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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

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) 

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Ph.d. specialization committee.

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

Richard A. Davis

• Faculty of Arts and Sciences Howard Levene Professor of Statistics

Vineet Goyal

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

Garud N. Iyengar

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

Gail Kaiser

Rocco a. servedio, clifford stein.

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

John Wright

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

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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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 .

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.

NYU Center for Data Science

Harnessing Data’s Potential for the World

PhD in Data Science

An NRT-sponsored program in Data Science

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

The Fall 202 4 online application was deadline Tuesday, December 5, 2023, 5pm ET.

Our Fall 2024 PhD Admissions Information Session took place Thursday, October 26 at 1pm.

The Committee welcomes applications from candidates with relevant undergraduate/master’s degrees and candidates with work or research experience in data science. Relevant degrees include mathematics, statistics, computer science, engineering, and other scientific disciplines that develop skills in drawing inferences or making predictions using data. Coursework or equivalent experience in calculus, probability, statistics and programming are required.

Please visit the  GSAS Application Resource Center  for more information on the application process. Please see our FAQs page for additional information.

For a list of all of our faculty research areas,  please review our Areas and Faculty page.

DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

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

Requirements

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

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

In order to receive a doctoral degree, students must:

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

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

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

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

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

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

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

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

Optional MS degree en route to PhD

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

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

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

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PhD in Data Science and Analytics

Degrees & Programs

• Doctoral Degree in Data Science and Analytics
• Certificates

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

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

About the Doctoral Degree in Data Science and Analytics

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

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

Information Sessions for Fall 2025 Admission

To be announced

Data Science and Analytics PhD Curriculum

Stage One: Pre-Program Requirements

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

Stage Two: Coursework

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

• CS 8265  - Big Data Analytics
• CS 8267  - Machine Learning
• MATH 8010  - Theory of Linear Models (optional)
• MATH 8020  - Graph Theory
• MATH 8030  - Applied Discrete and Combinatorial Mathematics 
• STAT 8240  - Data Mining I
• STAT 8250  - Data Mining II
• Comprehensive Exam 
• 21 credit hours of electives in computer science, statistics, mathematics, information technology, or other area by permission. 
• Research Proposal 
• DS 9700 Doctoral Internship/Research Lab
• DS 9900 Dissertation
• Dissertation Proposal Defense
• DS 9900 DissertationFinal Dissertation Defense

Stage Three: Project Engagement and Research/Dissertation

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

Program Student Learning Outcomes

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

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

Admission Requirements and Application

Frequently Asked Questions (FAQ)

How long will the program take?

How much does the program cost?

Who would be successful in the program?

Where do these graduates work after graduation?

What are the publication/research requirements?

What did Science Doctoral Students Study?

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

What is the Project Engagement requirement?

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

Can I live on campus?

Are the courses online?

Do I have to have a masters degree to apply?

Where did Data Doctoral Students Study?

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

Ph.D. in Data Science and Analytics Student Cohorts

Royce Alfred

Bachelor's Degree:   Psychology, Kennesaw State University

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

Work History:   4 years as a Data Scientist at Equifax

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

Venkata Abhiram Chitty

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

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

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

Caleb Greski

Bachelor's Degree: 

Master's Degree: 

Work History: 

Courses Taught: 

Publications: 

Professional Objective: 

Moukthika Kadaparthi

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

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

Work History:  

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

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

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

Master's Degree:   Business Analytics, Syracuse University

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

Courses Taught:  Calculus I, Marketing Analytics, Data Mining

Awards:   Merit-Based Scholarship, Syracuse University

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

Kausar Perveen

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

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

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

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

Service and Awards:

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

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

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

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

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

Courses Taught:   Introduction to Statistics

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

Ayomide Isaac Afolabi

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

Master's Degree:  Data Science, Auburn University 

Work History:   Graduate Research Assistant, Auburn University 

Courses Taught:   Python Programming 

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

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

Dinesh Chowdary Attota

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

Master's Degree:   Computer Science, Kennesaw State University

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

Publications:  

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

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

Nzubechukwu Ohalete

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

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

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

Courses Taught:  Elementary Mathematics, Mathematical Methods

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

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

Ryan Parker

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

Master's Degree:   Integrative Biology, Kennesaw State University

Work History:  Instructor of Biology, Kennesaw State University

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

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

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

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

Askhat Yktybaev

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

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

Work History:

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

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

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

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

Sanad Biswas

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

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

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

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

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

Mallika Boyapati

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

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

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

Courses Taught:   DATA 4310 - Statistical Data Mining

Publications:

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

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

Nina Grundlingh

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

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

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

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

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

Namazbai Ishmakhametov

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

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

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

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

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

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

Symon Kimitei

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

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

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

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

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

Poster Presentations:

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

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

Jitendra Sai Kota

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

Master's Degree:   Computer Science, Florida State University

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

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

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

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

ResearchGate

Catrice Taylor

Bachelor's Degree:   Economics, Clemson University 

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

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

Sahar Yarmohammadtoosky

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

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

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

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

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

Martin Brown

Bachelor's Degree:  Mathematics, Swansea University, United Kingdom

Master's Degree:  Mathematics, Murray State University

• Graduate Research Assistant, Kennesaw State University, August 2020 to present
• Graduate Teaching Assistant, Murray State University, August 2018 to May 2020

Course Taught:  Problem Solving in Mathematics

Publications:   Brown, Martin K. W. "Evaluating an Ordinal Output using Data Modeling, Algorithmic Modeling, and Numerical Analysis" (2020).   Murray State Thesis and Dissertations 168 .

Awards:  David Pryce History of Mathematics Prize 2017-2018

Professional Objective:  To pursue a career in data science, machine learning, and predictive analytics to solve real-world issues 

Inchan Hwang

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

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

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

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

Duleep Prasanna Rathgamage Don

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

Master's degree:   Mathematics, Georgia Southern University

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

Courses Taught:   Trigonometry, and Calculus I & II

Publications/Presentations:

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

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

Linglin Zhang

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

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

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

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

Yihong Zhang

Bachelor’s Degree:   Psychology Mathematics Interdisciplinary, Chatham University

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

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

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

2019 - 2020

Trent Geisler

Graduation Date:   Summer 2022

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

Dissertation Advisor:   Dr. Herman Ray

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

Srivatsa Mallapragada

Bachelor’s Degree:  Mechanical Engineering, Andhra University College of Engineering, India

Master’s Degree: Mechanical Engineering, University of North Carolina at Charlotte

Continuous Improvement Intern, Daimler Trucks North America at Cleveland, North Carolina, USA Computational Fluid Dynamics (CFD) Graduate Research Assistant, NC Motorsports and Research Laboratory Manufacturing Intern, Caterpillar India Pvt Ltd, Sriperambudur, India Selected Publications/Presentations:

Mallapragada, S. (2017). Computational Investigations on the Aerodynamics of a Generic Car Model in Proximity to a Side Wall (Master’s thesis, The University of North Carolina at Charlotte). Uddin, M., Mallapragada, S., & Misar, A. (2018). Computational Investigations on the Aerodynamics of a Generic Car Model in Proximity to a Side-Wall (No. 2018-01-0704). SAE Technical Paper. Dimensionality Reduction of Hyperspectral Images for Classification, Srivatsa, M., Michael, W. & Hung, C. C. Ninth International Conference on Information ISSN: 1343-4500 Bounds, C., Mallapragada, S., and Uddin, M., "Overset Mesh-Based Computational Investigations on the Aerodynamics of a Generic Car Model in Proximity to a Side-Wall," SAE Int. J. Passeng. Cars - Mech. Syst. 12(3):211-223, 2019, https://doi.org/10.4271/06-12-03-0015. Service and Awards: Base SAS Programmer V9 Professional Objectives: I am currently working in unsupervised pattern recognition in high dimensional data sets. After I graduate, I would like to pursue a career in Data Science and Machine Learning in the corporate environment.

