phd data science

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.

View All People
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

PhD in Data Science

Students conduct research on cutting edge problems alongside preeminent faculty at UChicago and explore the emerging field of Data Science. As an emerging discipline, Data Science addresses foundational problems across the entire data life cycle. Tackling issues of inequity, climate change, and sustainability will require cutting edge research in artificial intelligence and data usage combined with innovative educational programs to train students in the concepts of information systems. Students of Data Science will not only immerse themselves in a rapidly evolving field; they will help redefine it altogether.

Research Excellence:

As a PhD student in Data Science, you will learn from faculty who have developed research programs that span a wide variety of data science and AI topics, from theory to applications, with a focus on making a societal impact.

Research Topics:

Artificial Intelligence
Data, AI, and Society
Data Systems
Human-Centered Data Science
Machine Learning and Statistics
Use-Inspired Data Science

For more information, including a link to the application, see the Committee on Data Science website .

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Home / Data Science Programs / PhD in Data Science

Data Science PhD Programs

If you’re passionate about big data and interested in an advanced degree, you may be wondering which degree is right for you. Should you go with a Master of Science (M.S.) or a PhD in data science?

Our guide to getting a PhD in data science is here to help. Here, we’ll break down potential pros and cons of choosing either option, related job opportunities, dissertation topics, courses, costs and more.

Southern Methodist University

Master of science in data science.

Earn your MS in Data Science at SMU, where you can specialize in Machine Learning or Business Analytics, and complete in as few as 20 months.

No GRE required.
Complete in as little as 20 months.

University of California, Berkeley

Master of information and data science.

Earn your Master’s in Data Science online from UC Berkeley in as few as 12 months.

Complete in as few as 12 months
No GRE required

info SPONSORED

Just want the schools? Skip ahead to our complete list of data-related PhD programs .

Why Earn a PhD in Data Science?

A PhD in Data Science is a research degree designed to equip you with knowledge of statistics, programming, data analysis and subjects relevant to your area of interest (e.g. machine learning, artificial intelligence, etc.).

The keyword here is research . Throughout the course of your studies, you’ll likely:

Conduct your own experiments in a specific field.
Focus on theory—both pure and applied—to discover why certain methodologies are used.
Examine tools and technologies to determine how they’re built.

PhD Benefits vs. Downsides

There are a number of benefits and downsides to earning a PhD in data science. Let’s explore some of them below.

Benefits of a PhD in Data Science

In a PhD in data science program, you may have the opportunity to:

Research an area in data science that may potentially change the industry, have unexpected applications or help solve a long-standing problem.
Collaborate with academic advisors in data science institutes and centers.
Become a critical thinker—knowing when, where and why to apply theoretical concepts.
Specialize in an upcoming field (e.g. biomedical informatics ).
Gain access to real-world data sets through university partnerships.
Work with cutting-edge technologies and systems.
Automatically earn a master’s degree on your way to completing a PhD.
Qualify for high-level executive or leadership positions.

Downsides of a PhD in Data Science

On the other hand, some PhDs in data science programs may:

Take four to five years on a full-time schedule to complete. These are years you could be earning money and learning real-world skills.
Be expensive if you don’t find or have a way to fund it.
Entail many solitary hours spent reading and writing
Not give you “on-the-job” knowledge of corporate problems and demands.

Is a PhD in Data Science Worth It?

A PhD in data science may open the door to a number of career opportunities which align with your personal interests. These include, but aren’t limited to:

Data scientist. Data scientists leverage large amounts of technical information to observe repeatable patterns which organizations can strategically leverage.
Applications architect. When you work as an applications architect, your main goal is to design key business applications.
Infrastructure architect. Unlike an applications architect, infrastructure architects monitor the functionality of business systems to support new technological developments.
Data engineer. Data engineers perform operations on large amounts of data at once for business purposes, while also building pipelines for data connectivity at the organizational level.
Statisticians : Statisticians analyze and interpret data to identify recurring trends and data relationships which can be used to help inform key business decisions.

At the end of a day, whether a data science PhD is worth it will be entirely dependent upon your personal interests and career goals.

Do You Need a PhD to Land a Job?

