data science phd usa

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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Top 10 Universities in USA Offering Ph.D In Data Science

In 2011, McKinsey Global Institute called Big Data as the Next Frontier. From then to now, data science has evolved to form an integral part of digital transformation and technological innovation for the next futuristic world. This implies that not only is the demand for data scientists is growing everyday but also they receive lucrative salaries due to high market demand. Every industry from the fields of business, finance, government, healthcare, social networking, and technology, are looking for minds with a data science degree and complementary skills. Further, earning a Post-Doctoral degree in this field is beneficial because it can give students the research skills needed to make an impact in their field of choice. Depending on the university attended, a student might have to take classes such as machine learning and computational statistics, big data, probability and statistics for data science, and inference and representation along with major in data science.

Below is a list of top 10 of universities in the USA that have some excellent data science programs and courses .

Brown University – Providence, Rhode Island

Course: 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. 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. All students must also train as teaching assistants for at least one semester.

2019-2020 Tuition: $66,702 per year

Length: 6 Credit Hours

Indiana University-Purdue University Indianapolis  – Indianapolis, Indiana

Course: 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 seven 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. All students receive scholarships. 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.

2019-2020 Tuition: $368 per credit (Indiana Resident), $1,006 per credit (Non-resident)

Length: 60 credits

New York University – New York, New York

Course: 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 ten 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.

2019-2020 Tuition: $1,856 per unit

Length: 72 Credits

Yale University – New Haven, Connecticut

Course: 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 need to complete 12 courses in the first year on 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.

2019-2020 Tuition: $43,300 per year

The University Of Maryland, College Park, Maryland

Course: PhD in Information Studies – Concentration in Big Data/Data Science

PhD students can take part in research in a variety of areas, including big data, data science, and informatics. The program is designed for students who want a research-oriented career, and faculty members mentor students in a variety of disciplines. The program does not list any specific prerequisites for the doctoral program, and the interdisciplinary program accepts students with varied academic backgrounds. Students can go for either Full-time or part-time program.

2019-2020 Tuition: $731 per credit (Maryland Resident), $1,625 per credit (Non-resident)

Length: 27 Credit Hours

Kennesaw State University – Kennesaw, Georgia

Course: 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 six electives (spread over four years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship. Before dissertation research, a 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.

2019-2020 Tuition:  $1,066 per credit

Length: 4 years

University of Massachusetts Boston – Boston, Massachusetts

Course: 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.

2019-2020 Tuition: $768 per credit (Massachusetts Resident), $1,499 per credit (Non-resident)

California Institute of Technology, Pasadena, California

Course: PhD in Computing and Mathematical Science focusing on Data Sciences

Caltech has a PhD in Computing and Mathematical Sciences that is multidisciplinary and brings together faculty and students from fields including computer science, electrical engineering, applied math, operations research, economics, and the physical sciences. In their first year, all students take courses in math and computing fundamentals, and each student must take three courses in a focus area and meet breadth requirements. All candidates must complete a dissertation.

Tuition: $54,537 per year

Length: 3 Years

University At Buffalo, Buffalo, New York

Course: PhD in 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. Nine 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.

Tuition: $5,655 per semester (New York Resident), $11,550 per semester (Non-resident)

Length: 72 Credits

Clemson University / Medical University of South Carolina (MUSC) – Joint Program– Clemson, South Carolina & Charleston, South Carolina

Course: Doctor of Philosophy in Biomedical Data Science and Informatics – Clemson

Students can choose one of three tracks to pursue: precision medicine, population health, and clinical and translational informatics. Students can 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.

2019-2020 Tuition: $668 per credit (South Carolina Resident), $995 per credit (Non-resident)

Length: 65-68 credit hours

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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

The PhD in Data Science is designed to be completed fully in-person at UChicago’s Hyde Park campus. There are no online options at this time. Newly admitted students are guaranteed full-funding for up to 5 years and provided with an annual stipend, contingent on satisfactory progress towards the degree.

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 four graduate electives that can come from proposed courses in data science as well as existing courses in Computer Science or Statistics. Some students, after consulting with the graduate committee 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 (CODAS) .

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 hour-long 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 to candidates following a successful defense and the electronic submission of the final version of the dissertation to the University’s Dissertation Office.

DiscoverDataScience.org

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

Created by aasif.faizal

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

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

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

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

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

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

So why pursue a data science PhD?

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

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

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

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

Topics include:

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

Preparing for a Doctorate Program

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

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

Data Science PhD Programs, Historically

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

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

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

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

Can You Get a PhD in Data Science Online?

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

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

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

Overview of PhD Coursework

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

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

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

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

What’s the core curriculum like?

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

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

Dissertation

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

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

Join professional associations

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

Leverage your social network

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

Kaggle competitions

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

Internships

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

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

Get certified

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

Conferences

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

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

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

What most schools will require when applying:

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

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

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

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

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

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

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

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

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

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

Complete List of Data Science PhD Programs

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

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

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

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

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

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

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

View Course Offerings

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

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

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

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

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

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

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

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

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

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

Coding well in C++ is preferred.

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

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

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

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

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

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

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

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

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

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

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

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

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

View Course Offerings – Clemson

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

View Course Offering

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

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

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

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

Applicants must have a master’s already.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Students in this program work in research groups across campus.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

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

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

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

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

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

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

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

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

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

• 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) 

Information Request Form

Ph.d. specialization committee.

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

Richard A. Davis

• Faculty of Arts and Sciences Howard Levene Professor of Statistics

Vineet Goyal

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

Garud N. Iyengar

• Data Science Institute Avanessians Director of the Data Science Institute
• The Fu Foundation School of Engineering and Applied Science Professor of Industrial Engineering and Operations Research

Gail Kaiser

Rocco a. servedio, clifford stein.

