phd in data science and engineering

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

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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

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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

• Related Programs

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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• Graduate Programs

Data Science & Engineering

This Ph.D. program focuses on advanced skills in engineering, developing, constructing, testing and maintaining architectures for data, databases and large-scale processing systems.

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Become An Expert in Data Engineering

As a student in the Data Science & Engineering program at USD, you'll learn how to clean and organize data, as well as develop the necessary skills to construct, test, and maintain architectures for data, databases, and large-scale processing systems through computer programming and mathematical processing. You'll have the opportunity to partner with top professors from both the South Dakota Mines and USD, bringing together the expertise of both institutions to gain a comprehensive and interdisciplinary education in data science and engineering.

The program curriculum is tailored to meet the needs of students who seek to advance their careers in data science, as well as those who aspire to become academic researchers. You'll engage in coursework covering a range of topics, including machine learning, data visualization, statistical modeling, and big data analytics. Additionally, you'll have the opportunity to conduct research with faculty members who are experts in various areas of data science and engineering.

The demand for data science jobs continues to grow. According to Forbes, data science jobs have increased by over 650% since 2013, making the skillsets gained from this doctorate program highly in-demand. You'll be equipped with the skills you need to design and implement cutting-edge data analysis solutions, make data-driven decisions, and develop innovative data-driven products and services. This will enable you to pursue careers in a variety of fields, including academia, industry, government, and health care.

Degrees & Offerings

Data science & engineering (ph.d.).

A doctoral degree in data science and engineering is designed to equip you with the skills and knowledge necessary to excel in the ever-growing field of data science. The collaborative program is an exciting opportunity for students seeking a rigorous and interdisciplinary education to gain expertise from USD and South Dakota Mines. Pursue coursework in core, elective and research requirements for a total of 72 credit hours. You may apply 24 credits and six research credits from a previous M.S. degree toward the Ph.D. requirements, subject to approval by the student’s committee.

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Program Details

College of Arts & Sciences

Computer Science

Mathematical sciences, known for excellence, since 2013, the number of jobs in data science and engineering has increased by over 650%, and experts project they will continue to rise by 28% by 2026. that is approximately 11 million new data science jobs by 2026..

Source: Forbes and U.S. Bureau of Labor Statistics

Amazing interdisciplinary course options through USD's partnership with South Dakota Mines is unique and what makes this educational experience exceptional.

Percent of computer science graduates secure employment., small faculty-student ratio assures you the one-on-one attention and mentorship you need to succeed., student opportunities.

As a student at USD, the opportunities available to you extend beyond the classroom. Explore your interests, find your community and experience your education to the fullest extent through the following opportunities.

• Assistantships
• Computer Science Student Organizations
• Mathematical Sciences Student Organizations

An Affordable Education

Gain valuable real-world, professional experience while enjoying paid employment and discounted tuition rates as a graduate student at USD. The affordability you gain through graduate assistantships and fellowships will equip you will valuable, professional-level skills that will set you apart after graduation. 

Teaching and research assistantships are awarded by individual departments. If you do not receive an assistantship within your department, you are eligible for an assistantship outside of your academic program or administrative assistantships within support offices are available at USD. Further inquiries should be directed to the graduate chair of the department.

For more information and to apply for a graduate assistantship, visit the Graduate Assistantship webpage.

Real Experience Through Research

You'll find the mentorship and opportunities you need to study topics that spark your curiosity. 

At USD, our graduate students are actively pursuing unique research and presenting at local and national conferences exploring innovative areas of interest to them. Additional research and grant opportunities include:

Three Minute Thesis (3MT®) 

Three Minute Thesis (3MT®) competition provides the opportunity to communicate the significance and impact of your research project in just three minutes. 

Research and Creative Scholarship Opportunities

Each semester students may apply for grants through the USD Graduate School, faculty and students pursue research in virtually all academic departments on campus, and in many cases, it is a required portion of a graduate degree program. 

IdeaFest is an annual event celebrating student research, creative scholarship and academic engagement. Undergraduate and graduate students in all disciplines present their work in oral and poster presentations, live performances, readings, exhibits and displays. 

USD is home to more than 170 student organizations, including several that are housed in the Department of Computer Science. Through these organizations, you can forge valuable professional connections, make lifelong friends and gain incomparable experience in your field.