In most cases, you don’t need a PhD in data science to land a job. Most computer and information research-related careers require a master’s degree, such as an online master’s in data science .

As you begin your search, pay attention to prospective employers and qualifications for your desired position:

Companies and labs that specialize in data science—and tech players like Amazon and Facebook — may have a reason for specifying a PhD in the education requirements.
Other industries may be happy with a B.S. or M.S. degree and relevant work experience.

Careers for Data Science PhD Holders

People who hold a PhD in data science typically find careers in academia, industry and university research labs, government and tech companies. These places are most likely seeking job candidates who can:

Research and develop new methodologies.
Build core products, tools and technologies that are based on data science (e.g. machine learning or artificial intelligence algorithms for Google or the next generation of big data management systems ).
Reinvent existing methods and tools for specific purposes.
Translate research findings and adopt theory to practice (e.g. evaluating the latest discoveries and finding ways to implement them in the corporate world).
Design research projects for teams of statisticians and data scientists.

Sample job titles include:

Director of Research
Senior Data Scientist/Analyst
Data/Analytics Manager
Data Science Consultant
Laboratory Researcher
Strategic Innovation Manager
Tenured Professor of Data Science
Chief Data Officer (CDO)

PhD in Data Science Curriculum

Typical Program Structure Data science PhDs are similar to most doctoral programs. That means you’ll typically have to:

Complete at least two years of full-time coursework.
Pass a comprehensive exam—comprising oral and written portions—that shows you have mastered the subject matter.
Submit a dissertation proposal and have it approved.
Devote 2-3 years to conducting independent research and writing your dissertation. You may be teaching undergraduate classes at the same time.
Defend your work in a “dissertation defense”—usually an oral presentation to academics and the public.

During these years, you’ll likely engage in professional activities that may help improve your career prospects. Such opportunities include attending and speaking at conferences, applying for summer fellowships, consulting, paid part-time research and more.

Dissertation

PhD students are expected to make a creative contribution to the field of data science—that means you’re encouraged not to go over old ground or rehash what’s already out there. Your contribution will be summed up in your dissertation, which is a written record of your original research.

Some students go into a PhD program already knowing what they want to research. Others use the first couple of years to explore the field and settle on a dissertation topic. Your advisor may be your closest ally in this process.

Data Science vs. Business Analytics vs. Specialties

Doctoral programs in data science may also fall under the related disciplines such as statistics, computational sciences and informatics. It is important to evaluate each program’s curriculum. Will the foundation courses and electives prepare you for the research area that you want to explore?

A related degree you may consider is a PhD in Business Analytics (or Decision/Management Sciences). These degree programs are typically administered through a university’s School of Business, which means the curriculum includes corporate topics like management science, marketing , customer analytics, supply chains, etc.

Interested in a particular subset of data science? Some universities offer specialty PhD programs. Biostatistics and biomedical/health informatics are two examples, but you’ll also find a number of doctoral programs in machine learning (usually run by the Department of Computer Science) and sub-specialties in fields like artificial intelligence and data mining.

Considerations When Choosing a PhD Program

Typical Admissions Requirements PhD candidates typically submit an application form and pay a fee. Universities often look for applicants who have:

A Bachelor of Science (BS) in computer science , statistics or a relevant discipline (e.g. engineering) and a similar master’s degree with an official transcript from an accredited institution
A GPA of 3.0 or higher on a 4.0 scale
GRE test scores
TOEFL or IELTS for applicants whose native language is not English
Letters of recommendation
Statement of purpose/intent
Résumé or CV

If you don’t already have certain skills (e.g. stats, calculus, computer programming, etc.), the university may ask you to complete prerequisite courses.

Programs for PhD in Data Science – Online vs. On-Campus Online programs may require you to attend a few campus events (e.g. symposiums), but allow you to complete coursework and conduct research in your own hometown.

While online learning can be a convenient way of obtaining your PhD from the comfort of home, there are a few important factors to consider.

Are you extremely passionate about an area of research?
Do you mind committing to 4-5 years of study?
Does your university have funding sources (private and government) for data science research?
Will you have access to exciting data resources, labs and industry partners?
Do you know how you’re going to pay for the program?

How Much Does a PhD Cost?