• 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 Research

Department of Data Science

• Graduate Programs

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

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

Data Science, Analytics and Engineering, PhD

• Program description
• At a glance
• Degree requirements
• Admission requirements
• Tuition information
• Application deadlines
• Program learning outcomes
• Career opportunities
• Contact information

Analytics, Big Data, Data Engineering, Data Science, approved for STEM-OPT extension, computing, statistics

Learn to meet the need for data-driven discovery of new knowledge and decision-making, which enhances enterprise performance as well as scientific investigation.

The PhD program in data science, analytics and engineering engages students in fundamental and applied research.

The program's educational objective is to develop each student's ability to perform original research in the development and execution of data-driven methods for solving major societal problems. This includes the ability to identify research needs, adapt existing methods and create new methods as needed. This is accomplished through a rigorous education with research and educational experiences.

Students complete a foundational core covering database management, information assurance, statistical learning and statistical theory before choosing to focus on data analytics or data engineering. The program culminates in the production of a dissertation.

This program may be eligible for an Optional Practical Training extension for up to 24 months. This OPT work authorization period may help international students gain skills and experience in the U.S. Those interested in an OPT extension should review ASU degrees that qualify for the STEM-OPT extension at ASU's International Students and Scholars Center website.

The OPT extension only applies to students on an F-1 visa and does not apply to students completing a degree through ASU Online.

• College/school: Ira A. Fulton Schools of Engineering
• Location: Tempe
• STEM-OPT extension eligible: Yes

84 credit hours, a written comprehensive exam, an oral comprehensive exam, a prospectus and a dissertation

Required Core (12 credit hours) CSE 511 Data Processing at Scale (3) CSE 543 Information Assurance and Security (3) CSE 572 Data Mining (3) or IEE 520 Statistical Learning for Data Mining (3) or EEE 549 Statistical Machine Learning: From Theory to Practice (3) IEE 670 Mathematical Statistics (3) or STP 502 Theory of Statistics II: Inference (3) or EEE 554 Probability and Random Processes (3)

Electives and Additional Research (39 credit hours)

Research (12 credit hours) DSE 792 Research (12)

Other Requirements (9 credit hours) data engineering coursework or data analytics coursework

Culminating Experience (12 credit hours) DSE 799 Dissertation (12)

Additional Curriculum Information All students must take qualifying exams covering the required core courses within one year of matriculation into the program.

The dissertation prospectus should be submitted and its oral defense completed no later than one year following completion of the 60th credit hour and also no later than the fourth year in the program.

Students must select coursework from either the data engineering or the data analytics requirements. Students should see the academic unit for the approved course list.

Students cannot take a data engineering or data analytics course and have it meet an elective requirement at the same time. Students need to take a different elective course to reach the number of credit hours required for the program. Other coursework may be used with the approval of the academic unit to fulfill these requirements.

Twelve credit hours of DSE 792 Research are required, and up to 24 credit hours are allowed on the plan of study. Students with research hours in excess of 12 will add these credit hours to their electives and additional research.

Electives include:

• additional DSE 792 Research credit hours (up to 12 credit hours allowed beyond the required 12)
• approved elective courses, of which up to three credit hours of DSE 790: Reading and Conference are permitted, with approval.

When approved by the student's supervisory committee and the Graduate College, this program allows 30 credit hours from a previously awarded master's degree to be used for this degree. If students do not have a previously awarded master's degree, the 30 hours of coursework are to be made up of electives to reach the required 84 credit hours.

Applicants must fulfill the requirements of both the Graduate College and the Ira A. Fulton Schools of Engineering.

Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree in engineering, computer science, mathematics, statistics or a related field from a regionally accredited institution.

Applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program or a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in an applicable master's degree program.

Applicants are required to submit:

• graduate admission application and application fee
• official transcripts
• two letters of recommendation
• letter of intent or written statement
• proof of English proficiency

Additional Application Information An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency.

ASU does not accept the GRE® General Test at home edition.

If the student is assigned any deficiency coursework upon admission, those classes must be completed with a grade of "B" (scale is 4.00 = "A") or higher within two semesters of admission to the program. Deficiency courses do not apply to the total credit hours required to complete the degree program.

Deficiency courses are: CSE 205 Object-oriented Programming and Data Structures IEE 380 Probability and Statistics for Engineering Problem Solving MAT 242 Elementary Linear Algebra or MAT 342 Linear Algebra or MAT 343 Applied Linear Algebra MAT 267 Calculus for Engineers III

Program learning outcomes identify what a student will learn or be able to do upon completion of their program. This program has the following program outcomes:

• Apply the tools and methods from industrial statistics, operations research, machine learning, computer science and computer engineering on solving data analytic problems.
• Manage large, heterogeneous data sets for knowledge discovery.
• Conduct research resulting in an original contribution to knowledge in data sciences.

Graduates demonstrate proficiency with existing methodology and significant accomplishment at advancing the state of the art in their chosen area, enabling them to pursue careers in the following fields:

• advanced research

Computer Science and Engineering Program | CTRPT 105 [email protected] 480-965-3199

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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 solutions 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 backgrounds 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 term courses (64 units) are required for post-BA/BS students and 12 term courses (48 units) 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 units) as long as these units 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 units) 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-term training course (4 units) covering various aspects of the responsible and ethical conduct of computational and data-driven research, a two-term doctoral seminar (4 units) 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 units) 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 units. 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 term (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 term 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 term (fourth year) of study.

Candidates must undergo a final oral examination no later than the end of the 10th term (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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• Faculty of Computing & Data Sciences
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• BS in Data Science
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• MS in Data Science
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• PhD in Computing & Data Sciences
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• MS in Bioinformatics
• PhD in Bioinformatics

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Ph.D. in Data Science

The ph.d. in data science at smu is distinctive because of its highly interdisciplinary nature..

Most existing Data Science Ph.D. programs are either housed in a single department, such as Statistics, Computer Science, Operations Management or Business Analytics; or they focus on a single disciplinary area of research, such as Business or Medicine.

