Students will demonstrate the ability to verbally present scientific results in a scholarly setting. Program Policy Document: Please see the Program Policy Document for more information.     Requirements for the Degree: The Nutrition Science PhD Program requires successful completion of a minimum of 48 credit hours, completion of preliminary examination, dissertation proposal defense, dissertation defense and one publishable paper. The Nutrition Science PhD Program is designed to be completed over a 4-year period. Core Requirements: HBNS 855 - Qualitative and Mixed Methods Research in Health Sciences Credit(s): 3 HBNS 856 - Multivariable Biostatistics for Population Health Credit(s): 3 HBNS 812 - Current Topics in Nutritional Sciences Credit(s): 3 HBNS 822 - Research Methods in Nutrition Assessment Credit(s): 3 Statistics/ Data Analysis Electives: Choose three of the following:. ANFS 650 - Applied Biomedical Communication Credit(s): 3 HBNS 609 - Survey Research Methods Credit(s): 3 BISC 643 - Biological data analysis Credit(s): 3 COMM 603 - Communication Research Methods - Procedures Credit(s): 3 COMM 604 - Communication Research Methods - Analysis Credit(s): 3 EDUC 812 - Regression and Structural Equation Modeling Credit(s): 3 EDUC 856 - Introduction to Statistical Inference Credit(s): 3 EPID 603 - Biostatistics for Health Sciences I Credit(s): 3 EPID 604 - Introduction to Epidemiologic Data Analysis in SAS Credit(s): 3 EPID 605 - Epidemiology Methods I Credit(s): 3 EPID 610 - Epidemiology Methods II Credit(s): 3 EPID 613 - Biostatistics for Health Sciences II Credit(s): 3 EPID 614 - Biostatistics for Health Sciences III Credit(s): 3 EPID 615 - Epidemiology Methods III Credit(s): 3 HDFS 615 - Research Methods Credit(s): 3 HDFS 815 - Research Issues and Designs Credit(s): 3 KAAP 602 - Data Analysis and Interpretation in Health Sciences Credit(s): 3 MMSC 635 - Practical Genomics, Proteomics & Bioinformatics Credit(s): 3 NURS 814 - Advanced Quantitative Research in Nursing Science Credit(s): 3 PSYC 809 - Research Design Credit(s): 3 PSYC 860 - Psychological Statistics I Credit(s): 3 PSYC 878 - Hierarchical Linear Modeling Credit(s): 3 SOCI 605 - Data Collection and Analysis Credit(s): 3 SPPA 718 - Survey Research Methods Credit(s): 3 SPPA 808 - Qualitative Research Methods Credit(s): 3 STAT 608 - Statistical Research Methods Credit(s): 3 STAT 613 - Applied Multivariate Statistics Credit(s): 3 STAT 615 - Design and Analysis of Experiments Credit(s): 3 STAT 656 - Biostatistics Credit(s): 3 Nutrition and General Electives: 12 credits - please discuss any elective choices in consultation with Advisor Choose four of the following: HBNS 813 - Health Psychology and Behavioral Medicine Credit(s): 3 HBNS 819 - Social Marketing Credit(s): 3 MMSC 650 - Medical Biochemistry Credit(s): 4 HBNS 608 - Nutrition Program Planning and Evaluation Credit(s): 3 HBNS 610 - Overweight and Obesity Prevention and Management Credit(s): 3 HBNS 640 - Nutrition and Aging Credit(s): 3 HBNS 655 - Issues in International Nutrition Credit(s): 3 HBNS 810 - Nutrition Informatics Credit(s): 3 BHAN/NTDT Seminars NTDT 665 is taken for 0 credits each spring (4 times). BHAN 860 is taken for 0 credits each fall (4 times) HBNS 860 - Graduate Research Seminar Credit(s): 0 HBNS 665 - Seminar Credit(s): 1-3 Dissertation: HBNS 969 - Dissertation Research Credit(s): 9 Credits to Total a Minimum of 48 Last revised for 2024-2025 academic year. Crime Analysis, Certificate Improve public safety with a Certificate in Crime Analysis. Learn about analyzing data, identifying emerging crime patterns, and communicating findings. All Programs More Information About this program. The Certificate in Crime Analysis provides you with the knowledge, skills and abilities necessary to perform entry-level crime analysis in Federal, State and local criminal justice agencies. As a crime analyst you may: perform detailed statistical analyses of crime data  analyze a wide variety of data including arrests, convictions, known criminal associates, and other criminal intelligence data  conduct citizen surveys of crime victimization, satisfaction with police services, and perceptions of departmental performance perform detailed statistical analyses of crime data analyze a wide variety of data including arrests, convictions, known criminal associates, and other criminal intelligence data design and execute managerial and administrative studies forecasting personnel, budgeting, and other resource needs prepare periodic reports on criminal activity and trends identify emerging crime patterns, and communicate your findings to a variety of internal and external audiences Crime analysts must be skilled in manipulating data and creating relational databases that can accommodate a wide variety of data formats and sources. A broad knowledge of law enforcement operations, criminological theory, statistics, research methods and relevant computer technology is needed.   Crime analysts also need good critical thinking skills, logic and reasoning ability, and effective writing and presentation skills. How to Apply Executive & Professional Admissions Certificate Requirements The Certificate in Crime Analysis is a one-year program consisting of 25 credit hours. This online-only program can be completed on a full- or part-time basis, with part-time students completing in two years. All students enrolled in the Crime Analysis certificate program take a series of required foundation courses (19 credits), and elective courses (6 credits) on data management and analysis, terrorism and intelligence analysis. Advanced Criminological Theory  Statistical Analysis  Statistics Lab  Advanced Research Methods in Criminology and Criminal Justice  Advanced Crime Assessment  Crime Mapping  Data & Intelligence Analysis  Qualitative Research Methods in Criminology and Criminal Justice  Typologies of Crime and Criminal Behavior  Issues in Contemporary Law Enforcement  Terrorism