Sudhashree Sayenju

Graduation Date:   Spring 2023

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

Dissertation Advisor:   Dr. Ramazan Aygun

Christina Stradwick

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

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

Courses Taught:  Prep for College Algebra at Marshall University

Selected Presentations:

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

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

2018 - 2019

Md Shafiul Alam

Graduation Date:   Fall 2022

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

Dissertation Advisor:   Dr. Ying Xie

Current Position:   AI Framework Engineer, Intel Corporation

Jonathan Boardman

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

Current Position:   Data Scientist, Equifax

Tejaswini Mallavarapu

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

Master’s Degree:   Computer Science, Kennesaw State University

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

Selected Publications:

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

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

Seema Sangari

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

Dissertation Advisor:   Dr. Michael Whitman

Current Position:   Principal Modeler, HSB 

Srivarna Settisara Janney

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

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

Selected Publications/Presentations:

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

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

2017 - 2018

Graduation Date: Summer 2021

Dissertation: Incentive-based Data Sharing and Exchanging Mechanism Design

Dissertation Advisor: Dr. Meng Han

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

Mohammad Masum

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

Dissertation Advisor: Dr. Hossain Shahriar

Current Position: Assistant Professor, San Jose State University

Lauren Staples

Graduation Date: Fall 2021

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

Dissertation Advisor: Dr. Joseph DeMaio

Current Position: Senior Data Scientist, Microsoft

2016 - 2017

Shashank Hebbar

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

Dissertation Advisor: Dr. Ying Xie

Current Position: Data Scientist, Credigy

Jessica Rudd

Graduation Date: Summer 2020

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

Dissertation Advisor: Dr. Herman Ray

Current Position: Senior Data Engineer, Intuit Mailchimp

Graduation Date: Spring 2020

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

Dissertation Advisor: Dr. Sherry NI

Current Position: Applied Scientist II, Amazon

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

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

Dissertation: Attack and Defense in Security Analytics

Dissertation Advisor: Dr. Selena He

Current Position: NLP Data Scientist, NBME

2015 - 2016

Edwin Baidoo

Graduation Date:  Spring 2020

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

Dissertation Advisor:  Dr. Stefano Mazzotta

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

Bogdan Gadidov

Graduation Date:  Summer 2019

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

Dissertation Advisor: Dr. Mohammed Chowdhury

Current Position: Data Scientist, Variant

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

Dissertation Advisor:  Dr. Mingon Kang

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

Graduation Date:  Spring 2019

Dissertation:  Deep Embedding Kernel

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

Bob Venderheyden

Graduation Date: Fall 2019

Dissertation:  Ordinal Hyperplane Loss

Dissertation Advisor:  Dr. Ying Xie

Current Position:  Principal Data Scientist, Microsoft

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PhD in Computing & Data Sciences

For more information and to get in touch, please visit the Faculty of Computing & Data Sciences website .

The PhD program in Computing & Data Sciences (CDS) at Boston University prepares its graduates to make significant contributions to the art, science, and engineering of computational and data-driven processes that are woven into all aspects of society, economy, and public discourse, leading to solution of problems and synthesis of knowledge related to the methodical, generalizable, and scalable extraction of insights from data as well as the design of new information systems and products that enable actionable use of those insights to advance scholarly as well as practical pursuits in a wide range of application domains.

Applicants to the PhD program in CDS are expected to have earned a bachelor’s or master’s degree in one of the methodological or applied disciplines relating to the computational and data-driven areas of scholarship in CDS. They are expected to possess basic mathematical and computational competencies, and demonstrable propensity for cross-disciplinary work. To accommodate a diversity of student backgrounds and preparations, a holistic admission review is utilized. As such, GRE tests and scores are not required, but could be optionally provided and considered as part of the applicant’s portfolio, which may also include evidence of prior, relevant preparation, including creative works, software code repositories, etc. Special attention will be paid to applicants from underrepresented minorities in computing and data science disciplines.

Completion of the PhD degree in CDS requires coursework covering breadth and depth topics spanning the foundational, applied, and sociotechnical dimensions of computing and data science; completion of research rotations that expose students to ongoing projects; completion of a cohort-based training on ethical and responsible computing; and successful proposal and defense of a doctoral thesis.

For their thesis work, and in preparation for careers in academia, industry, and government, CDS PhD students are expected to pursue theoretical, applied, or empirical studies leading to solution of new problems and synthesis of new knowledge in a topic area determined in consultation with their mentors and collaborators, which may include external researchers and practitioners in industrial and academic research laboratories.

Upon completion of the program, students will be prepared to pursue careers in which they lead independent cutting-edge research and development agendas, whether in academia (by teaching, mentoring, and supervising teams of students engaged in scholarly pursuits) or in industry (by collaborating, directing, and effectively managing diverse teams of practitioners working at the forefront of industrial R&D).

Learning Outcomes

The following learning outcomes explain what you will be able to do at the end of your time as a CDS PhD candidate, as a result of earning your degree.

• Exhibit a strong grasp of the principles governing the design and implementation of the methodological approaches for computational and data-driven inquiry.
• Identify the literature and demonstrate mastery of the compendium of works relevant to a well-defined area of research inquiry in computing and data sciences.
• Show capacity to engage meaningfully in and materially contribute to multidisciplinary research and development endeavors.
• Evidence a strong sense of social and professional responsibility for decisions related to the development and deployment of computational and data-driven technologies.
• Assess and argue the merits, limitations, and possibilities of new research work in a specialized area at the level commensurate with standards of scholarly venues in that area.
• Formulate and pursue a research agenda leading to solution of new problems and to synthesis of new knowledge shared through peer-reviewed publications.

Course Requirements

Sixteen semester courses (64 credits) are required for post-BA/BS students and 12 semester courses (48 credits) are required for post-MA/MS students. Students with prior graduate work (including master’s degrees) may be able to transfer up to two courses (8 credits) as long as these credits were not used to fulfill matriculation requirements, upon the recommendation of the student’s academic advisor, and subject to approval by the Associate Provost for CDS.

Of the 16 courses, up to 3 undergraduate courses (12 credits) may be counted as background courses, selected in consultation with the student’s academic advisor and subject to approval by the Associate Provost for CDS. Other than these remedial courses, all other courses must be graduate-level courses or directed studies offered by CDS or by other BU departments in order to satisfy the following degree requirements.

The methodology core requirement ensures that students possess foundational knowledge and competencies in a subset of the following eight methodological areas of CDS:

• Mathematical Foundations of Data Science
• Statistical Modeling and Inference
• Efficient and Scalable Algorithms
• Predictive Analytics and Machine Learning
• Combinatorial Optimization and Algorithms
• Computational Complexity
• Programming and Software Design
• Large-scale Data Management

A list of courses that can be used to satisfy these competencies will be maintained on the website for CDS. Students who start their PhD program in CDS are expected to satisfy at least six of these competencies. Students who complete the course requirement for the PhD program in a cognate discipline are expected to satisfy at least four of these competencies.

The subject core requirement ensures that students establish depth in one area of inquiry that is aligned with either the methodological or applied dimensions of CDS. Subject areas are defined by groups of CDS faculty members working in related disciplinary and/or interdisciplinary areas of research who expect their prospective students to have enough depth in the subset of topics to enable them to tackle doctoral-level research in these topics. The set of subject areas as well as a list of preapproved graduate-level courses offered in CDS or elsewhere at BU that can be used to satisfy each subject area will be maintained on the website for CDS.

During the first two years in the program, all PhD candidates in CDS must complete three cohort-based requirements; namely, a two-semester training course (4 credits) covering various aspects of the responsible and ethical conduct of computational and data-driven research, a two-semester doctoral seminar (4 credits) that introduces them to the research portfolios of CDS faculty members as well as to the skills and capacities needed for success as scholars, and at least two research or lab rotations (8 credits) that expose them to real-world computational and data-driven applications that must be tackled through effective multidisciplinary teamwork.

A cumulative GPA not less than 3.3 must be maintained for all non-Pass/Fail courses taken to satisfy the methodology core requirement and the subject core requirement of the degree, excluding any background courses and excluding any transferred credits. Students who receive grades of B– or lower in any three courses taken at BU will be withdrawn from the program.

Language Requirement

There is no foreign language requirement for the PhD degree in CDS.

Qualifying Examinations

No later than the end of the sixth semester (third year), all PhD candidates in CDS must pass a public oral examination administered by a committee of three faculty members, chaired by the student’s research (and presumptive thesis) advisor or coadvisors. The oral area exam is meant to establish the student mastery of a well-defined area of scholarship and preparedness to pursue original research in that area. The oral area examination may require completion of a survey paper or completion of a pilot project ahead of the examination. The scope as well as any additional requirements needed for the examination should be developed in consultation with and approval of the research advisor(s), at least one semester prior to the exam.

Dissertation and Final Oral Examination

Candidates shall demonstrate their abilities for independent study in a dissertation representing original research or creative scholarship. A prospectus for the dissertation must be successfully defended no later than the end of the eighth semester (fourth year) of study.

Candidates must undergo a final oral examination no later than the end of the 10th semester (fifth year) of study in which they defend their dissertation as a valuable contribution to knowledge in their field and demonstrate a mastery of their field of specialization in relation to their dissertation.