The program’s core curriculum consists of courses in Computer Science, Operations Management, Statistics, and Data Science, and elective courses go beyond those disciplines to include Mathematics, Finance, Marketing, Education, Psychology, Chemistry, Game Design, Economics, and more. Student and faculty interest will continue to set directions for how the program evolves in the future.

Another distinctive feature are the research rotations that students engage in after having completed 4 semesters of coursework.

The goal of this program is to recognize that data science research can inform nearly every discipline at the university and beyond; and that the future of research and work in data science will not be limited to specific and restricted areas.

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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.

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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

Associate Professor, Data Science

Sarath Chandra Janga

Associate Professor, Bioinformatics, Data Science

Assistant Professor, 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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PhD in Data Science and Analytics

Degrees & Programs

• Doctoral Degree in Data Science and Analytics
• Certificates

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

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

About the Doctoral Degree in Data Science and Analytics

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

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

Information Sessions for Fall 2025 Admission

To be announced

Data Science and Analytics PhD Curriculum

Stage One: Pre-Program Requirements

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

Stage Two: Coursework

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

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

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

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

Stage Three: Project Engagement and Research/Dissertation

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

Program Student Learning Outcomes

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

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

Admission Requirements and Application

Frequently Asked Questions (FAQ)

How long will the program take?

How much does the program cost?

Who would be successful in the program?

Where do these graduates work after graduation?

What are the publication/research requirements?

What did Science Doctoral Students Study?

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

What is the Project Engagement requirement?

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

Can I live on campus?

Are the courses online?

Do I have to have a masters degree to apply?

Where did Data Doctoral Students Study?

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

Ph.D. in Data Science and Analytics Student Cohorts

2024 - 2025

Charles Fanning

Bachelor's Degree: Mathematics, Lewis University

Master's Degree: Data Science from Lewis University

Professional Objective: to make meaningful contributions to a wide breadth of interdisciplinary fields through the medium of data science, both in academia and industry. Right now, I am interested in the applications of deep learning on medical imaging data across various modalities and in topological data analysis.

Sharanya Dv

Bachelor's Degree: Physics, Computer Science and Mathematics, St. Aloysius College, Mangalore, India

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

Work History: Data Modernization Team Intern, Google Cloud Partner, Niveus Solutions Pvt. Ltd.

Professional Objective: My goal is to contribute effectively to data-driven decision-making processes and to continuously develop my expertise in the ever-evolving field of data science.

Mohsin Md Abdul Karim

Bachelor's Degree: Mathematics, Jahangirnagar University, Dhaka, Bangladesh

Master's Degrees: Mathematics, Jahangirnagar University, Dhaka, Bangladesh;  Mathematics, Eastern Illinois University; Mathematics, University of Louisiana at Lafayette

Work History:  Export Officer, Jamuna Bank Ltd., Dhaka, Bangladesh; Business Intelligence Team, Nagad Ltd., Dhaka, Bangladesh

Professional Objective: Create a distinct value for myself in an organization so that I can be treated as an asset to them.

Faruk Muritala

Bachelor's Degree: Mathematics, Federal University of Technology, Minna, Niger State Nigeria

Master's Degrees: Mathematics, Kwara State University, Malete, Nigeria; Data Science and Analytics, Kennesaw State University

Work History: 

• Graduate Research Assistant and Teacher of Record, Kennesaw State University
• Mathematics Instructor, Al-Ihsan Group of School, Offa Kwara Nigeria
• Data Entry Officer, National Examination Council (NECo), Niger State Nigeria

Courses Taught:  Introduction to Data Science

Publications:  

• Muritala, F., Olayiwola, R.O., Oyedeji, A.A., Akande, S.O. (2021). “ Mathematical Modeling of Heat Transfer in Micro-channels ”. Journal of Science, Technology, Mathematics, and Education (JOSTMED), 17(3), September 2021.
• Muritala, F. (2022). K-Step Block Hybrid Method for Numerical Solution of Fourth Order Ordinary Differential Equations (Accessed ProQuest). Kwara State University, Malete, Nigeria.
• Muritala, F., Kolawole, M.K., Oyedeji A.A., Lawal, J.O., Alaje A.I., “ Development of an Order (K+3) Block-Hybrid Linear Multistep Method for Direct Solution of General Second Order Initial Value Problems ”, UNIOSUN Journal of Engineering and Environmental Sciences. Vol. 4 No. 2. Sept. 2022. DOI: 10.36108/ujees/2202.40.0230.
• Muritala F., Jimoh A.K., Ogunniran M.O., Oyedeji A.A., Lawal J.O., “ Coherent Hybrid Block Method for Approximating Fourth-Order Ordinary Differential Equation”, Journal of Amasya University the Institute of Sciences and Technology 2023, DOI: 10.54559/jauist.1262994.
• Lawal J., Zhiri A., Muritala F., Ibrahim R., Lukonde A., “Mathematical Modeling for Mycobacterium Tuberculosis” Journal of Balkan Science and Technology 2024. DOI: 10.55848/jbst.2024.41
• Austin. R.B., Muritala F., “A Nonparametric Process Capability Index for Multiple Stream Processes”. 2024 Joint Statistical Meeting Conference Paper, August 2024, Portland, Oregon. 

Award: J. Stephen and Jennifer Lewis Priestley Doctoral Endowed Scholarship, Kennesaw State University. August 2024.

Professional Objective:  to utilize my mathematics, data science, and educational skills to become a leading data scientist and researcher and make a positive impact. I am open to exploring opportunities in academia and industry and eager to learn, relearn, and grow in a dynamic research environment.

Joseph Richardson

Bachelor's Degree:  Actuarial Science (Statistics), University of West GA

Master's Degree: Analytics, Georgia Tech

Professional Objective: I aim to teach and conduct research at an academic institution while also consulting privately.