Association of Computing Machinery

Formed in 1947, the ACM is is the world's largest scientific and educational computing society. Becoming a member will help you build relationships with other students and computer science professionals and open doors for you professionally.

Network Security Club

The club regularly participates in the North Central Collegiate Cyber Defense Competition, in which schools compete to create the best corporate network security system. In 2017, USD's team took second place.

Upsilon Pi Epsilon (UPI)

This international honor society promotes excellence in computer science and information technology, with more than 300 chapters at schools across the country.

Graduate & Professional Student Association (GPSA)

Join your fellow graduate and professional students at USD. We aim to build a sense of community between graduate and professional students. Build your resume, network with others and gain experience and the unique opportunity to represent the interests of graduate students to the greater campus and community.

At USD, our graduate students are active. USD is home to more than 170 student organizations, including several that are housed in the Department of Mathematical Sciences. You may be interested in creating projects and hosting outreach opportunities to make friends, develop new skills and explore your interests.

Pi Mu Epsilon

Pi Mu Epsilon is a national mathematics honor society dedicated to promoting math and recognizing math students by sponsoring conferences, travel funds for student speakers and cash prizes.

A place to bond with fellow students, the Math Club sponsors field trips and activities that are open to both math and non-math students.

Join your fellow graduate and professional students at USD. We aim to build a sense of community between graduate and professional students. Build your resume, network with others and gain experience and the unique opportunity to represent the interests of graduate students to the greater campus and community. 

Join Now   

Departments & Facilities

Learn to develop and manage technology that changes the world in the Department of Computer Science.

Our renowned faculty will help you understand the wide field of mathematics and how it impacts our everyday lives.

Two South Dakota Institutions

By collaborating with distinguished professors from both the University of South Dakota and South Dakota Mines, you can benefit from the combined expertise of both institutions.

A Strong Computer Science Program

Faculty & staff.

Get to know the faculty and staff in the Computer Science and Mathematical Sciences department. Experts in their field, they contribute to research and scholarship in algorithms, artificial intelligence, machine vision, pattern recognition, data science, information retrieval, cybersecurity, statistics, analysis, biomathematical modeling and more.

Dan Van Peursem

Yuhlong Lio

Jose Flores

Ramiro Lafuente-Rodriguez

Teresa Chasing Hawk

Laurie Fritsch

Catalin Georgescu

Douglas Goodman

Kyle Greywall

Farhad Akhbardeh

Clark Bennett

Angela Keith

Shannon Kortan

Kelly Steinmetz

Kristen Maxon

Carrie Minette

Gabe Picioroaga

Rodrigue Rizk

Vijayalakshmi Saravanan

Sally Schmidt

Sandra Shumaker

Zachary Tschetter

Longwei Wang

Nicholas Wulf

Surprisingly affordable, graduate tuition & costs.

Learn more about tuition and fees for undergraduate students and see how your out-of-pocket costs at USD compare to those at other colleges and universities. Visit the Graduate Tuition & Costs Detailed page  for program specific costs and fees.

Financial Aid

Navigating options for how to pay for college can be challenging, but you are not alone. The Office of Financial Aid  will work with you and your family to explore how you can make your college education even more affordable.

Graduate Assistantships

USD offers opportunities for grad students to earn a reduced tuition rate in exchange for their teaching, research and service efforts to the university.

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• College of Engineering
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• Computational Data Science and Engineering
• PhD Program

Ph.D. In Computational Data Science and Engineering

The PhD in Computational Data Science and Engineering (CDSE) is an interdisciplinary graduate program designed for students who seek to use advanced computational methods to solve large problems in diverse fields ranging from the basic sciences (Physics, chemistry, mathematics, etc.) to sociology, biology, engineering, and economics.

The mission of the Department of Computational Data Science and Engineering  is to graduate professionals who (a) have expertise in developing novel computational methodologies and products, and/or (b) have extended their expertise in specific disciplines (in science, technology, engineering, and socioeconomics) with computational tools

Admission Requirements 

Admissions decisions reflect an evaluation of the applicant’s potential to engage in graduate coursework and independent and original investigations. Applicants must use the Graduate School admission portal. Admission is granted for a specific semester or summer term, and any change in the admission date must be requested in writing and approved by the School of Graduate Studies.