Both the prospectus and final dissertation must be administered by a dissertation committee of at least three readers (including the dissertation advisor or coadvisors) and chaired by a CDS faculty member who is not one of the readers.

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Boston University is accredited by the New England Commission of Higher Education (NECHE).

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PhD Program

Wharton’s PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include: analysis of observational studies; Bayesian inference, bioinformatics; decision theory; game theory; high dimensional inference; information theory; machine learning; model selection; nonparametric function estimation; and time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

Apply online here .

Department of Statistics and Data Science

The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686

Phone: (215) 898-8222

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Ph.D. in Data Science

(Qualifying students may be eligible for an application fee waiver. Contact Dr. Hai Phan, program director, at [email protected] for further information.)

Considering the Ph.D. in Data Science

Why pursue a ph.d..

You are the master of your professional destiny.

The NJIT Advantage

Our renowned research makes a world of difference

The world is waiting for people like you. Take the next step ahead.  

The Ph.D. in Data Science is jointly administered by the Department of Data Science in the Ying Wu College of Computing and the Department of Mathematical Sciences in the College of Science and Liberal Arts. To accommodate different interest profiles of students, the program offers two options. There is significant overlap between the two options.

Computing Option

Explore the path to innovation

Statistics Option

Formulate the solution for transformation

Contact the Program Director

Students graduating with a PhD degree in Data Science should anticipate the acquisition of skills, knowledge, and professional training that will enable them to pursue data science careers such as data scientist, data analyst, data engineer, data miner, and academic data science researcher in a broad range of industrial sectors, startups, academia, and government institutions. The primary goal of the PhD degree in Data Science is to educate students who have the necessary skills and knowledge to pursue competitive professional and academic careers, swiftly advancing to leadership positions and to contribute to the creation of novel insights and knowledge in the field.

Application deadlines are October 15 for spring and December 15 for fall. However, we will continue to accept applications after the deadline for qualified candidates.

Prospective applicants are expected to have software development experience, computational skills, and an understanding of statistical methods. The minimum requirements for admission to the PhD program are within the guidelines and policies approved by the University and include:

• A Bachelor’s degree in data science, computer science, informatics, mathematics/statistics, engineering, or another closely related discipline (as approved by the PhD directors) from a college or university accredited in the United States, or its equivalent, with an expected overall GPA of 3.5 out of 4.0.
• GRE scores are required. They will be evaluated in agreement with other Ph.D. programs at NJIT.
• Prepared students shall have a good background in programming and data structures (corresponding to NJIT CS 280 and CS 435), multivariate calculus (e.g. NJIT Math 211), and Probability and Statistics (e.g. Math 333/341). Admitted students lacking competencies in one or more of these areas shall consult with the academic advisor to take relevant preparatory courses. 
• International student applicants shall demonstrate proficiency in English if it is not their first language, following the NJIT admission standard. Exemptions can be granted to applicants who have earned (or will earn, before enrolling at NJIT) a Bachelor’s, Master’s, or Doctoral degree from a university of recognized standing in a country in which all instruction is provided in English.

Progression of Students

To continue in the Ph.D. program, a student must fulfill the following requirements/milestones:

Maintain a cumulative GPA of 3.0 or better. Students will need a cumulative GPA of 3.5 if they wish to be considered for financial support of any kind.

End of year one: Students must take the written part of the Ph.D. qualifying exam.

Every student (in both options) will have to pass qualifying exams in these two courses:  

CS 675     Machine Learning

MATH 644    Regression

• CS 644   Introduction to Big Data OR   IS 650   Data Visualization & Interpretation
• MATH 631    Linear Algebra

Upon the approval of the PhD program director, students must file a program of study that lists the courses to be taken and the timeline of study. 

Dissertation

Students are recommended to choose a dissertation advisor as soon as possible, but no later than 3 months after passing the qualifying exam. A student needs to inquire who among the tenured/tenure track faculty is closest to their area of research interest. The Ph.D. program director should be consulted for this purpose, unless the student has already determined who they wish to work with, e.g., based on class offerings or publication records. 

Students will have to pass the oral part of the qualifying exam, followed by registering for research credits. They have to present, orally and in writing, a Dissertation Proposal and, before graduating, have to write and orally defend a state-of-the-art research dissertation in front of a committee of faculty members.  Individual professors will impose publication requirements in conferences and/or academic journals as a condition for graduating. 

“Data is the new oil. Like oil, data is valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity. So must data be broken down, analyzed for it to have value.” - The British mathematician Clive Humbly

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Application Checklist

Applications to HDSI graduate programs must be submitted online using UCSD’s Application for Graduate Admission . 

Statement of Purpose

An applicant’s statement of purpose is very important and is given careful consideration in the selection process. For additional guidelines, please refer to  Statement of Purpose . There is no specific word limit, but be concise and specific in preparing your statement, giving information that demonstrates your level of preparation and potential for success in graduate school.The statement of purpose must be submitted online through the  UCSD Application for Graduate Admission .

NOTE : Requests to update the Statement of Purpose  are not granted  after submission. Please carefully double-check edits prior to official submission.

An applicant’s Curriculum Vitae or Resume is used to gain a better understanding of the individual’s potential for success in this program. It should highlight details such as academic history, relevant work experience, skills, publications, honors/awards, etc. A resume or curriculum vitae should be uploaded to the   UCSD Application for Graduate Admission .

Three Letters of Recommendation

Three (3) letters of recommendation are required. Additional letters may be submitted, however, you must submit a minimum of three (3) letters.

LORs are submitted via the online application only. For additional information about the procedures and policies for letters of recommendation, please review the UCSD Graduate Division guidelines at  Procedures for Letters of Recommendation .

Transcripts

Unofficial transcripts should be uploaded online to the  UCSD Application for Graduate Admission . More information about how to upload unofficial transcripts can be found on this website:  Academic Records and Transcripts

Official transcripts from all prior higher education institutions attended will be required  AFTER  an applicant receives their official offer of admission from UCSD. Applicants who attended any campus of the University of California, including UCSD, must provide official transcripts of the UC coursework. Transcripts from UCSD may be ordered by an applicant from the Office of the Registrar. There is no charge for UCSD transcripts of record sent to departments in support of an application for graduate student.

Test Score Reports

Applicants for Fall 2024 Admission to the Data Science Graduate Programs (MS and PhD) will not need to provide a GRE score with their applications for Admission.

If applicants have already completed the GRE and would like to include their scores with their application, they can include these scores when submitting the application for review. Applicants with valid test scores should request that ETS submit the scores directly to the UC San Diego institution code 4836 ; the department codes are not needed. Information about the GRE is available from the  Educational Testing Service  (ETS) website.

English Language Proficiency 

Demonstrated proficiency in the English Language is required for all international applicants whose native language is not English.

UC San Diego accepts both the Test of English as a Foreign Language (TOEFL) and the International English Language Testing System (IELTS) scores.  To learn more about the minimum score requirements and possible exemptions, please visit UC San Diego’s English Language Proficiency  website.

Students may us the following Institution code when submitting official scores:  4836 . The Department code can be left blank.

Application Fee

Applicants will be required to pay the application fee for each application submitted or to request a fee waiver at the end of the online application. Your application cannot be reviewed until this fee is paid.

• US Citizens, Permanent Residents, and Undocumented Applicants: $135.00
• International Applicants: $155.00

Applicants may submit multiple applications for the same term. Please note that each application must be submitted to a different department and to only one degree level (Master or PhD) per department.

For information regarding fee waivers, please review the  Graduate Division  website.

After You Apply

• The HDSI admissions team reviews applications as they are received. There is no need to follow up with the admissions team, you will receive communication regarding your status by March 2023.

Dates and Deadlines: Fall 2024 Cohort

• Application submission deadline: December 15, 2023
•   *PhD DELAY*  PhD applicants can expect to receive offer letters by April 15, 2024.
• **PhD DELAY**   Admitted PhD students will have until May 1, 2024 to submit their decisions.
• Mandatory Orientations take place before instruction begins and are announced to admitted students.

In order to provide the tools and resources needed to achieve academic, professional, and personal development goals, the Data Science program has a committed staff available to support students and their needs.

If you have any questions or concerns regarding admission, please reach out to our team via [email protected] .

Laura Horton

Responsible for graduate student admissions and recruitment.

Back up: Julia Nemeth

What are the requirements for admission to the program?

Our program does not have specific prerequisite courses or undergraduate degree requirements for admission. Our aim is to build a diverse cohort with a range of expertise and interests.

More information regarding the courses required once admitted can be found on our MS Program Requirements and PhD Program Requirements webpages.

What is the cost of attendance for a graduate program at UCSD?