David Stabler

Bachelor's Degree:  Computer Science, Southern Polytechnic State University

Master's Degree:  Computer Science, Southern Polytechnic State University/Kennesaw State University

Work History: Four decades of IT, currently in the Research Division of the Federal Reserve Bank of Atlanta 

Professional Objective: prepare to be a more effective professor

Benjamin Watson

Bachelor's Degree: Mathematics, Morehouse College

Master's Degrees: Mathematics Education, Georgia State University; Data Science and Analytics, Kennesaw State University (in progress)

• Limited Term Instructor, Kennesaw State University
• Reporting Business Analyst, Northeast Georgia Health System
• Virtual Mathematics and Computer Science Instructor, Imagine Learning
• Mathematics Instructor, Dekalb County School District

Professional Objective: I am seeking to apply combinatorial data analysis to drive innovation in healthcare through patient subtyping, drug discovery, and generative AI. I am also committed to advancing data science research and contributing to academic instruction.

Qiuyuan Zhang

Bachelor's Degree:  Electronic Information Engineering, Xidian University, China

Master's Degree:  Data Science, Georgia State University

Work History:  Graduate Research Assistant, Georgia State University

Professional Objective:  to evolve into a research-oriented professional contributing significantly to academia or industry

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: 

Courses Taught: 

Publications: 

Professional Objective: 

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

Master's Degree:   Business Analytics, Syracuse University

Work History:  

• 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

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

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

Nzubechukwu Ohalete

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

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

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

Courses Taught:  Elementary Mathematics, Mathematical Methods

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

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

Ryan Parker

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

Master's Degree:   Integrative Biology, Kennesaw State University

Work History:  Instructor of Biology, Kennesaw State University

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

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

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

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

Askhat Yktybaev

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

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

Work History:

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

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

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

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

Sanad Biswas

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

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

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

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

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

Mallika Boyapati

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

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

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

Courses Taught:   DATA 4310 - Statistical Data Mining

Publications:

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

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

Nina Grundlingh

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

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

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

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

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

Namazbai Ishmakhametov

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

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

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

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

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

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

Symon Kimitei

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

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

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

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

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

Poster Presentations:

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

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

Jitendra Sai Kota

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

Master's Degree:   Computer Science, Florida State University

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

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

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

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

ResearchGate

Catrice Taylor

Bachelor's Degree:   Economics, Clemson University 

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

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

Sahar Yarmohammadtoosky

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

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

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

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

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

Martin Brown

Graduation Date: Spring 2024

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

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

Inchan Hwang

Graduation Date: Summer 2024 

Dissertation: Next-Generation Medical Imaging Dataset Management Leveraging Deep Learning Frameworks in Breast Cancer Screening

Dissertation Advisor: Dr. MinJae Woo

Current Position: Assistant Professor of Cybersecurity, Montreat College

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

Graduation Date: Summer 2024

Dissertation: Innovative Approaches for Identifying and Reducing Disparity in Machine Learning Model Performance – Bridging the Gap in Binary Classification for Health Informatics

Current Position: Data and Analytics RDP Associate, Equifax

Yihong Zhang

Bachelor’s Degree:   Psychology Mathematics Interdisciplinary, Chatham University

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

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

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

2019 - 2020

Trent Geisler

Graduation Date:   Summer 2022

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

Dissertation Advisor:   Dr. Herman Ray

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

Srivatsa Mallapragada

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

Dissertation Advisor: Dr. Ying Xie

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

Sudhashree Sayenju

Graduation Date:   Spring 2023

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

Dissertation Advisor:   Dr. Ramazan Aygun

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

Christina Stradwick

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

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

Courses Taught:  Prep for College Algebra at Marshall University

Selected Presentations:

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

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

2018 - 2019

Md Shafiul Alam

Graduation Date:   Fall 2022

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

Dissertation Advisor:   Dr. Ying Xie

Current Position:   AI Framework Engineer, Intel Corporation

Jonathan Boardman

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

Current Position:   Data Scientist, Equifax

Tejaswini Mallavarapu

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

Master’s Degree:   Computer Science, Kennesaw State University

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

Selected Publications:

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

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

Seema Sangari

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

Dissertation Advisor:   Dr. Michael Whitman

Current Position:   Principal Modeler, HSB 

Srivarna Settisara Janney

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

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

Selected Publications/Presentations:

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

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

2017 - 2018

Andrew M. Henshaw

Bachelor’s Degree: Electrical Engineering, Georgia Tech

Master’s Degree: Electrical Engineering, Georgia Tech

Master’s Degree: Business Administration, Georgia State University

• Georgia Tech Research Institute, Sr. Research Engineer, 2001-
• APower Solutions, Vice President, 1999-2001
• Georgia Tech Research Institute, Research Engineer II, 1990-1999
• Georgia Tech, School of Electrical Engineering, Research Engineer I, 1986-1990

Courses Taught: Software-Defined Radio Development with GNU Radio: Theory and Application, Georgia Tech Professional Education

Selected Publications/Presentations: Python Cookbook, Vol 1, 2002, “Sorting Objects Using SQL’s ORDER BY Syntax”

Triangulation Clustering

Lyrical: Complexity Analysis of Pop Song Lyrics

Service and Awards:  International Test and Evaluation Association (ITEA) Atlanta Chapter, President, 1995