Degree Requirements

For admission to the Ph.D. in Computational Data Science and Engineering, the applicant must have a Master of Science or of Engineering degree in computational, computer, or data science or engineering, or in science, engineering, business, economics, technology or in a field allied to computational data sicence and engineering with a minimum GPA of 3.00/4.00. Other possibilities are for direct admission to the PhD or for admission to an accelerated MS/PhD in CDSE. Please contact us for more detail.

Graduate School requirements

Applicants to the Ph.D. Program must comply with the requirements for admission as specified by the School of Graduate Studies for all similar programs. In particular, they must comply with:

• The TOEFL and GRE examination requirements;
• Requirements regarding official transcripts for all college-level academic work;
• Requirements regarding Letters of Recommendation; and
• Completing an Application and paying all application fees

Other Requirements

• The Applicant shall provide a “Statement of Purpose” in the context of pursuing the Ph.D. in Computational Science and Engineering.
• An applicant requesting financial aid is strongly encouraged to provide a resume.

Ph.D. Degree Requirements

Post-bs option.

Total credit hours: 62

• Pass 12 credit hours as core courses: CSE 702, 703, 801, 804
• Pass 27 credit hours of elective courses from engineering, computer science, mathematics, physics, chemistry, biology, economics, business, agricultural science, or other courses approved by the CDSE Department, with approval of Advisor
• Pass the Doctoral Seminar (CSE 992: 1 credit hour) twice for a total of 2 credit hours.
• Take 15 credits of Dissertation-CSE 997
• Pass 6 additional credit hours to complete the 62 credit hour requirement. These credit hours can be from Dissertation-CSE 997, Continuation of Dissertation-CSE 999, Supervised Teaching-CSE 993, Supervised Research-CSE 994, or approved graduate level courses, with approval of Advisor
• At least 26 credit hours should be 800-999
• Pass Qualifying Exam, Preliminary Exam, and Dissertation Defense
• Maintain appropriate GPA

Post-MS Option

Total credit hours: 44

• Pass 24 credit hours of elective courses from engineering, computer science, mathematics, physics, chemistry, biology, economics, business, agricultural science, or other courses approved by the CDSE Department, with approval of Advisor
• Pass 3 additional credit hours to complete the 44 credit hour requirement. These credit hours can be from Dissertation-CSE 997, Continuation of Dissertation-CSE 999, Supervised Teaching-CSE 993, Supervised Research-CSE 994, or approved graduate level courses, with approval of Advisor

Qualifying Written Examination Requirements

  The successful Ph.D. candidate must pass a Qualifying Examination administered by the Department in the four core courses CSE 702, CSE 703, CSE 801, CSE 804.

Research and Dissertation Requirements

Major Advisor:   Initially the Director of the Ph.D. Program will serve as an Academic Advisor for all new students entering the Program. Each student in the Ph. D. Program is expected to select a Major Advisor by the beginning of the second year with the approval of the Department Chair. The Major Advisor must hold a tenure or tenure-track full-time faculty position at the university, and shall subsequently act as the Academic Advisor as well.

Composition of Ph.D. Committee:   A Ph.D. Advisory Committee will consist of a minimum of five (5) graduate faculty with the Major Advisor as its chairperson. The Ph.D. Advisory Committee will be recommended by the Major Advisor, with input from the student, to the Chair of Computational Data Science and Engineering, for approval by the Dean of Graduate Studies. The Committee shall supervise the student’s Program, administer dissertation review and approval, and finally recommend the awarding of the degree.

Plan of Study:   Upon the student’s selection of a research area, the Ph.D. Advisory Committee shall review the student’s prior transcripts, evaluate and recommend any transfer credits, and provide advice to the student. The student shall subsequently prepare a Plan of Study for approval by the Ph.D. Advisory Committee, the Chair of the CDSE Department and the Dean of the School of Graduate Studies.

Oral Defense of Dissertation Proposal (Preliminary Examination):   The dissertation proposal is submitted to the student’s Major Advisor and the Ph.D. Advisory Committee for review. The committee will make recommendations as needed. The proposal must be orally defended by the candidate before the Advisory Committee, and it must be approved by the Committee, and the student can proceed further with his/her research.