The tuition and fees breakdown for the in-person MS and PhD programs can be found on the UCSD Graduate Division Tuition & Fees webpage.

Are the in-person MS and PhD programs in Data Science STEM designated?

Yes, Our in-person graduate degrees are STEM designated.

For more information about the designation or OPT Extensions, please visit the UCSD Graduate Division STEM OPT Extension webpage.

I am having trouble submitting part of my application, who should I contact?

If you are a current or former UCSD student, you will not be able to submit your application online. Please make sure your application is complete then email HDSI’s Graduate Team ( [email protected] ) so that our staff can submit your materials for review.

For non-UCSD applicants, please reach out to the UCSD Graduate Division office via email ( [email protected] ) or by visiting their website for more contact information.

I am a current undergraduate student at UCSD, how do I apply to a graduate program in Data Science?

I am a current graduate student at ucsd, how do i change my program of study to data science, are gre scores required.

No, GRE scores are not required for the Fall 2024 cohort applications.

Please check back on this website for GRE requirements in future academic years.

I am an international student with a degree from an international institution where English was the primary language of instruction. Do I need to submit TOEFL/IELTS scores?

Non-native english speakers may be exempt from the English Proficiency Requirement (TOEFL/IELTS) if they earned or will be earning a bachelor’s, master’s, or doctoral degree with grades of B (3.0) or better from either

• A regionally accredited U.S. college or university where English is the sole language of instruction, or
• A foreign college or university which provides instruction solely in English. You may verify whether your institution meets this requirement by looking up your institution in the IAU World HIgher Education Database (WHED).

More information can be found on the UCSD Graduate Division English Language Proficiency webpage.

PhD in Data Science

One of the first programs of its kind, in the nation, WPI’s interdisciplinary PhD in Data Science recognizes that traditional data processing applications can no longer handle today’s large and complex datasets. New models are needed to handle big data; and knowledgeable graduates with expertise in turning those observations into meaningful recommendations are in high demand.

Value Proposition Description

You’ll be working alongside faculty and industry partners to analyze, capture, search, share, store, transfer, query, and visualize huge amounts of data to solve real-world challenges. Some broad-stroke examples:

• using predictive analytics to identify cyber threats
• employing big data analytics to improve healthcare outcomes
• empowering “smart” cities to make data-driven policy changes critical for societal well-being

Applying to the Data Science PhD Program

Students applying to the data science PhD program will find WPI’s data science degree options listed with engineering, science, and mathematics on the application form.

WPI’s PhD in data science is interdisciplinary, drawing from Computer Science , Mathematical Sciences , and the Business School . Together, courses and dissertation research revolve around five key areas:

• Integrative Data Science
• Business Intelligence and Case Studies
• Data Access and Management
• Data Analytics and Mining
• Mathematical Analytics

PhD requirements include coursework as well as a research component. Together they total a minimum of 60 credit hours beyond the  Data Science master’s degree requirement . Students entering the Ph.D. Program with a bachelor’s degree first complete the M.S. in data science at WPI using the M.S.  Thesis  option as first step towards their Ph.D. degree. Each Ph.D. student is assigned an Academic Advisor and together they formulate a Plan of Study that then is approved by the Data Science Steering Committee.

• Core coursework requirements in the 5 categories as detailed below – 21 credits / 7 courses
• Electives in coursework – 9 credits / 3 courses
• Research credits – 30 credits TOTAL 60 credits (beyond MS program)

A Ph.D. student must obtain core competency by taking 7 courses from the below list of Data Science core areas, with an A grade in 4 out of the 7 courses and at least a grade B for the remaining 3 courses,  within 2 years after starting the Ph.D. 60 program.

Integrative Data Science  (Required) DS 501. Introduction to Data Science (3 credits)

Mathematical Analytics 3 credits (Select at least one) DS 502. Statistical Methods for Data Science (3 credits) MA 542. Regression Analysis MA 554. Applied Multivariate Analysis

Data Access and Management 3 credits (Select at least one) CS 542. Database Management Systems (3 credits) MIS571. Database Applications Development DS 503. Big Data Management (3 credits) CS 561. Advanced Topics in Database Systems

Data Analytics and Mining 3 credits (Select at least one) CS 548. Knowledge Discovery and Data Mining (3 credits) DS 504. Big Data Analytics (3 credits) CS 539. Machine Learning

Business Intelligence and Case Studies 3 credits (Select at least one) MIS 584. Business Intelligence MKT 568. Data Mining Business Applications

Nine more course credits must be taken, with the listing of courses pre-approved as electives for the Data Science program found in the WPI Graduate Catalog under  Data Science . Other graduate courses, graduate research credits, or ISGs may also be used, with prior approval of the Data Science Steering Committee. 

Two elective courses designed as ramp up courses for students who may be lacking in sufficient background in either statistics or programming, respectively, are listed below. They can count towards the 33 credits of the DS MS degree, However, they cannot be used to meet the above requirements in five core categories.

MA 511. Applied Statistics for Engineers and Scientists

CS 5007. Intro to Applications of CS with Data Structures and Algorithms (Programming for non-CS majors)

At least 30 credits must be research credits, consisting of DS 597 Directed Research and DS 699 Dissertation Research. Prior to Admission to Candidacy, a student may receive up to 18 credits of Pre-Dissertation Research under DS 597. Only after Admission to Candidacy by passing the Research Qualifier may a student receive credit toward Dissertation Research under DS 699.

As part of the research component, PhD students pass a Qualifying Examination and propose and defend Dissertation Research.  Learn more about the Ph.D. milestones, including the Ph.D. Qualifying Examination, the Ph.D. dissertation proposal and Ph.D. final dissertation defense.

Software Tools & Labs

The Data Science Innovation Lab is dedicated workspace for project work by students in the Data Science program. Robust servers and computer clusters are available for experimenting with large-scale datasets throughout labs at WPI, including many interdisciplinary facilities.

State-of-the-art software programs:

Close faculty interaction, cutting-edge equipment, and personal attention let you structure your program so it suits your individual career goals. You’ll leave with a degree that will help you succeed in your distinctive path.

Data science research gives you opportunities to work on grand challenge problems with societal importance, including topics such as cybersecurity, healthcare, and sustainability.

Our data science graduate program offers expertise in computer science, statistics, and business topics while giving you essential opportunities to work with industry partners.

WPI’s innovative and multidisciplinary graduate program prepares students to become talented and effective leaders in this rapidly evolving field.

WPI faculty and candidates in the PhD in data science are exploring every aspect of this burgeoning field. Together, they’re fueling breakthroughs that have direct, real-world impact in health, genetic analysis, sustainability, educational software, financial trading, and more.

A faculty advisor will help you design a Plan of Study for your dissertation as well as coursework in the core areas of data analytics and big data computing, statistical foundations and mathematical analytics, and business intelligence and innovation.

Cassandra DB2 Hadoop IBM Cognos IBM ILOG CPLEX IBM SPSS Modeler InfoSphere Big Insights InfoSphere Streams Mahout Maple MATLAB

MySQL Oracle Server Palisade DecisionTools Suite R RapidMiner SAS Spotfire SQL Server Tableaux Weka

Faculty Profiles

As founding Head of the interdisciplinary Data Science program here at WPI, I take great pleasure in doing all in my power to support the Data Science community in all its facets from research collaborations, and new educational initiatives to our innovative industry-sponsored and mentored Graduate Qualifying projects at the graduate level.

Professor Kong’s research interests focus on data mining and machine learning, with emphasis on addressing the data science problems in biomedical and social applications. Data today involves an increasing number of data types that need to be handled differently from conventional data records, and an increasing number of data sources that need to be fused together. Dr. Kong is particularly interested in designing algorithms to tame data variety issues in various research fields, such as biomedical research, social computing, neuroscience, and business intelligence.

Yanhua Li is an Associate Professor in the Computer Science Department and Data Science Program at Worcester Polytechnic Institute (WPI). His research interests focus on artificial intelligence (AI) and data science, with applications in smart cities in many contexts, including spatial-temporal data analytics, urban planning and optimization.

My research focuses on compressed sensing, machine learning, signal processing, and the interaction between mathematics, computer science and software engineering. My interests range from theoretical results to algorithms for tackling practical applied problems, and I enjoy problems most when mathematical results lead to efficient software implementations for big data. I am looking forward to working with students at all levels and backgrounds who share an interest in mathematics, software, or data.

I am Associate Professor of Operations and Industrial Engineering at Worcester Polytechnic Institute (WPI), with courtesy professorships in Mathematical Sciences and Data Science. I hold a Ph.D. in Industrial Engineering from the University of Pittsburgh. My objective is to use science and technology to assist real human need by improving systems that serve vulnerable peoples, such as refugees and asylum seekers, survivors of human trafficking, and children in the foster care system.