Graduation Date: Summer 2021

Dissertation: Incentive-based Data Sharing and Exchanging Mechanism Design

Dissertation Advisor: Dr. Meng Han

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

Mohammad Masum

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

Dissertation Advisor: Dr. Hossain Shahriar

Current Position: Assistant Professor, San Jose State University

Lauren Staples

Graduation Date: Fall 2021

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

Dissertation Advisor: Dr. Joseph DeMaio

Current Position: Senior Data Scientist, Microsoft

2016 - 2017

Shashank Hebbar

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

Current Position: Data Scientist, Credigy

Jessica Rudd

Graduation Date: Summer 2020

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

Dissertation Advisor: Dr. Herman Ray

Current Position: Senior Data Engineer, Intuit Mailchimp

Graduation Date: Spring 2020

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

Dissertation Advisor: Dr. Sherry NI

Current Position: Applied Scientist II, Amazon

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

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

Dissertation: Attack and Defense in Security Analytics

Dissertation Advisor: Dr. Selena He

Current Position: NLP Data Scientist, NBME

2015 - 2016

Edwin Baidoo

Graduation Date:  Spring 2020

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

Dissertation Advisor:  Dr. Stefano Mazzotta

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

Bogdan Gadidov

Graduation Date:  Summer 2019

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

Dissertation Advisor: Dr. Mohammed Chowdhury

Current Position: Data Scientist, Variant

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

Dissertation Advisor:  Dr. Mingon Kang

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

Graduation Date:  Spring 2019

Dissertation:  Deep Embedding Kernel

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

Bob Venderheyden

Graduation Date: Fall 2019

Dissertation:  Ordinal Hyperplane Loss

Dissertation Advisor:  Dr. Ying Xie

Current Position:  Principal Data Scientist, Microsoft

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PhD Program

Requirements for doctor of philosophy (ph.d.) in data science.

The goal of the doctoral program is to create leaders in the field of Data Science who will lay the foundation and expand the boundaries of knowledge in the field. The doctoral program aims to provide a research-oriented education to students, teaching them knowledge, skills and awareness required to perform data driven research, and enabling them to, using this shared background, carry out research that expands the boundaries of knowledge in Data Science. The doctoral program spans from foundational aspects, including computational methods, machine learning, mathematical models and statistical analysis, to applications in data science.

Course Requirements

https://datascience.ucsd.edu/graduate/phd-program/phd-course-requirements/ 

Research Rotation Program

https://datascience.ucsd.edu/graduate/phd-program/research-rotation/

Preliminary Assessment Examination

The goal of the preliminary assessment examination is to assess students’ preparation for pursuing a PhD in data science, in terms of core knowledge and readiness for conducting research. The preliminary assessment is an advisory examination.

The preliminary assessment is an oral presentation that must be completed before the end of Spring quarter of the second academic year. Students must have a GPA of 3.0 or above to qualify for the assessment and have completed three of four core required courses . The student will choose a committee consisting of three members, one of which will be the HDSI academic advisor of the student. The other two committee members must be HDSI faculty members with  0% or more appointments; we encourage the student to select the second faculty member based on compatibility of research interests and topic of the presentation. The student is responsible for scheduling the meeting and making a room reservation. 

The student may choose to be evaluated based on (A) a scientific literature survey and data analysis or (B) based on a previous rotation project. The student will propose the topic of the presentation. 

• If the student chooses the survey theme, they should select a broad area that is well represented among HDSI faculty members, such as causal inference, responsible AI, optimization, etc. The student should survey at least 10 peer-reviewed conference or journal papers representative of the last (at least) 5 years of the field. The student should present a novel and rigorous original analysis using publicly available data from the surveyed literature: this analysis may aim to answer a related or new research question.
•  If the student chooses the rotation project theme, they should prepare to discuss the motivation for the project, the analysis undertaken, and the outcome of the rotation. 

For both themes, the student will describe their topic to the committee by writing a 1-2 page proposal that must be then approved by the committee. We emphasize that this is not a research proposal. The student will have 50 minutes to give an oral presentation which should include a comprehensive overview of previous work, motivation for the presented work or state-of-the-art studies, a critical assessment of previous work and of their own work, and a future outlook including logical next steps or unanswered questions. The presentation will then be followed by a Q&A session by the committee members; the entire exam is expected to finish within two hours. 

The committee will assess both the oral presentation as well as the student’s academic performance so far (especially in the required core courses). The committee will evaluate preparedness, technical skills, comprehension, critical thinking, and research readiness. Students who do not receive a satisfactory evaluation will receive a recommendation from the Graduate Program Committee regarding ways to remedy the lacking preparation or an opportunity to receive a terminal MS in Data Science degree provided the student can meet the degree requirements of the MS program . If the lack of preparation is course-based, the committee can require that additional course(s) be taken to pass the exam. If the lack of preparation is research-based, the committee can require an evaluation after another quarter of research with an HDSI faculty member; the faculty member will provide this evaluation. The preliminary assessment must be successfully completed no later than completion of two years (or sixth quarter enrollment) in the Ph.D. program. 

The oral presentation must be completed in-person. We recommend the following timeline so that students can plan their preliminary assessments:

• Middle of winter quarter of second year: Student selects committee and proposes preliminary exam topic.  
• Beginning of spring quarter of second year: Scheduling of exam is completed. 
• End of spring quarter of second year: Exam. 

Research Qualifying Examination and Advancing to Candidacy

A research qualifying examination (UQE) is conducted by the dissertation committee consisting of five or more members approved by the graduate division as per senate regulation 715(D). One senate faculty member must have a primary appointment in the department outside of HDSI. Faculty with 25% or less partial appointment in HDSI may be considered for meeting this requirement on an exceptional basis upon approval from the graduate division.

The goal of UQE is to assess the ability of the candidate to perform independent critical research as evidenced by a presentation and writing a technical report at the level of a peer-reviewed journal or conference publication. The examination is taken after the student and his or her adviser have identified a topic for the dissertation and an initial demonstration of feasible progress has been made. The candidate is expected to describe his or her accomplishments to date as well as future work. The research qualifying examination must be completed no later than fourth year or 12 quarters from the start of the degree program; the UQE is tantamount to the advancement to PhD candidacy exam.

A petition to the Graduate Committee is required for students who take UQE after the required 12 quarters deadline. Students who fail the research qualifying examination may file a petition to retake it; if the petition is approved, they will be allowed to retake it one (and only one) more time. Students who fail UQE may also petition to transition to a MS in Data Science track.