and Homeland Security  Economics and Business Forecasting  Data Management in Business  Data Mining for Business Intelligence  Special Topics courses  Provide a foundation in criminological theory, statistics and research methods, criminal behavior, temporal and spatial crime analysis, criminal intelligence analysis Prepare students for positions and advancement in Federal, state, and local criminal justice agencies as crime analysts and intelligence analysts Foster the ability to design and execute applied research studies focused on crime and crime trends Be able to use descriptive and inferential statistics to analyze crime and related social data Be able to conduct temporal and spatial analyses of crime and related social data Instilling knowledge of how to use a variety of computer databases to merge and draw data from diverse sources Making effective oral presentations of crime analyses to specific audiences (including criminal justice officials, representatives of government, at professional conferences, and to the general public Produce effective written analytic products suitable for publication and dissemination by criminal justice agencies Conduct crime analysis and related research while maintaining strong ethical standards and awareness of moral and ethical implications of their work Draw on relevant criminological theory to ensure underlying theoretical processes are sound, that subsequent analyses are theoretically grounded, and that broad perspectives are brought to bear on the complexity of crime analysis Explore the Catalog See the Graduate Catalog for full course descriptions. Course Catalog Admission Requirements Applicants will be accepted into the program each quarter. Applicants’ academic history, motivation, aptitude for post-baccalaureate education, personal goals and professional experiences will be considered.   Combine This Certificate with a Criminal Justice Master's Students can continue their criminal justice education and apply up to ten certificate in crime analysis credits to the Master’s of Arts in Criminal Justice program pending admission into the master’s program. Learn More About This Degree Join an Info Session Gainful Employment Disclosure More about our graduation rates, the median debt of students who completed the program, and other important information is   available here . Featured Faculty Adjunct Faculty Graduate Program Coordinator Professor, Criminal Justice, Criminology, and Forensics Associate Professor Adjunct Professor, Criminal Justice, Criminology, and Forensics Professor, Criminal Justice, Criminology, and Forensics Director, Master of Arts in Criminal Justice Professor/Director, Crime & Justice Research Center Chair, Department of Criminal Justice, Criminology, and Forensics Professor, Criminal Justice, Criminology, and Forensics Assistant Professor, Criminal Justice Assistant Teaching Professor, Criminal Justice, Criminology, and Forensics Related Programs Criminal justice, joint ma & jd. In the Criminal Justice dual degree program, you can earn both the master’s in criminal justice and JD degrees concurrently. Master of Arts, Juris Doctorate Criminal Justice, MA In a top-rated master’s in criminal justice program, you’ll advance your career through a comprehensive, rigorous and analytic study of crime. Master of Arts Criminal Justice, Online, MA In a top-rated online master’s in criminal justice program, you’ll advance your career through a comprehensive, rigorous and analytic study of crime. Get in Touch If you have any questions about the program or application, we’re here to help. Destiny Ledesma Senior Admissions Counselor [email protected] 206-296-5960 263 IMAGES Explain the Skills needed for PhD Data Analysis. And Researcher’s Guide To PhD In Data Science PhD in Data Science Data Analysis Plan in Research Methodology Data Analysis Methods Commonly Used in PhD Theses PhD in Data Analytics VIDEO Data analysis Data Collection and Statistical Analysis MS in Data Analytics and Program Evaluation: Building Real-World Skills for Career Growth Download Paid Research Paper This is why Data Scientists and Data Analysts are in DEMAND Never Use AI| turnitin class id COMMENTS PhD in Data Science An NRT-sponsored program in Data Science Overview Overview Advances in computational speed and data availability, and the development of novel data analysis methods, have birthed a new field: data science. This new field requires a new type of researcher and actor: the rigorously trained, cross-disciplinary, and ethically responsible data scientist. Launched in Fall 2017, the … PhD in Data Science Review the most comprehensive list of Data Science PhD programs in US, courses, requirements, campus/online and tuition. Getting a PhD in Data Science: What You Need to Know A PhD in Data Science is a research degree that typically takes four to five years to complete but can take longer depending on a range of personal factors. In addition to taking more advanced courses, PhD candidates devote a significant amount of time to teaching and conducting dissertation research with the intent of advancing the field. 