The Data Science Community

Getting Involved

We’re data scientists – we use tools of the trade and big data analytics to innovate. Follow department happenings and industry trends via our social media channels on  Facebook  and  LinkedIn .

Refer a Friend

Do you have a friend, colleague, or family member who might be interested in Worcester Polytechnic Institute’s (WPI) graduate programs? Click below to tell them about our programs.

Need to Earn a Master’s First? Explore Our Pioneering MS in Data Science

Not quite ready to apply for our data science PhD program since you first need to earn a master’s? Our pioneering master’s in data science dives into how to articulate findings into how to synthesize huge amounts of data and articulate findings into innovative solutions. WPI is one of a handful of universities that offers a MS in data science. Are you a working professional and prefer to study online? Our master’s in data science online makes it possible for you to advance your expertise from wherever you are conveniently online. Our core courses dive into the same on campus data-science essentials like analysis techniques, database management, and more. Maybe you’re excited about elevating your career in big data, but have questions about PhD data science salary and popular job titles. Check out our career information for data science.

Advance Your Data Science Skills with a Graduate Certificate

Individuals who know how to interpret and harness large amounts of data are in high demand and our graduate certificates are a perfect way to customize your data science skills and aspirations. Our on campus graduate certificate in data science enables students to select six courses that dive into mathematical analytics, data management, business intelligence, and more. Maybe you have a busy work-life balance and prefer to study online? Our online data science certificate brings world-class instruction right to you with flexible course offerings that enable you to enhance your data analytics skills.

Ready to Start Your Data Science Path?

If data intrigues you and you love the idea of finding patterns and revealing the information in massive amounts of data, a future in data science likely appeals to you. If you have your sights set on a PhD in data science, you can get started on the right academic path with a bachelor’s in data science . WPI’s bachelor’s program offers hands-on projects to increase your understanding of the field while giving you real skills you can use. If you’re majoring in another field, such as business or computer science, a minor in data science will give you a solid understanding of data science concepts. With a minor in data science, you’ll gain skills and learn how to apply them to your chosen discipline.

WPI is proud to be the recipient of not one, but two National Science Foundation Research Traineeship programs. The programs provide exceptionally talented graduate students with specialized training and funding assistance to join careers at the forefront of technology and innovation. The programs are for graduate students in research-based master's and doctoral degree programs in STEM. Learn more .

The BioPoint Program for Graduate Students has been designed to complement traditional training in bioscience, digital and engineering fields. Students accepted into one of the home BioPoint programs will have the flexibility to select research advisors and take electives in other departments to broaden their skills. BioPoint curriculum is designed to be individual, interactive, project-focused and diverse, and includes innovative courses, seminars, journal clubs and industrial-based projects. Learn more .

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Data Science PhD Programs

A PhD in data science is a research-intensive degree that relies heavily on mathematics and computation to extract information from large data sets to make deductions or spot patterns and trends. 

Typically, a PhD in data science degree is interdisciplinary and is mainly offered as a part of a STEM program in computer science, engineering, mathematics, or statistics. A PhD in a specific data science-related area can be a good option for curious people who want to learn independently. 

Ad techguide.org is an advertising-supported site. Clicking in this box will show you programs related to your search from schools that compensate us. This compensation does not influence our school rankings, resource guides, or other information published on this site. Got it! Featured Analytics Programs

The field of data science and data science careers can be found in almost every industry, and the role of a data scientist continues to grow and evolve.

The actual job of a data scientist looks different for different organizations, and there is much more to it than the knowledge of software tools and the domain in which it is applied. 

To be successful as a data scientist , you need to have the right kind of data to solve your problem, the ability to understand business problems and the skills to apply the right kind of process to solve the problem. These skills get honed with practice and experience. 

Before we get into more details about getting a PhD in data science, one thing to know is that it is not mandatory to get a PhD to get most data science-related jobs.

Whether to get a PhD or not in data science depends on the kind of data science roles you are pursuing. Check out the master’s in data science guide for more details .

However, some roles are more research-oriented or need niche expertise, such as natural language processing (NLP), linguistics, speech recognition, etc.

At times, these roles might need you to create and develop algorithms from scratch. This requires research experience, and a PhD becomes relevant to the role of a data scientist. 

Through a PhD, individuals learn many skills to prepare for the commercial and academic world. This includes code, formulating questions, researching, creating technical documents, and solving problems. 

Trending Research Areas in Data Science

Insights into the research trends that have been going on in data science can be seen from the proceedings of well-regarded research conferences. For curious people, we have listed below some of the research areas in the field of data science and artificial intelligence trending today:

• Big Data on the Cloud
• Use of Augmented Analytics
• Focus on Edge Intelligence
• Automation of Data Cleaning
• Responsible AI
• Data-centric AI
• Increase in Use of Natural Language Processing
• Generative AI for Deepfake and Synthetic Data

Best PhD in Data Science Programs for 2024

Boise state university, capitol technology university, chapman university, clemson university, colorado technical university, florida atlantic university, george mason university, harrisburg university of science and technology, icahn school of medicine at mount sinai, indiana university.

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

Data Science PhD Program Overview and Curriculum

Here is a walkthrough of what a journey into a PhD looks like from beginning to end:

Admission Process

In general, admission requirements for most of the institutions include:

• Undergraduate and graduate transcripts
• GRE scores (may or may not be optional)
• TOEFL (English as a foreign language test, which may or may not be optional)
• A statement of intent for the program (reason for applying and plans)
• Letters of reference from undergrad professors or work supervisors if already working.
• Application fee (may be waived or reduced)
• Online application
• A curriculum vitae or resume (outlining all of your academic and professional accomplishments)

Post-Admission Process

• Every PhD program requires a student to complete a minimum number of credits to fulfill eligibility criteria. These credits can be a test of your knowledge, either at a foundational or advanced level. 
• Working on research projects over the first one or two years of the program will prepare you to frame the right questions, work on real-world data issues, and develop the necessary skill set required in the chosen data science-related topic.
• A qualifying exam is mandatory for every PhD program. These exams are designed to assess the candidate’s ability to meet the prerequisite standards/eligibility criteria. The assessment analyzes theoretical and practical understanding of the subject needed/required to work on your research project.
• Teaching undergraduate classes provides you with opportunities and experiences that will set you up for a future in academia.
• The dissertation proposal contains the hypothesis of your research that should meet the standards of publications in data analytics. The committee/faculty members need to approve the proposal before any proceedings to work on it.
• Students are expected to present their original work on the dissertation proposal. They are supposed to have expertise in their topic of dissertation. This is a crucial aspect of obtaining a doctoral degree in data science. It denotes that the student has mastered all of the necessary skills to undertake independent research that will contribute to the advancement of the field after completing their degree.
• Besides credits and qualifying exams, attending or presenting your research work at conferences, seminars, conventions, and summits provides you with an opportunity to network and form connections to boost your career. PhD can be fun as well as stressful. You can consider this degree as a marathon race that requires you to focus while testing your endurance for four to five years and finally providing you with the experience of a lifetime.

Online PhD Data Science Degree Programs

An online version of a PhD program allows individuals who have other work or family responsibilities to continue their education, albeit taking away some opportunities to network in person and in traditional ways. Students should verify the authenticity of online PhD programs by validating the institution’s accreditation before deciding to enroll. 

Traditionally, not many universities have offered completely online data science PhD programs. Mainly this reflects the hands-on nature of the degree that requires teaching, collaboration, and research. 

However, due to the COVID-19 pandemic, many traditional education programs have shifted courses and learning opportunities to digital platforms.

Because of technological advances in the EdTech industry, universities are becoming more comfortable hosting online classes and communication whenever needed. This might mean that some data science PhD work can be done online or remotely.

The best practice would be to contact the PhD programs you are interested in and inquire about online options. It is also a good idea to reach out to professors that would act as PhD advisors and be sure that they are willing and able to support long-term online learning and research.

Most universities provide funding for PhD students and programs in stipends, research, and teaching assistantships. If your preferred choice of university does not provide funding, it is a good idea to look into external financial assistance such as scholarships and fellowships.

Prior admission tuition fees must be considered when enrolling in a PhD program.

Career Paths and Outcomes

A PhD in data science may be offered from different departments at the university, such as statistics, computer science, mathematics, business, or even medical sciences.

Due to the interdisciplinary nature of data science PhD programs, career tracks, and research opportunities are numerous and diverse.