Dissertation Defense Examination and Thesis Requirements

Students must successfully complete a final dissertation defense oral presentation and examination to the Dissertation Committee consisting of five or more members approved by the graduate division as per senate regulation 715(D).  One senate faculty member in the Dissertation Committee must have a primary appointment in a department outside of HDSI. Partially appointed faculty in HDSI (at 25% or less) are acceptable in meeting this outside-department requirement as long as their main (lead) department is not HDSI.

A dissertation in the scope of Data Science is required of every candidate for the PhD degree. HDSI PhD program thesis requirements must meet Regulation 715(D) requirements. The final form of the dissertation document must comply with published guidelines by the Graduate Division.

The dissertation topic will be selected by the student, under the advice and guidance of Thesis Adviser and the Dissertation Committee. The dissertation must contain an original contribution of quality that would be acceptable for publication in the academic literature that either extends the theory or methodology of data science, or uses data science methods to solve a scientific problem in applied disciplines.

The entire dissertation committee will conduct a final oral examination, which will deal primarily with questions arising out of the relationship of the dissertation to the field of Data Science. The final examination will be conducted in two parts. The first part consists of a presentation by the candidate followed by a brief period of questions pertaining to the presentation; this part of the examination is open to the public. The second part of the examination will immediately follow the first part; this is a closed session between the student and the committee and will consist of a period of questioning by the committee members.

Special Requirements: Generalization, Reproducibility and Responsibility A candidate for doctoral degree in data science is expected to demonstrate evidence of generalization skills as well as evidence of reproducibility in research results. Evidence of generalization skills may be in the form of — but not limited to — generalization of results arrived at across domains, or across applications within a domain, generalization of applicability of method(s) proposed, or generalization of thesis conclusions rooted in formal or mathematical proof or quantitative reasoning supported by robust statistical measures. Reproducibility requirement may be satisfied by additional supplementary material consisting of code and data repository. The dissertation will also be reviewed for responsible use of data.

Special Requirements: Professional Training and Communications

All graduate students in the doctoral program are required to complete at least one quarter of experience in the classroom as teaching assistants regardless of their eventual career goals. Effective communications and ability to explain deep technical subjects is considered a key measure of a well-rounded doctoral education. Thus, Ph.D. students are also required to take a 1-unit DSC 295 (Academia Survival Skills) course for a Satisfactory grade.

Obtaining an MS in Data Science

PhD students may obtain an MS Degree in Data Science along the way or a terminal MS degree, provided they complete the requirements for the MS degree.

Course Exceptions: Students with MS in Data Science (or similar field)

If a student has already been granted a Master’s degree in Data Science (or a related field, as determined by the Graduate Program Committee) before entering the HDSI PhD program, the student can submit a “Requirement Substitution” petition for up to 2 courses to be substituted by DSC 299 (up to 8 units).

Further leniency may be granted in exceptional cases in which both the student and their faculty advisor must separately appeal to the Graduate Program Committee. It is up to the Graduate Program Committee to decide whether the appeal is rejected or granted in part or in its entirety.

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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

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Doctoral Degree Data Sciences Ph.D.

Doctorate education focuses on enabling the student to make original contributions to their respective fields of study.

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The mission of the program is to create scientifically minded and technically proficient professionals with a comprehensive background in the methodological diversity of the data sciences and the intellectual depth to offer influential perspectives to analytical teams across disciplines.

There are two phases of the doctoral program at HU: a learning phase that includes coursework, seminars, research, and fieldwork that contributes to the student’s knowledge in the program of study; and a research phase that focuses on the student’s original research culminating in their final examination. Upon a student’s successful completion of all required course work, defense of the dissertation, and completion of all milestones, the student is awarded the doctoral degree in the program of study.

Program Goals

The Data Sciences Program will produce Ph.D. graduates who will have:

• Applied diverse data science methodologies using a scientific process individually or in teams to provide impactful insights from large sets of data;
• Used effective communications to explain insights from analytical processes on data to diverse audiences; and,
• Grown professionally through self-study, continuing education, and professional development.

Doctorate Program Admissions Process

Doctorate program applicants are encouraged to apply at least six months prior to the start of any semester. This application process allows ample time for an admissions decision and development of an academic schedule. The Admission Committee reviews all documents and will request an interview with the applicant prior to making an admission decision for a limited number of applicants to become resident or non-resident candidates for the degree.

Learn More: Graduate Admissions

“This hot new field promises to revolutionize industries from businesses to government, healthcare to academia.”

– The New York Times

Full Time Faculty

Kevin Huggins, Ph.D., CISSP

Professor of Computer and Data Science

• 717-901-5100, ext. 1619
• [email protected]

Program Courses

The following courses comprise the 36 semester hours required for the Ph.D. in Data Sciences. Complete 18 semester hours in upper level courses, 6 semester hours of Doctoral Research Seminars and defend dissertation proposal, and complete 12 hours to complete the dissertation process and defend the dissertation.

ANLY 705 – Modeling for Data Science (3 credits)

This course provides a more in depth presentation of the theory behind linear statistical models, segmentation models, and production level modeling. Further emphasis is placed on practical application of these methods when applied to massive data sources and appropriate and accurate reporting of results.

ANLY 710 – Appld Expmntal & Quasi-Expmnt Des (3 credits)

Methods and approaches used for the construction and analysis of experiments and quasi-experiments are presented, including the concepts of the design and analysis of completely randomized, randomized complete block, incomplete block, Latin square, split-plot, repeated measures, factorial and fractional factorial designs will be covered along with methods for proper analysis and interpretation in quasi-experiments.

ANLY 715 – Applied Multivariate Data Analysis (3 credits)

This course provides hands-on experience in understanding when and how to utilize the primary multivariate methods Data Reduction techniques, including Principal Components Analysis and Exploratory and Confirmatory Factor Analyses, ANOVA/MANOVA/MANCOVA, Cluster Analysis, Survival Analysis and Decision Trees.