Candidacy for Ph.D. in Computational Data Science and Engineering:   Admission to candidacy for Ph.D. in Computational Data Science and Engineering shall require compliance with all existing Graduate School policies, and shall occur after the student has successfully passed the Qualifying Examination and the Preliminary Examination.

Final Oral Examination:   The final oral examination is scheduled after the dissertation is complete except for such revisions as may be necessary as a result of the examination, but not earlier than the semester or its equivalent after admission to candidacy, and not before all required course work has been completed or is in progress.

Dissertation:   The doctoral dissertation presents the results of the student’s original investigation in the field of major interest. It must be a contribution to knowledge, be adequately supported by data and be written in a manner consistent with the highest standards of scholarship. Publication is expected.

Grade Point Average:   The student must successfully complete the approved Plan of Study with a minimum cumulative GPA of 3.0 or better.

Residency Requirements:   For the Doctor of Philosophy (PhD) degree, the student is expected to be registered for graduate work for at least four semesters beyond the Master of Science. At least two residence credits must be secured in continuous residence (registration in consecutive semesters) as a graduate student at the university.

Current students

Ph.d.–data science option.

Doctor of Philosophy (Mechanical Engineering: Data Science): students will receive credentialed training in the analysis of large datasets. The goal of this option is to educate all students in the foundations of data science, so they may apply those methods and techniques in current research. The PhD Data Science is designed for students with little or no background in data science, computer science or coding.

The requirements for the Doctor of Philosophy (Mechanical Engineering: Data Science) are as follows:

I. Courses from three out of four of the following areas

1. software development for data science, recommended courses, 2. statistics and machine learning, 3. data management and data visualization, recommended courses, 4. department specific requirement.

If listed above, then course doesn’t count twice

II. eScience Community Seminar

• 2 quarters of the eScience Community Seminar  OR ME Data Drive seminar with Professor Steve Brunton. Seminar credits do not count towards graduation requirements. 

III. Fulfillment of the Mechanical Engineering requirements

In addition, all students are required to take at least one additional course in quantitative methods (statistics, applied mathematics, mathematics, or computational science) or in a methodology directly relevant to their area of focus. such courses are to be specified in each student’s individualized training plan..

Students may not count any course toward both the ME coursework requirements and the Data Science requirements. For example, if students take ME 574 and count it toward the computational or numerical analysis requirement, they cannot use this course to fulfill the Data Science requirement. Students must ensure that there’s a minimum of 9 distinct credits taken for the Data science option.

Princeton University

Ai@princeton.

Nimble, high-intensity research teams at Princeton collaborate across disciplines to accelerate discovery in artificial intelligence without the logistical barriers that have traditionally slowed universities down. Engineers, scientists, humanists and policy experts who are pre-eminent in their fields come together in a deeply interdisciplinary approach to problem solving.

Visit AI@Princeton

Program Information

Diversity, equity, and inclusion.

Princeton hosts international conference on modeling and securing Earth’s groundwater

Charting a pathway to next-gen biofuels

Junior faculty awards recognize outstanding teaching and research

Diversifying AI through data collection and career development

Mengdi Wang makes a play at decoding disease

New initiatives bring Princeton to the fore of AI innovation

Ryan P. Adams

Computer Science

Sigrid Adriaenssens

Civil and Environmental Engineering

Christine Allen-Blanchette

Mechanical and Aerospace Engineering

Sanjeev Arora

Adji Bousso Dieng

Jianqing Fan

Operations Research and Financial Engineering

Adam Finkelstein

Jaime Fernández Fisac

Electrical and Computer Engineering

Michael Freedman

Tom Griffiths

Felix Heide

Peter Henderson

Egemen Kolemen

Aleksandra Korolova

Jason D. Lee

Anirudha Majumdar

Reed Maxwell

Prateek Mittal

Karthik Narasimhan

Arvind Narayanan

Ravi Netravali

H. Vincent Poor

Peter Ramadge

Vikram Ramaswamy

Zhiyong "Jason" Ren

Szymon Rusinkiewicz

Olga Russakovsky

Sebastian Seung

Olga Troyanskaya

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Data Science and Engineering

The Data Science and Engineering programs offered at the University of Maine are intended to meet the growing demand for graduates with core skills in managing and analyzing complex data and analytics challenges. The graduate programs provide a pathway for students from diverse fields to transition to multiple data science and engineering career paths by providing them with core graduate-level courses across the entire spectrum of the data lifecycle.