Academic Outcomes

• With a PhD, one can get hired in academia as a postdoctoral researcher or a fellow to advance their experience further. They can also begin their career as an associate professor in a university.

Industry Outcomes

• A data scientist ’s main focus or mission is to assist companies, organizations, and research efforts to resolve data-related problems. These problems range from user behavior to security risk factors and understanding consumer sentiment concerning the company’s products and services.
• A research scientist/quantitative researcher will be a part of the R&D team of the company or industry. A research scientist is responsible for developing hypotheses, conducting research, and building profitable business outcomes. A typical example would be handling a survey that identifies the latest trends and patterns of consumers’ lifestyle, income, and expenditures, then building/ improving a product or a service to generate profits.
• A chief data officer is the head of the organization’s data operations. A CDO brings their expertise to lead strategies and the ability to create models that use data to transform business strategies. A typical day as a CDO involves formulating data governance and management frameworks. Other tasks revolve around building data warehouses as a central repository for all information within a corporation.

Frequently Asked Questions

A doctoral degree is the training expected at many top universities for professors, researchers, and principal investigators in academia. A doctoral degree provides an edge where professional duties are research-oriented in corporate offices. According to Stitchdata, over 79 percent of data scientists who list their education data have earned a graduate degree. Of those, 38 percent have earned a PhD.

Regardless of how much funding the institution provides, it is advisable to always look for external financing and scholarships. The reason is that funding could change year-to-year at the institution or program level, so having a backup plan is always a good idea because doctoral studies usually take four to five years to complete.

An educational background in a quantitative field with a strong research interest is preferred by most schools. Since data science is interdisciplinary, universities may have prerequisites other than a background in mathematics or statistics.

Like other PhD programs, the GPA (which reflects previous academic experience) carries significant weight during the admissions process. However, since the PhD admissions process can vary greatly from program to program, it is good to check with the university about what factors factor into its admissions decisions.

PhD in Data Science Program List

Bowling green state university, indiana university-purdue university indianapolis, jackson state university, kennesaw state university, new jersey institute of technology, new york university, northcentral university, stevens institute of technology, university of massachusetts boston, university of nevada reno, university of tennessee-knoxville, university of vermont, washington university in st. louis, worcester polytechnic institute, yale university, related resources.

• PhD in Data Analytics Programs
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Discover novel solutions to data research problems

There’s no choice but to lead when you’re breaking new ground. Guide rapid development in an emerging field when you earn our Ph.D. in Data Science.

• Degrees & Courses

Data Science Ph.D.

A dynamic data science environment.

Graduates of our program—the first of its kind in both Indiana and the Big Ten—develop the skills to make pioneering research contributions to data science theory and practice in academic and the industrial sectors.

Our students acquire the skills to develop inventive and creative solutions to data research problems—solutions that demonstrate a high degree of intellectual merit and the potential for broader impact. The Ph.D. curriculum also prepares students to make research contributions that advance the theory and practice of data science.

A leader in data science research

The Data Science Ph.D. Program at IU Indianapolis provides a world-class education and research opportunities. Ph.D. students in the program learn fundamental Data Science methods while pursuing independent, original research in a broad variety of topics, including:

• Novel techniques for Natural Language Processing and Text Analytics.
• Applications of AI to social welfare, digital governance, cultural heritage, biomedical sciences, and environmental sustainability.
• Intelligent conversational agents and models of Human-AI collaboration.
• Data Visualization and Human-Data Interaction.

Meet our faculty

The program is in the midst of a major expansion, with over 50 graduate students joining the program in the past year alone. Multiple faculty in our department have secured high-profile research grants, including three    active   CAREER awards, the National Science Foundation’s most prestigious award for early-career faculty. The IU Indianapolis campus hosts the newly created Institute of Integrative Artificial Intelligence, providing an interdisciplinary nexus between Data Science, AI, and various science and engineering fields.

Sunandan Chakraborty

Assistant Professor, Data Science

Sarath Chandra Janga

Associate Professor, Bioinformatics, Data Science

Leon Johnson

Lecturer, Data Science

Kyle M. L. Jones

Associate Professor, Library and Information Science, Data Science

Bohdan Khomtchouk

Assistant Professor, Bioinformatics, Data Science

Angela Murillo

Assistant Professor, Library and Information Science, Data Science

Saptarshi Purkayastha

Associate Professor, Data Science, Health Informatics

Khairi Reda

Associate Professor, Data Science, Human-Computer Interaction

Elie Salomon

Lecturer, Data Science; Library and Information Science

Ayoung Yoon

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• Degrees in Data Science
• Ph.D. Studies in Data Science

Ph.D. with specialization in Data Science

The Ph.D. in Data Science Studies at William & Mary is offered as a specialization within Applied Science, with the core mission of training students in the use of exceptionally large, heterogeneous datasets to drive decisionmaking across a wide range of fields (from Physics to the social sciences).  Graduate students complete a core sequence of coursework as a cohort, and then work closely with an advisory committee to complete the degree program.  Competitive stipends and tuition are provided to selected students (stipends for AY23-24 are about $30,000).

To receive a Doctor of Philosophy in Applied Science with a Specialization in Data Science, the candidate must:

• Complete a sequence of coursework, normally lasting two years.
• Pass a comprehensive qualifying examination designed to demonstrate competence.
• Produce and defend a dissertation prospectus which details anticipated research after coursework is completed, including preliminary quantitative works.
• Carry out a substantive original research project, and produce a dissertation describing this research which is approved by the student’s advisory committee and successfully defended in a public oral examination.

What to Expect & What We Expect

Be prepared for a rigorous program that emphasizes the analysis of large datasets, frequently in applied domains using machine learning techniques. You will take courses in both the underlying mathematical foundations and computational techniques used to define, implement, and validate models across a range of disciplines. 

Generally, we expect students applying to this program will have a background in computer programming, probability and statistics.  Most successful candidates will also have some experience working with large datasets in applied contexts.  Python is the most commonly used language, though some courses and laboratories use alternatives such as R, Scala, or compiled languages.

Most students will start their program during the Fall semester (while spring admissions are possible, they will only occur under exceptional circumstances).  In many cases, a Ph.D. student might expect a schedule similar to:

• Year 1:  Coursework (Mathematical and Computational Methods, Applied Machine Learning, Bayesian & Frequentist Statistics, Deep Learning, Network Analysis), first research lab experiences, TAing a course, Doctoral Research Seminar.
• Year 2:  Coursework (Mathematical and Computational Methods II, Data Engineering, Natural Language Processing, Probabilistic Programming, Reinforcement Learning, Directed Research), qualifiers, dissertation prospectus defense, TAing a course.
• Year 3+: Dissertation, full time research in a research lab, annual evaluation of progress to dissertation by the graduate committee.

See the Graduate Catalog for details.

How to Apply

Data Science Ph.D. students are admitted to the Graduate program in Applied Science , in which they will earn a specialization of Data Science.  Applications can be submitted online at the link below, or by clicking  here . Note that we do not today require GRE scores, but they can be optionally submitted for consideration.

Applications are accepted on a rolling basis, but we aim to make our first round of decisions during the spring semester each year.  To be as competitive as you can be, we recommend your application be submitted by February 15th. 

The application process includes:

• Registering for an Account, and clicking "Start New Application"
• Choosing to apply to the "Graduate Arts and Sciences" school, and then the "Applied Science" program.  
• Providing various background and demographic information.
• Providing a 3500 character (or less) synopsis of your past project experience, including (if any) publications.
• Providing a 3500 character (or less) synopsis of your background, including extracurricular activities and general experience.
• Writing a personal essay describing your career plans and rationale for the pursuit of graduate study in Data Science - we recommend you identify members of our faculty that may match your interests in this essay.
• For your "Applied Science Research Interest", you will choose "Data Science".
• For the advisor you are interested in working with, you will choose the graduate director, "Dan Runfola"; graduate advisor assignment will occur after admission.  You will leave your second choice blank.

If you have any questions about the admissions process, you can contact Dr. Dan Runfola, the Graduate Director of Applied Science ( [email protected] ).

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Language Technologies Institute

School of computer science.

Master of Computational Data Science

The Master of Computational Data Science (MCDS) program focuses on engineering and deploying large-scale information systems, and includes concentrations in Systems, Analytics, and Human-Centered Data Science.

Requirements

The MCDS program offers three majors: Systems, Analytics, and Human-Centered Data Science. All three require the same total number of course credits, split among required core courses, electives, data science seminar and capstone courses specifically defined for each major. The degree can also be earned in two different ways, depending on the length of time you spend working on it. Regardless of the timing option, all MCDS students must complete a minimum of 144 units to graduate.