ANLY 720 – Data Science from an Ethical Perspe (3 credits)

This course introduces the power and pitfalls of handling user information in an ethical manner. The student is offered a historical and current perspective and will gain an understanding of their role in assuring the ethical use of data.

ANLY 725 – Research Seminar in Unstructured (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Unstructured Data. Topics of special interest in Unstructured Data analysis are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

ANLY 730 – Research Seminar in Forecasting (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Forecasting. Topics of special interest in Forecasting Data analysis are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

ANLY 735 – Research Seminar in Machine (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Machine Learning. In addition, topics of special interest in Machine Learning are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

ANLY 740 – Graph Theory (3 credits)

This course introduces standard graph theory, algorithms, and theoretical terminology. Including graphs, trees, paths, cycles, isomorphisms, routing problems, independence, domination, centrality, and data structures for representing large graphs and corresponding algorithms for searching and optimization.

ANLY 745 – Functional Prog Mthds for Data Sci (3 credits)

This course is designed to build on the Functional Programming Methods for Analytics course. The student works to extend programming skills to write the student’s own versions of popular statistical functions using a current programming language.

ANLY 755 – Advanced Topics in Big Data (3 credits)

Topics include the design of advanced algorithms that are scalable to Big Data, high performance computing technologies, supercomputing, grid computing, cloud computing, and Parallel and Distributed Computing, and issues in data warehousing.

ANLY 760 – Doctoral Research Seminar (3 credits)

This seminar provides support to doctoral students within their specific domains of research. Led by the faculty advisor for that domain, the course is designed to provide a forum where faculty and students can come together to discuss, support, and share the experiences of working in research.

ANLY 761 – Research Seminar in Unstructured (3 credits)

ANLY 762 – Research Seminar in Forecasting (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Forecasting. Topics of special interest in Forecasting are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

ANLY 763 – Research Seminar in Machine (3 credits)

This course follows a research seminar format. Students and faculty develop research proposals, analyses, and reporting in the domain of Machine Learning. Topics of special interest in Machine Learning are presented by faculty and students under faculty direction. Topics of special interest vary from semester to semester.

ANLY 799 – Doctorial Studies (6 credits)

Advancement to candidacy is a prerequisite of this course. This is an individual study course for doctoral students. Content to be determined by the student and the student’s Doctoral Committee. May be repeated for credit.

2024–2025 Academic Course Catalogs

Get information about core courses, electives and concentrations in our current academic course catalog.

• Undergraduate Catalog
• Graduate Catalog
• Undergraduate Catalog (PDF)
• Graduate Catalog (PDF)

Information for Students who want to come to the U.S.

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Program News

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The Ph.D. in Data Science at Harrisburg University of Science and Technology is listed among the Top 10 by Analytics…

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Department of Statistics and Data Science

Ph.d. program.

Fields of study include the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak convergence), information theory, bioinformatics and genetics, classification, data mining and machine learning, neural nets, network science, optimization, statistical computing, and graphical models and methods.

With this background, graduates of the program have found excellent positions in universities, industry, and government. See the list of alumni for examples.

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

Choosing a Field of Study

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

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

Residency Requirements

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

Your Advisor and Special Committee

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

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

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

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

Statistics PhD Travel Support

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

Completion of the PhD Degree

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

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

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Earn your MBA and SM in engineering with this transformative two-year program.

Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.

A doctoral program that produces outstanding scholars who are leading in their fields of research.

Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.

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PhD Program

Program overview.

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Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding intellectual skills who will carry forward productive research on the complex organizational, financial, and technological issues that characterize an increasingly competitive and challenging business world.

Start here.

Learn more about the program, how to apply, and find answers to common questions.

Admissions Events

Check out our event schedule, and learn when you can chat with us in person or online.

Start Your Application

Visit this section to find important admissions deadlines, along with a link to our application.

Click here for answers to many of the most frequently asked questions.

PhD studies at MIT Sloan are intense and individual in nature, demanding a great deal of time, initiative, and discipline from every candidate. But the rewards of such rigor are tremendous:  MIT Sloan PhD graduates go on to teach and conduct research at the world's most prestigious universities.

PhD Program curriculum at MIT Sloan is organized under the following three academic areas: Behavior & Policy Sciences; Economics, Finance & Accounting; and Management Science. Our nine research groups correspond with one of the academic areas, as noted below.

MIT Sloan PhD Research Groups

Behavioral & policy sciences.

Economic Sociology

Institute for Work & Employment Research

Organization Studies

Technological Innovation, Entrepreneurship & Strategic Management

Economics, Finance & Accounting

Accounting  

Management Science

Information Technology

System Dynamics  

Those interested in a PhD in Operations Research should visit the Operations Research Center .  

PhD Program Structure

Additional information including coursework and thesis requirements.

MIT Sloan Predoctoral Opportunities

MIT Sloan is eager to provide a diverse group of talented students with early-career exposure to research techniques as well as support in considering research career paths.

Rising Scholars Conference

The fourth annual Rising Scholars Conference on October 25 and 26 gathers diverse PhD students from across the country to present their research.

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The goal of the MIT Sloan PhD Program's admissions process is to select a small number of people who are most likely to successfully complete our rigorous and demanding program and then thrive in academic research careers. The admission selection process is highly competitive; we aim for a class size of nineteen students, admitted from a pool of hundreds of applicants.

What We Seek

• Outstanding intellectual ability
• Excellent academic records
• Previous work in disciplines related to the intended area of concentration
• Strong commitment to a career in research

MIT Sloan PhD Program Admissions Requirements Common Questions

Dates and Deadlines

Admissions for 2024 is closed. The next opportunity to apply will be for 2025 admission. The 2025 application will open in September 2024. 

More information on program requirements and application components

Students in good academic standing in our program receive a funding package that includes tuition, medical insurance, and a fellowship stipend and/or TA/RA salary. We also provide a new laptop computer and a conference travel/research budget.