In support of the interdisciplinary spirit of data science and engineering, the program is designed to accommodate students from a wide range of undergraduate degrees or other graduate degree backgrounds with options for specialization in different domains.  A collection of courses with a variety of in-class and online options support students in residence as well as meet the needs of people currently in the workforce or who are otherwise place-bound and need training or retraining in the area of Data Science and Engineering.

Program Highlights

• The programs draw upon faculty and courses from throughout the University and other University of Maine System campuses
• Data collection technologies
• Data representation and management
• Data analytics
• Data visualization and human-centered computing
• Data security, preservation, and reuse
• Spatial informatics
• Bioinformatics/biomedicine
• Business information
• Social and behavioral data science
• Engineering analytics

Master of Science in Data Science and Engineering

The 30 credit Master of Science (MS) can be completed online or on-campus and will train students in the management, analysis, and visualization of large and complex data sets. The thesis option consists of 24 credits of course work with 6 thesis credits. The non-thesis option consists of 30 credits of course work, 3 of which will be a project or internship course.

Students with undergraduate degrees in any field may apply, however, those with two semesters of calculus (e.g., MAT 126, 127), a semester of statistics (e.g., STS 232 or ECE 316 or CHB 350), and proficiency in programming will have more options for classes they may pursue. Students without these background prerequisites will be required to take foundation courses that will count toward the degree.

Graduate Certificate in Data Science and Engineering

The 15 credit graduate certificate can be completed online or on-campus and is an option for those unsure about committing to a master’s degree. Courses taken in the certificate can be used toward the master’s degree.

NOTE: If you are an international student who is interested in pursuing any of our graduate certificate programs, please contact Katy Blackmer  BEFORE applying.

The Data Science & Engineering program is not accepting applications for Spring 2024 at this time.

Degrees offered: MS, Graduate Certificate

Program Format: On Campus or Online

Test Required: None

Contact: Kate Beard & Silvia Nittel

Contact Email: [email protected]; [email protected]

Program Website: Data Science and Engineering

Accelerated Program Information for UMaine Undergraduates

Major Pathways: Any Undergraduate Degree

Middle of the first semester of the junior year

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

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Cleveland State University

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Phd, specialization in applied data science, in partnership with the cleveland clinic foundation , full assistantships (tuition and stipend) are available.  all areas of interest in data science are welcome.  it is recommended to apply by march 5th for full consideration for fall semester. .

A joint specialization program with the Cleveland Clinic.  The Department of Computer Science (CS) offers programs of course work and research leading to the Doctor of Philosophy in Engineering, Applied Data Science Specialization, which is run as a joint venture with the Cleveland Clinic’s Lerner Research Institute (LRI).  Department faculty and staff members of LRI work in cooperation to offer graduate training in data science in relation to medical and biological sciences. Students will be provided with a unique opportunity to conduct research at one of the nation’s top medical research institutes.  

Classes are taken on the CSU campus, and the research is performed either on the ever-expanding Cleveland Clinic campus (a short, 3-mile bus ride from CSU on the Healthline) or on the CSU campus. 

Faculty and Research  

The faculty of the Applied Data Science program include members from the Departments of Computer Science, Chemical and Biomedical Engineering, Chemistry, Math and Statistics, and Physics at CSU, and over 30 adjunct faculty members from the Lerner Research Institute at the Cleveland Clinic.  The ADS faculty at CSU and at the Cleveland Clinic conduct their world-recognized  research  in the following areas: 

• Biomedical data science and informatics to solve challenging problems in biology, medicine and public health by applying concepts and methods from computer science and data science together with principles of information science. 
• Data ethics; Bias in medical information, Fairness in and fairness via AI, Health privacy, and Cybersecurity in healthcare. 
• High performance computing infrastructure, including hybrid cloud, artificial intelligence (AI) and quantum computing technologies, to empower big data medical research. 

CSU and the Cleveland Clinic offer a vibrant research environment, including top-notch research facilities, core technical support, weekly technical seminars, and social programs.  