Here are the options:

• Standard Timing — a 16-month degree consisting of study for fall and spring semesters, a summer internship, and fall semester of study. Each semester comprises a minimum of 48 units. This timing is typical for most students. Students graduate in December.
• Extended Timing — a 20-month degree consisting of study for fall and spring semesters, a summer internship, and a second year of fall and spring study. Each semester comprises a minimum of 36 units. Students graduate in May.

Core Curriculum

All MCDS students must complete 144 units of graduate study which satisfy the following curriculum:

• Five (5) MCDS Core Courses (63 units)
• Three courses (3) from one area of concentration curriculum (36 units)
• Three (3) MCDS Capstone courses (11-635, 11-634 and 11-632) (36 units)
• One (1) Electives: any graduate level course 600 and above in the School of Computer Science (12 units)

Area of Concentration

• During the first two semesters in the program, all students take a set of five (5) required core courses: 11-637 Fundamentals of Computational Data Science, 15-619 Cloud Computing, 10-601 Machine Learning, 05-839 Interactive Data Science, and 11-631 Data Science Seminar.
• By the end of the first semester, all students must select at least one area of concentration — Systems, Analytics, or Human-Centered Data Science — which governs the courses taken after the first semester.
• To maximize your chances of success in the program, you should consider which concentration area(s) you are best prepared for, based on your educational background, work experience, and  areas of interest as described in your Statement of Purpose.
• You are strongly encouraged to review the detailed curriculum requirements for each concentration area, in order to determine the best fit given your preparation and background.

For a complete overview of the MCDS requirements read the  MCDS Handbook .

To earn an MCDS degree, students must pass courses in the core curriculum, the MCDS seminar, a concentration area, and electives. Students must also complete a capstone project in which they work on a research project at CMU or on an industry-sponsored project.

In total, students must complete 144 eligible units of study, including eight 12-unit courses, two 12-unit seminar courses, and one 24-unit capstone course. Students must choose at minimum five core courses. The remainder of the 12-unit courses with course numbers 600 or greater can be electives chosen from the SCS course catalog. Any additional non-prerequisite units taken beyond the 144 units are also considered electives.

Students who plan to select the Systems concentration may wish to enroll in 15-513 “Introduction to Computing Systems” during the summer session preceding their enrollment in the program; this course is a prerequisite for many advanced Systems courses, so it should be completed during Summer if you wish to enroll in advanced Systems courses in the Fall.

Click here   to see the MCDS Course Map.

Some example courses of study are included below.

Example 1: Analytics Major, 16 Months

Example 2: Systems Major, 16 Months

Example 3: Human-Centered Data Science Major, 16 Months

Carnegie Mellon's School of Computer Science has a centralized  online application process . Applications and all supporting documentation for fall admission to any of the LTI's graduate programs must be received by the application deadline. Incomplete applications will not be considered.  The application period for Fall 2024 is now closed. Information about the Fall 2025 admissions cycle will be available in summer 2024.

Application Deadlines

Fee Waivers

Fee waivers may be available in cases of financial hardship, or for participants in select "pipeline" programs. For more information, please refer to the  School of Computer Science Fee Waiver page .

The School of Computer Science requires the following for all applications:

• A GPA of 3.0 or higher.
• GRE scores: These must be less than five years old. Our Institution Code is 2074; Department Code is 0402. (This requirement is waived for CMU undergrads.)
• The GRE At Home test is accepted but we prefer you take the GRE at a test center if possible.
• Unofficial transcripts from each university you have attended, regardless of whether you received your degree there.
• Current resume.
• Statement of Purpose.
• Three letters of recommendation
• A short (1-3 minutes) video of yourself. Tell us about you and why you are interested in the MCDS program. This is not a required part of the application process, but it is STRONGLY suggested.  
• Proof of English Language Proficiency

Proof of English Language Proficiency: If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo. We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored.

We do not issue waivers for non-native speakers of English. In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university. We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States. No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.

Applicants applying to MCDS are required to submit scores from an English proficiency exam taken within the last two years. Scores taken before Sept. 1, 2021, will not be accepted regardless of whether you have previously studied in the U.S. For more information about their English proficiency score policies, visit the  MCDS  admission website.  Successful applicants will have a minimum TOEFL score of 100, IELTS score of 7.5, or DuoLingo score of 120. Our Institution Code is 4256; the Department Code is 78. Additional details about English proficiency requirements are provided on the  FAQ  page. 

Applications which do not meet  all  of these requirements by the application deadline (see above) will not be reviewed.

For more details on these requirements, please see the  SCS Master's Admissions page.

In addition to the SCS guidelines, the LTI requires:

• Any outside funding you are receiving must be accompanied by an official award letter.

No incomplete applications will be eligible for consideration.

For specific application/admissions questions, please contact  Jennifer Lucas  or Caitlin Korpus .

Program Contact

For more information about the MCDS program, contact Jennifer Lucas or Caitlin Korpus

Jennifer Lucas

Caitlin korpus, online graduate certificate program, program handbook.

Machine Learning & Data Science Foundations

Online Graduate Certificate

Be a Game Changer

Harness the power of big data with skills in machine learning and data science, your pathway to the ai workforce.

Organizations know how important data is, but they don’t always know what to do with the volume of data they have collected. That’s why Carnegie Mellon University designed the online Graduate Certificate in Machine Learning & Data Science Foundations; to teach technically-savvy professionals how to leverage AI and machine learning technology for harnessing the power of large scale data systems.   

Computer-Science Based Data Analytics

When you enroll in this program, you will learn foundational skills in computer programming, machine learning, and data science that will allow you to leverage data science in various industries including business, education, environment, defense, policy and health care. This unique combination of expertise will give you the ability to turn raw data into usable information that you can apply within your organization.  

Throughout the coursework, you will:

• Practice mathematical and computational concepts used in machine learning, including probability, linear algebra, multivariate differential calculus, algorithm analysis, and dynamic programming.
• Learn how to approach and solve large-scale data science problems.
• Acquire foundational skills in solution design, analytic algorithms, interactive analysis, and visualization techniques for data analysis.

An online Graduate Certificate in Machine Learning & Data Science from Carnegie Mellon will expand your possibilities and prepare you for the staggering amount of data generated by today’s rapidly changing world. 

A Powerful Certificate. Conveniently Offered. 

The online Graduate Certificate in Machine Learning & Data Science Foundations is offered 100% online to help computer science professionals conveniently fit the program into their busy day-to-day lives. In addition to a flexible, convenient format, you will experience the same rigorous coursework for which Carnegie Mellon University’s graduate programs are known. 

For Today’s Problem Solvers

This leading certificate program is best suited for:

• Industry Professionals looking to deliver value to companies by acquiring in-demand data science, AI, and machine learning skills. After completing the program, participants will acquire the technical know-how to build machine learning models as well as the ability to analyze trends.
• Recent computer science degree graduates seeking to expand their skill set and become even more marketable in a growing field. Over the past few years, data sets have grown tremendously. Today’s top companies need data science professionals who can leverage machine learning technology.   

At a Glance

Start Date May 2024

Application Deadlines Rolling Admissions

We are still accepting applications for a limited number of remaining spots to start in Summer 2024. Apply today to secure your space in the program.

Program Length 12 months

Program Format 100% online

Live-Online Schedule 1x per week for 90 minutes in the evening

Taught By School of Computer Science

Request Info

Questions? There are two ways to contact us. Call 412-501-2686 or send an email to  [email protected]  with your inquiries .

Program Name Change

To better reflect the emphasis on machine learning in the curriculum, the name of this certificate has been updated from Computational Data Science Foundations to Machine Learning & Data Science Foundations.

Although the name has changed, the course content, faculty, online experience, admissions requirements, and everything else has remained the same. Questions about the name change? Please contact us.

Looking for information about CMU's on-campus Master of Computational Data Science degree? Visit the program's website to learn more.  Admissions consultations with our team will only cover the online certificate program.

A National Leader in Computer Science

Carnegie Mellon University is world renowned for its technology and computer science programs. Our courses are taught by leading researchers in the fields of Machine Learning, Language Technologies, and Human-Computer Interaction. 

Number One  in the nation for our artificial intelligence programs.

Number One  in the nation  for our programming language courses.

Number Four  in the nation for the caliber of our computer science programs.

U.S. News Releases 2024 Best Graduate Programs Rankings

Find the top-ranked graduate schools in business, education, law, nursing and other fields.

U.S. News Ranks Best Graduate Schools

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To help prospective graduate students find a school that fits their needs, U.S. News released the 2024 rankings for multiple graduate fields.