As you research PhD in data science programs, you’ll probably find information on relevant fellowships on some university websites, as well as advice on financial matters. Here are a few ways that you may be able to fund your education:

PhD Fellowships: You’ll find a number of fellowships sponsored by the university, by companies and by the government (e.g. National Science Foundation). Be aware that some external fellowships will only cover the years of your dissertation research.
Teaching/Research Assistantships: Assistantships are a common way for universities to support PhD students. In return for teaching undergraduates or working as a researcher, you’ll often receive a break on tuition costs and a living stipend.
In-State Tuition : Public universities may offer in-state students a much lower cost per credit.
Regional Discounts: Many state universities have agreements to offer reduced tuition costs to students from neighboring states (e.g. New England Board of Higher Education Regional Student Program (RSP) . Check to see if this applies to your PhD.
Travel Grants: Doctoral students may have the opportunity to attend research conferences and network with future collaborators. Some grants are designed with this purpose in mind.
Student Loans: In addition to grants, you can consider applying for student loans to finance your PhD studies. Remember, a doctorate is a long-term commitment—you may not see a financial return on your education for a number of years.

Some PhD students in data science are fully funded . For example:

U.S. citizens and permanent residents in Stanford’s PhD in Biomedical Informatics are funded by a National Library of Medicine (NLM) Training Grant and Big Data to Knowledge (BD2K) Training Grants

If you’re coming from overseas, try talking to your school about any differences between funding for citizens and international students.

How Long Does a PhD in Data Science Take?

The length of time it takes to obtain a PhD will likely vary depending on your chosen program. Programs for similar or identical degrees can have differing completion requirements at different schools, meaning how many years your PhD program takes will differ as well.

Of course, the amount of time you spend working toward a PhD in data science can also vary depending on whether you choose to take it part-time or full-time. Assuming you consistently pass your classes, a full-time commitment to your PhD program will expedite your way through it.

But a commitment like that won’t fit everyone’s lifestyles. For example, you might need to work to support yourself financially, or you might be raising a family. These sorts of important commitments are time-consuming and can take a lot of energy. So, in that case, a part-time commitment to your PhD program might make more sense for you.

Interested in STEM Careers?

If you’re looking for information on career paths that involve STEM , see our guides below:

Data Science and Analytics Careers:

Data Scientist
Data Analyst
Business Analyst

Computer Science, Computer Engineering and Information Careers:

Computer and Information Research Scientist

Marketing and User Research Careers:

UX Designer

Compare Careers and STEM Fields:

Cybersecurity vs. Computer Science

Related Graduate STEM Degrees

Master’s in Business Analytics
Master’s in Information Systems
Master’s in Computer Engineering
Master’s in Computer Science
Master’s in Cybersecurity Programs
Master’s Applied Statistics
Master’s in Data Analytics for Public Policy
Data Science MBA Programs
Master’s in Geospatial Science and
Geographic Information Systems
Master’s in Health Informatics
Master of Library and Information Science

Related Undergraduate STEM Degrees

Online Bachelor’s in Data Science
Sponsored: Computer Science at Simmons

PhD in Data Science School Listings

We found 57 universities offering doctorate-level programs in data science. If you represent a university and would like to contact us about editing any of our listings or adding new programs, please send an email to [email protected].

Last updated August 2021. The program’s website is always best for most up to date program information.

PhD in Data Science/Analytics Online

Looking for on-campus programs? See the full list of on-campus PhD in Data Science/Analytics programs .

Colorado Technical University

Doctor of computer science – big data analytics, colorado springs, colorado.

Name of Degree: Doctor of Computer Science – Big Data Analytics

Enrollment Type: Self-paced

Length of Program: 4 years

Credits: 100

Admission Requirements:

Carnegie Mellon University

School of computer science, ph.d. program in machine learning, pittsburgh, pennsylvania.

Name of Degree: Ph.D. Program in Machine Learning

Enrollment Type: N/A

Length of Program: 2 years

Credits: N/A

Recent transcripts
Statement of purpose
Three letters of recommendation
TOEFL scores if your native language is not English

Chapman University

Schmid college, ph.d. in computational and data sciences, orange, california.