Course Requirements 

The program provides a foundation built on fundamentals in computer and data science topics.  In-depth knowledge in the specific field of interest is gained from advanced courses in engineering and sciences.   In brief, the course requirements for the ADS program include: 

Core ADS courses

CIS 606 – Analysis of Algorithms CIS 666 - Artificial Intelligence CIS 667 - Bioinformatics STA 567 – Applied Regression Models

• Two courses in advanced engineering mathematics (e.g., STA 524 Probability and Mathematical Statistics, STA 536 Design and Analysis of Experiments)
• One course in research communications (e.g., ESC 720 Research Communications, BIO 785 Practical Grant Writing)
• Two courses from outside of engineering, in areas such as biology, chemistry, physics, mathematics, health sciences
• Ten credits of engineering electives; options include 600-level CIS, EEC, BME and STA courses.
• Students who do not have a degree in a computing field will be recommended to take CIS 545 Architecture and Operating Systems and CIS 550 Introduction to Algorithms before taking three CIS core subjects. Note that these two 500-level courses are counted toward the PhD in Engineering degree.

Program Committee

Brian Davis, Ph.D. Associate Dean of Research and Graduate Affairs Washkewicz College of Engineering, CSU https://engineering.csuohio.edu/dean/deans-office-administration

Nolan Holland, Ph.D. Chair and Professor, Department of Chemical & Biomedical Engineering Washkewicz College of Engineering, CSU https://facultyprofile.csuohio.edu/csufacultyprofile/detail.cfm?FacultyI...

Chansu Yu, PhD Professor, Department of Electrical and Computer Engineering Washkewicz College of Engineering, CSU https://academic.csuohio.edu/yu-chansu/

Paul Bishop, PhD, RVT Director, Vascular Core Laboratory Heart, Vascular & Thoracic Institute (Miller Family), Cleveland Clinic https://my.clevelandclinic.org/departments/heart/research-innovations/re...

Daniel Blankenberg, PhD Assistant Staff, Genomic Medicine Institute Lerner Research Institute of Cleveland Clinic https://www.lerner.ccf.org/genomic-medicine/blankenberg/

Daniel Rotroff, MSPH, PhD Assistant Staff, Department of Quantitative Health Sciences Lerner Research Institute of Cleveland Clinic https://lerner.ccf.org/quantitative-health/rotroff/

Program Faculty

All Computer Science program faculty , CSU Brian Davis, Ph.D., CSU Nolan Holland, Ph.D., CSU Chansu Yu, PhD, CSU Paul Bishop, PhD, RVT, Cleveland Clinic Daniel Blankenberg, PhD, Cleveland Clinic Daniel Rotroff, MSPH, PhD, Cleveland Clinic

Balu Krishnan, PhD Staff, Neurological Institute, Cleveland Clinic https://my.clevelandclinic.org/research/neurological/epilepsy/advanced-n...

Larisa Tereshchenko, MD, PhD Associate Staff, Quantitative Health Sciences, Cleveland Clinic https://my.clevelandclinic.org/staff/29410-larisa-tereshchenko

For Information Contact

Graduate Program Director, ADS  Department of Computer Science   Cleveland State University  2121 Euclid Avenue, FH 212  Cleveland, OH 44115-2425  216-687-4604 E-mail: Brian L Davis ( [email protected] ) or Chansu Yu ( [email protected] )

©2024 Cleveland State University | 2121 Euclid Avenue, Cleveland, OH 44115-2214 | (216) 687-2000. Cleveland State University is an equal opportunity educator and employer. Affirmative Action | Diversity | Employment  | Tobacco Free  | Non-Discrimination Statement  | Web Privacy Statement  | Accreditations

15 PhD positions available in Data Engineering for Data Science

Data is a key asset in modern society. Data Science, which focuses on deriving valuable insight and knowledge from raw data, is indispensable for any economic, governmental, and scientific activity. Data Engineering provides the data ecosystem (i.e., data management pipelines, tools and services) that makes Data Science possible. The European Joint Doctorate in "Data Engineering for Data Science" (DEDS) is designed to develop education, research, and innovation at the intersection of Data Science and Data Engineering. Its core objective is to provide holistic support for the end-to-end management of the full lifecycle of data, from capture to exploitation by data scientists.