Depending on the job or field, earning a graduate degree may lead to higher earnings, career advancement and specialized skill development.

But with several types of degrees and hundreds of graduate schools, it can be difficult to narrow down the options. To help prospective graduate students find a school that fits their needs, U.S. News released its 2024 Best Graduate Schools rankings today. They evaluate business, education, fine arts, health, law, library studies, nursing, public affairs, science, and social sciences and humanities graduate programs. Medical school and engineering rankings are not being released at this time.

A notable methodology change includes a new salary indicator based on profession in the business rankings.

Additionally, for the first time in four years, there are new rankings for a blend of doctoral and master's programs in audiology, occupational therapy, physical therapy, pharmacy, nurse midwifery and speech-language pathology. Graduate programs in nurse anesthesia and social work are also ranked for the first time since 2016 and 2022, respectively. Those and other specialty rankings are based on reputation ratings from scholars at other surveyed schools.

Read each program's specific methodology for the most detailed explanations of all the changes. The rankings are one source of information among many that prospective college students can use to inform their college decision. Below is a summary of the top-ranked schools in four major graduate program areas:

Best Law Schools

Best business schools, best nursing schools, best education schools.

Among the top 10 law schools . Yale Law School in Connecticut and California-based  Stanford Law School shared the top spot again. The  University of Chicago Law School in Illinois maintained its No. 3 rank, followed by a four-way tie at No. 4: Duke University School of Law in North Carolina, Harvard Law School in Massachusetts, the University of Pennsylvania Carey Law School and the University of Virginia School of Law .

Columbia Law School in New York ranked No. 8 again, while there was a three-way tie for No. 9: New York University School of Law , Northwestern University's Pritzker School of Law in Illinois and the University of Michigan—Ann Arbor Law School .

Looking beyond the top 10, multiple law schools moved up in the rankings. William & Mary Law School in Virginia, for instance, jumped nine spots from a tie at No. 45 to a five-way tie at No. 36.

U.S. News also ranked 13 law specialties: business/corporate, clinical training, constitutional, contracts/commercial, criminal, dispute resolution, environmental, health care, intellectual property, international, legal writing, tax and trial advocacy. (You can filter by specialty on the  main ranking page .)

Meanwhile, in the  part-time law school rankings – which consists of law schools with at least 20 part-time students enrolled in fall 2022 and fall 2023 – the top three stayed the same. The  Georgetown University Law Center in Washington, D.C., is once again at the top while D.C.-based  George Washington University Law School , now No. 3, switched places with the  Fordham University School of Law in New York City, which claimed second place.

Previously ranked at No. 3 and No. 6 respectively, the University of Pennsylvania's Wharton School and Stanford Graduate School of Business took the top spot in this year's full-time MBA program rankings . Northwestern's Kellogg School of Management and the University of Chicago's Booth School of Business moved down from their previous places in the top two to tie at No. 3.

While the top 10 mostly consists of the same schools as last year, both the Haas School of Business at the University of California, Berkeley and the University of Virginia's Darden School of Business joined those ranks this year. UC Berkeley rose from a three-way tie at No. 11 to a three-way tie at No. 7, while UVA moved up four spots from No. 14 to a tie at No. 10.

Farther down the full-time MBA rankings, there were some big changes. For example, Pitt's Joseph M. Katz Graduate School of Business soared 39 spots from a tie at No. 86 to a tie at No. 47.

Meanwhile, the very top of the part-time MBA rankings looks similar to last year, with the same schools in the top 5: UChicago, UC Berkeley, Northwestern, NYU's Leonard N. Stern School of Business and the Anderson School of Management at the University of California—Los Angeles. But UChicago took the No. 1 spot from UC Berkeley this year.

Moving up from No. 2, Johns Hopkins University School of Nursing in Maryland tied with Emory University's Nell Hodgson Woodruff School of Nursing in Georgia to claim the top spot in this year's nursing master's program rankings. Duke University School of Nursing in North Carolina climbed up by one to claim the third spot.

Johns Hopkins ranked No. 1, as it did last year, in the Doctor of Nursing Practice program rankings. George Mason University School of Nursing in Virginia – which reported more graduates and resources per faculty – soared from a four-way tie at No. 39 to take the No. 2 spot. Duke tied with the University of Washington School of Nursing to round out the top three.

Duke also ranked No. 1 in all of the ranked nursing master's nursing practice specialties, including administration, family, both acute and primary care adult gerontology, and mental health.

Once again, Teachers College, Columbia University in New York was No. 1 in the graduate education schools rankings. This year, however, it tied with the University of Wisconsin—Madison School of Education , which climbed two spots.

The University of Michigan—Ann Arbor's School of Education dropped from the top position to tie with the UCLA School of Education and Information Studies at No. 3. UCLA was previously tied at No. 7.

U.S. News also ranks nine education specialties, with the College of Education at Michigan State University claiming the top spot in the following categories: curriculum and instruction, educational administration, elementary teacher education, higher education administration and secondary teacher education.

Searching for a grad school of education? Access our  complete rankings  of Best Graduate Schools.

Grad Degree Jobs With $100K+ Salaries

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U.S. News & World Report ranks UNI among 2024 Best Graduate Schools

CEDAR FALLS, Iowa –  U.S. News & World Report has once again ranked the University of Northern Iowa among its list of 2024 Best Graduate Schools. Designed for prospective students looking to further their education beyond an undergraduate degree, the  Best Graduate Schools rankings evaluate programs in a variety of disciplines, including business, education, engineering, law, medicine and nursing. 

UNI’s honored programs for graduate education are a combination of face-to-face, entirely online and hybrid degrees offered in partnership with all UNI colleges,  Online and Distance Education , and the  College of Graduate, Research and Online Education where more than 80 degree, certificate and endorsement programs are housed. 

“It’s incredibly gratifying to have UNI recognized as a national leader in graduate education,” said Steph Huffman, associate vice president for strategic initiatives and dean of the College of Graduate, Research and Online Education. “UNI has a strong, long-standing history of developing educators, innovators, strategic leaders and other professionals who serve our schools, businesses and organizations, and communities. This ranking reflects our commitment to providing high-quality, impactful graduate educational opportunities.”

By collecting graduate school data annually, U.S. News is able to present the most current figures on enrollment, job placement, faculty and other critical quality indicators that help prospective students make informed decisions.

UNI’s  College of Education offers 10 master's programs and a doctoral program which are represented within the graduate rankings of Best Education Schools. These range from advanced degrees like elementary and early childhood education to programs that prepare school administrators, school psychologists and teacher librarians.

"The ranking continues to represent our outstanding faculty, innovative curriculum, and our ability to meet students where they are to deliver an experience that is individualized, relevant and meaningful,” said Amy Nielsen, the College of Education’s interim associate dean of graduate studies.

UNI also ranks among the top in the country for its  part-time MBA in the Wilson College of Business. 

"I am proud to see our MBA program recognized again as one of the top programs in the nation," said Leslie Wilson, dean of the Wilson College. "I know our faculty are working diligently to ensure our curriculum meets the needs of students preparing to be global business leaders. From the use of AI, addressing supply chain disruptions, and managing a remote workforce, we seek to provide our students with the necessary skills and knowledge to address the changing landscape and excel in their careers. This recognition is a testament to the hard work and dedication of our faculty, staff and students, who have helped make our program successful."

UNI’s  Speech-Language Pathology M.A. is among the top 100 speech pathology programs in the rankings. Students benefit from valuable clinical experience at the Roy Eblen Speech and Hearing Clinic located conveniently on campus.

“We take pride in offering students a comprehensive educational experience that combines hands-on clinical exposure, rigorous coursework, and valuable research opportunities with faculty members,” said Jennifer Garrett, head of the UNI Department of Communication Sciences and Disorders. “This recognition reaffirms our department's commitment to excellence in training future speech-language pathologists. With the experiences they gain here, our students graduate as highly competent professionals, ready to make meaningful contributions to improving the lives of individuals with communication disorders.”

UNI’s  Master of Public Policy Program is recognized as one of the Best Public Affairs Programs by U.S. News & World Report. With its fully online format, the program is ideal for those seeking career advancement within public service and nonprofit organizations in the field of public policy. 

“As the only nationally accredited program in the state, earning this recognition reflects our commitment to excellence and innovation in preparing future leaders for impactful public policy careers,” said Chris Larimer, MPP program coordinator and professor in the Department of Political Science. 

The University of Northern Iowa ranks highly in a number of U.S. News categories, including ranking  second best among regional public universities in the Midwest . It also ranks among the best value schools, best colleges for veterans and most innovative schools.

Media Contact: Adam Amdor

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