13 PhD programmes in Data Analytics Find the best PhD programmes in the field of Data Analytics from top universities worldwide. Check all 13 programmes. All PhD in Data Analytics Programs A PhD in Data Analytics or a closely related field is an interdisciplinary doctorate that focuses on cutting-edge research in the realms of advanced analytics, statistical computing, big data, and data science. Doctoral students in analytics: ... A Credit Analysis of the Unbanked and Underbanked: An Argument for Alternative Data ... Doctor of Philosophy in Data Science Pursuing a Ph.D. in Data Science will prepare you to become an expert in the field and work at the cutting edge of a new discipline. Explore an Online Ph.D. in Data Science A Ph.D. in data science gives students a deep understanding of programming, statistics, data analysis, machine learning, and artificial intelligence. The degree combines data science course requirements with practical experience and a dissertation. PhD Program The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. PhD in Big Data Analytics The PhD in Big Data Analytics is an interdisciplinary STEM PhD program focusing on systems and technologies for processing data and information. Unlike pure data science programs, this program includes the human and social implications of information and technology, bringing in critical components ... Ph.D. Specialization in Data Science The Ph.D. specialization in data science is an option within the Applied Mathematics, Computer Science, Electrical Engineering, Industrial Engineering and Operations Research, and Statistics departments. Only students already enrolled in one of these doctoral programs at Columbia are eligible to participate in this specialization. Your complete guide to a PhD in Data Analytics Discover why you should study a PhD Programme in Data Analytics, your study options, the best universities, useful resources, career options and more. Data Analytics in United States: 2024 PhD's Guide Everything about PhD's in Data Analytics in United States: Explore top universities, costs, scholarships, and admission requirements for all study formats. PhD The PhD program is intended to prepare students for a career in research and teaching at the University level. ... data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and ... Big Data Analytics (PhD) Degree Earn your Doctorate, Graduate Program in Big Data Analytics (PhD) from UCF's College of Sciences in Orlando, FL. Learn about program requirements and tuition. PhD in Data Analytics Programs PhD in Data Analytics Programs. A PhD in data analytics prepares professionals to work in data-driven fields, including research, business, healthcare, and government. The most common reason people pursue a PhD in a data-related field is that they are passionate about data and would like to have a career that involves research and making ... Ph.D. Programs The Ph.D. programs of the Department of Statistics at Carnegie Mellon University enable students to pursue a wide range of research opportunities, including constructing and implementing advanced methods of data analysis to address crucial cross-disciplinary questions, along with developing the fundamental theory that supports these methods. PhD in Data Analytics Read about PhD in Data Analytics. Data analysts have a variety of job opportunities. Technical know-how is needed for this vocation to evaluate and interpret data to enhance business performance. Online Doctorate in Big Data Analytics Pursue CTU's online doctorate in big data analytics and work to develop the thought leadership and computer science research skills needed to interpret distributed data. Apply for the program today! PhD in Data Science and Analytics We launched the first formal PhD program in Data Science in 2015. Our program sits at the intersection ofcomputer science, statistics, mathematics, and business. Our students engage in relevant research with faculty from across our eleven colleges. As one of the institutions on the forefront of the development of data science as an academic ... Ph.D. in Statistics The doctoral program in statistics provides future researchers with the preparation to teach and lead in academic and industry careers. Ph.D. in Analytics Notre Dame's PhD in Analytics seeks to develop thought-leaders in the analytics space that are engaged in impactful, cutting-edge scholarly research that considers the ethical dimension of data and its usage. Doctor of Philosophy (PhD) in Business Analytics and Data Science The Ph.D. program in Business Analytics and Data Science is designed to prepare accomplished professionals for senior positions in both public and private sectors. Big Data Analytics Ph.D. Program This program combines the strength of statistical science, data science and machine learning. The Ph.D. program intends to prepare students to fill the need for skilled positions, including leadership positions, in business and industry, as well as for positions in academia to conduct research and teach data analytics at the graduate level. What Is Data Analysis? (With Examples) Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions. Online Masters in Data Analytics Stay at home and earn your online data analytics master's degree from WGU, a respected and affordable online university. Boost your career with advanced skills. Best Free Resources to Learn Data Analysis and Data Science 2. Udemy. Udemy is the go-to marketplace for online courses. Their content library includes over 100,000 titles on almost every topic—including data analytics and data science. They also offer free courses uploaded by authors eager to share their knowledge with the public at no cost. Use the filtering options when browsing the marketplace to ... Master's in Data Science The Data Science master's program, jointly led by the Computer Science and Statistics faculties, trains students in the rapidly growing field of data science. Data Science lies at the intersection of statistical methodology, computational science, and a wide range of application domains. The program offers strong preparation in statistical modeling, machine learning, optimization, management ... Program: Nutrition Science (PhD) Requirements for the Degree: The Nutrition Science PhD Program requires successful completion of a minimum of 48 credit hours, completion of preliminary examination, dissertation proposal defense, dissertation defense and one publishable paper. The Nutrition Science PhD Program is designed to be completed over a 4-year period. Crime Analysis, Certificate Improve public safety with a Certificate in Crime Analysis. Learn about analyzing data, identifying emerging crime patterns, and communicating findings. essay homework paper phd project resume review speech study thesis