Application Details

DEDS operates under the Horizon 2020 - Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2020) framework. It is jointly organised by Université Libre de Bruxelles (Belgium), Universitat Politècnica de Catalunya (Spain), Aalborg Universitet (Denmark), and the Athena Research and Innovation Institute (Greece). Partner organisations from research, industry and the public sector prominently contribute to the programme by training students and providing secondments in a wide range of domains including Energy, Finance, Health, Transport, and Customer Relationship and Support.

DEDS is a 3-year doctoral programme based on a co-tutelle model. A complementary set of 15 joint, fully funded, doctoral projects focus on the main aspects of holistic management of the full data lifecycle. Each doctoral project is co-supervised by two beneficiaries and includes a secondment in a partner organisation, which grounds the research in practice and validate the proposed solutions. DEDS delivers innovative training comprising technical and transversal courses, four jointly organized summer and winter schools, as well as dissemination activities including open science events and a final conference. Upon graduation, a joint degree from the universities of the co-tutelle will be awarded.

Research Positions

The following ESR positions are available, for details please click at the selected topic:

ESR 1.1: Semantic-aware heterogeneity management

ESR 1.2: Traceability in big data processing

ESR 1.3: Privacy-aware data integration

Storage and Processing

ESR 2.1: Transparent in-situ data processing

ESR 2.2: Distribution and replication for feature selection

ESR 2.3: Model-based storage for time series

ESR 2.4: Analytic operators for trajectories

ESR 2.5: End-to-end optimisation for data science in the wild

ESR 2.6: Physical optimisation for large scale, DS workloads

Preparation

ESR 3.1: Spatio-temporal data integration & analysis

ESR 3.2: Synopses-driven data integration & federated learning

ESR 3.3: Unified information extraction for data preparation

ESR 4.1: Interactive exploration & analytics on complex big data

ESR 4.2: Scalable model selection in stream settings

ESR 4.3: A platform for prescriptive analytics

How to Apply?

To apply for the programme, please fill in the application form at the following URL:

Application form : https://deds.ulb.ac.be/emundus/

Application manual : Before applying please, read carefully the Application Manual that describes in detail the application procedure and requirements. It covers the most frequent questions.

Important Dates

Application deadline : February 7, 2021, midnight AoE (Anywhere on Earth)

Interviews : Mid of February 2021 - Mid of March 2021

Notification : End of March 2021

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Ritchie School of Engineering & Computer Science Alumni Spotlight: Kapil Desai - Data Science (Video)

Ritchie school communications team, video interview with data science master's degree alumni kapil desai.

We recently chatted with Ritchie School of Engineering & Computer Science alumni, Kapil Desai . We discussed his background and his Data Science and Engineering internship at Regeneron . "When I started, [I did not have an idea] about Data Science and different possibilities - how the data is handled, how it is stored, how different models can be built, and how it can be used for real practical industry experiences. But after attending courses I am confident that I can make use of various machine learning algorithms, and deep learning models to optimize processes, increase efficiency, and reduce human errors." Thank you, Kapil Desai , for sharing your insights and experiences!

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TRIADS software engineering team custom builds research tools for WashU faculty

Washington University faculty engage in research so innovative that it often demands specialized tools that don’t yet exist.

As part of its mission to foster groundbreaking, data-driven research, the Transdisciplinary Institute in Applied Data Sciences (TRIADS) has formed its own software engineering team to help connect faculty with custom solutions to meet their research needs.

WashU Professor of Political Science Dino P. Christenson approached the team in search of a sophisticated tool to help draw connections between people’s web browsing behavior and political opinions, while providing ironclad privacy protection for users. The resulting Online Privacy-Protected Synthesizer is a simple plugin that users can install in their web browser, which will then deliver a treasure trove of browsing data to Christenson and his research partners at Boston University.

“Our team had the great pleasure of working with software engineer Jessie Walker , who helped us turn the basics of an app into a polished plugin for multiple browsers,” Christenson said. “Thanks to TRIADS and Jessie’s quick and excellent work—and additional funding from the Weidenbaum Center —we’re ready for preliminary data collection and a major grant proposal in the near future.”