Name of Degree: Ph.D. in Computational and Data Sciences

Enrollment Type: Full-Time and Part-Time

Credits: 70

GRE required
Statement of intent
Resume or curriculum CV.
TOEFL score for international students

Indiana University – Indianapolis

School of informatics and computing, ph.d. in data science, indianapolis, indiana.

Name of Degree: Ph.D. in Data Science

Credits: 90

Bachelor’s degree; master’s preferred
Transcripts
TOEFL or IELTS

Kennesaw State University

School of data science analytics, doctoral degree in analytics and data science, kennesaw, georgia.

Name of Degree: Doctoral Degree in Analytics and Data Science

Enrollment Type: Full-Time

Credits: 78

Statement of how this degree facilitates your career goals

PhD in Data Science/Analytics On-Campus

Looking for online programs? See the full list of online PhD in Data Science/Analytics programs .

New York University

Center for data science, new york , new york.

Credits: 72

Resume or curriculum CV
TOEFL or IELTS (TOEFL Preferred)
Statement of Academic purpose

Institute for Computational and Data Sciences

Phd computational and data enabled science and engineering, buffalo, new york.

Name of Degree: PhD Computational and Data Enabled Science and Engineering

Computational Data Sciences

Master’s degree
Resume or CV
GRE scores (Temporarily suspended)

University of Maryland

College of information studies, doctor of philosophy in information studies, college park, maryland.

Name of Degree: Doctor of Philosophy in Information Studies

Credits: 60

Transcripts
Resume or CV or CV
academic writing sample
TOEFL/IELTS/PTE (required for most international applicants)

University of Massachusetts in Boston

College of management, doctor of philosophy in information systemaster of science for data science and management, boston, massachusetts.

Name of Degree: Doctor of Philosophy in Information SysteMaster of Science for Data Science and Management

Credits: 42

Official transcripts official
GMAT or GRE scores scores
Official TOEFL or IELTS score.

University of Nevada – Reno

College of science, ph.d. in statistics and data science, reno, nevada.

Name of Degree: Ph.D. in Statistics and Data Science

Length of Program: 4+ years

Undergraduate/Graduate Transcripts
TOEFL/IELTS (only required for international students)

University of Southern California

School of business, ph.d. in data sciences & operations, los angeles, california.

Name of Degree: Ph.D. in Data Sciences & Operations

Undergraduate/Graduate Transcripts
GRE or GMAT
(3) letters of recommendation
Passport Copy

University of Washington

Mechanical engineering, doctor of philosophy in mechanical engineering: data science, seattle, washington.

Name of Degree: Doctor of Philosophy in Mechanical Engineering: Data Science

Worcester Polytechnic Institute

Worcester, massachusetts.

PhD in Data Science

First Year Requirements

The standard first-year program requires students to complete nine courses: four required courses (1-4 below); one elective either in mathematical foundations or scalability and computing (pick from either 5 or 6); and finally four other electives that can come from proposed courses in data science or existing graduate courses in Computer Science or Statistics. Some students, after consulting with the committee graduate advisor, might decide to take the nine courses over the first two years.

Required courses:

Foundations of Machine Learning and AI Part 1
Responsible Use of Data and Algorithms
Data Interaction
Systems for Data and Computers/Data Design
Foundations of Machine Learning and AI Part 2
Data Engineering and Scalable Computing

Synthesis project

Students will take courses during the first two years after which they focus primarily on their research. A milestone in this transition is completion of a synthesis project before the end of the second year in the program. Thesis projects can be done in partnership with any of DSI affiliates, and aims to meaningfully connect PhD students to their chosen focus areas.

Thesis Advisor and Dissertation Committee

Students typically select a thesis advisor by the beginning of their second year. By the end of the third year, each PhD student, after consultation with their advisor, shall establish a thesis committee of at least three faculty members, including the advisor, with at least half of the members coming from the Committee on Data Science.

Proposal Presentation and Admission to Candidacy

By the end of the third year, students should have scheduled and completed a proposal presentation to their committee, in order to be advanced to candidacy. The proposal presentation is typically an hourlong meeting that begins with a 30-minute presentation by the student, followed by a question and discussion period with the committee.

Dissertation Defense

The PhD degree will be awarded following a successful defense and the electronic submission of the final version of the dissertation to the University’s Dissertation Office.