Walker and the TRIADS software team’s work requires a great deal of nimble thinking, building collaborations with faculty from multiple fields of study and developing solutions to supercharge their research.

“We view our role not just as technical support providers but as strategic partners who help faculty achieve their research goals,” Walker said. “By offering personalized guidance and solutions, we aim to facilitate breakthroughs in their research and contribute to their success.”

TRIADS software engineers don’t need to possess the expertise level of their faculty collaborators to help drive innovative research. Instead, the role demands the ability to ask the right questions and listen to the resulting answers critically. 

Nan Lin , a professor in WashU’s new Department of Statistics and Data Science , connected with TRIADS software engineer Greg Porter to help implement a new algorithm for quantile regression analysis. While Porter isn’t an expert in quantile regression, he knows how to code. And when you’re crunching numbers at the level of Lin and his team, you need a tool built to handle the strain.

“Greg’s expertise and guidance were instrumental in achieving our goals, and we are truly grateful for his contributions,” Lin said. “Throughout the project, his insights and recommendations significantly enhanced our understanding, particularly in identifying areas for parallelization and optimizing code performance.”

The TRIADS software engineering team also offers consultations and technical guidance for WashU faculty. To learn more and schedule a meeting, visit the TRIADS website.

in the news:

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STL DataFest unites the region's data scientists

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TRIADS announces details of its 2024 Seed Grant Program

Data Science, Analytics and Engineering

MS Program information – All PhD Program information PhD Admissions PhD Faculty Tuition and fees Career services Request information – PhD How and when to apply

Program description

Data scientists are consistently ranked among the top jobs in the USA, and there is an increasing need for all engineers to make use of data science tools like statistics, machine learning, artificial neural networks and artificial intelligence. Yet, the majority of engineering occupations require subject matter expertise beyond data science.

Degree programs 

Data science, analytics and engineering ms.

The MS program in data science, analytics and engineering with a concentration in electrical engineering provides an advanced education in high-demand data science and electrical engineering. A focus on probability and statistics, machine learning, data mining and data engineering is complemented by electrical engineering-specific courses to ensure breadth and depth in data science and electrical engineering.

All students must choose a concentration:

• Computing and Decision Analysis
• Electrical Engineering
• Materials Science and Engineering
• Sustainable Engineering and Built Environment
• Bayesian Machine Learning
• Computational Models and Data
• Human Centered Applications
• Mechanical and Aerospace Engineering

Application deadlines

Fall: December 31 Spring: July 31

Data Science, Analytics and Engineering PhD

The PhD program in data science, analytics and engineering engages students in fundamental and applied research as preparation for careers in academia, government or industry. The program’s educational objective is to develop each student’s ability to perform original research in the development and execution of data-driven methods for solving major societal problems. This includes the ability to identify research needs, adapt existing methods and create new methods as needed for data analytics and engineering.

This degree program is a collaboration between the School of Computing and Augmented Intelligence  and the School of Mathematical and Statistical Sciences (SoMSS) and provides a rigorous education with research and educational experiences that allow students to pursue careers in advanced research, teaching or state-of-the-art practice. Graduates demonstrate proficiency with existing methodology and significant accomplishment at advancing the state of the art in their chosen area of data science, analytics and engineering.

Fall: January 15 Spring: September 15

Contacts for the MS program are specific to the desired concentration. See the DSAE MS website contacts list for the correct contact.

PhD program

School of Computing and Augmented Intelligence Graduate advising office

• On campus programs: [email protected]
• Online MCS students:  [email protected]

Data science, analytics and engineering

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Read our online brochure for more details about the data science, analytics and engineering graduate program.

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Congrats to Mohammad Nazmus Sakib for successfully proposing his Ph.D. Thesis

Mohammad Nazmus Sakib successfully presented his thesis proposal titled, "Advancing Processing-in-Memory through Integration of Emerging Non-volatile Devices and Novel Data Representation," on May 1, 2024. Congrats to Sakib and wish him a successful thesis defense in the near future.

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A law degree is often synonymous with high pay and prestige, but not all lawyers practice the way they’re portrayed in Hollywood. Attorneys can be public defenders, private attorneys, specialized lawyers, judge advocates in the military and serve in many more roles. The skills you learn in a J.D. program are versatile and could be helpful to careers outside of the law, too.

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