Funding Information

Throughout the year, we organize events that give you a chance to learn more about the program and determine if a PhD in Management is right for you.

PhD Program Events

Discover your doctoral path.

An in-person event for prospective students with Boston-area management programs

September 12 PhD Program Overview

During this webinar, you will hear from the PhD Program team and have the chance to ask questions about the application and admissions process.

DocNet Recruiting Forum at University of Minnesota

We will be joining the DocNet consortium for an overview of business academia and a recruitment fair at University of Minnesota, Carlson School of Management.

September 25 PhD Program Overview

Complete PhD Admissions Event Calendar

Unlike formulaic approaches to training scholars, the PhD Program at MIT Sloan allows students to choose their own adventure and develop a unique scholarly identity. This can be daunting, but students are given a wide range of support along the way - most notably having access to world class faculty and coursework both at MIT and in the broader academic community around Boston.

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Profiles of our current students

MIT Sloan produces top-notch PhDs in management. Immersed in MIT Sloan's distinctive culture, upcoming graduates are poised to innovate in management research and education.

Academic Job Market

Doctoral candidates on the current academic market

Academic Placements

Graduates of the MIT Sloan PhD Program are researching and teaching at top schools around the world.

view recent placements 

MIT Sloan Experience

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The PhD Program is integral to the research of MIT Sloan's world-class faculty. With a reputation as risk-takers who are unafraid to embrace the unconventional, they are engaged in exciting disciplinary and interdisciplinary research that often includes PhD students as key team members.

Research centers across MIT Sloan and MIT provide a rich setting for collaboration and exploration. In addition to exposure to the faculty, PhD students also learn from one another in a creative, supportive research community.

Throughout MIT Sloan's history, our professors have devised theories and fields of study that have had a profound impact on management theory and practice.

From Douglas McGregor's Theory X/Theory Y distinction to Nobel-recognized breakthroughs in finance by Franco Modigliani and in option pricing by Robert Merton and Myron Scholes, MIT Sloan's faculty have been unmatched innovators.

This legacy of innovative thinking and dedication to research impacts every faculty member and filters down to the students who work beside them.

Faculty Links

• Accounting Faculty
• Economic Sociology Faculty
• Finance Faculty
• Information Technology Faculty
• Institute for Work and Employment Research (IWER) Faculty
• Marketing Faculty
• Organization Studies Faculty
• System Dynamics Faculty
• Technological Innovation, Entrepreneurship, and Strategic Management (TIES) Faculty

Student Research

“MIT Sloan PhD training is a transformative experience. The heart of the process is the student’s transition from being a consumer of knowledge to being a producer of knowledge. This involves learning to ask precise, tractable questions and addressing them with creativity and rigor. Hard work is required, but the reward is the incomparable exhilaration one feels from having solved a puzzle that had bedeviled the sharpest minds in the world!” -Ezra Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship

Sample Dissertation Abstracts - These sample Dissertation Abstracts provide examples of the work that our students have chosen to study while in the MIT Sloan PhD Program.

We believe that our doctoral program is the heart of MIT Sloan's research community and that it develops some of the best management researchers in the world. At our annual Doctoral Research Forum, we celebrate the great research that our doctoral students do, and the research community that supports that development process.

The videos of their presentations below showcase the work of our students and will give you insight into the topics they choose to research in the program.

Attention To Retention: The Informativeness of Insiders’ Decision to Retain Shares

2024 PhD Doctoral Research Forum Winner - Gabriel Voelcker

Watch more MIT Sloan PhD Program  Doctoral Forum Videos

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MS in Data Science

The master of science (MS) in data science program at the University of Rochester provides students with a strong background in the fundamentals and applications of data science, and is accredited by New York State.

The 30 credit program is designed for students with a background in any field of science, engineering, mathematics, or business, and can be completed in two to three semesters of full-time study.

Program Outcomes

Our alumni have found rewarding careers in small and large tech, healthcare, academia, research, finance, and manufacturing and services. See our program outcomes page to learn more about the career opportunities for data science graduates.

Apply Today

Program Components

The optional summer bridging course is designed for students who matriculate without a strong computer science background.

• CSC 162: Data Structures and Algorithms in Python
• DSCC 462: Computational Introduction to Statistics (fall)
• DSCC 465: Introduction to Statistical Machine Learning ( formerly Intermediate Statistical and Computational Methods) (spring; prerequisite: DSCC 462 or equivalent)
• DSCC 440: Data Mining (fall/spring)
• DSCC 461: Introduction to Databases (formerly Database Systems) (fall/spring)

Practicum students work in teams of three to four to understand a sponsoring organization's business problem, clean and analyze data, and devise an appropriate solution. Students also explore ethical issues related to the use of data science, and give a final presentation to the sponsor and the class. Two faculty members from within the Goergen Institute evaluate the final presentation, which serves as the master’s degree exit exam.

• DSCC 483: Data Science Practicum (taken final semester; fall/spring)

Students must to take three courses, for a minimum of 10 credits, from the following application areas:

• Business and social science
• Computational methods
• Health and biomedical sciences
• Statistical methodology

Students have the option of exploring data science broadly or earning a concentration by taking eight or more credits in a single application area.

They can also substitute an independent study, independent research and/or one to two internship credits for up to six application area credits.

View all current application area courses >

University of Rochester students are eligible for paid, summer internships through the New York State Center of Excellence in Data Science (CoE).

The CoE internship program partners with small businesses and startups in New York State to fund internship opportunities that help companies achieve their data science business objectives.

For more information, visit the CoE student internships page .

For additional information on the MS program, contact [email protected] or visit our Frequently Asked Questions page .

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Data Science

MASTER OF SCIENCE IN  DATA SCIENCE

Purdue’s innovative Master of Science in Data Science is an accessible, skills-focused master’s designed to meet the needs of professionals who have some background in data science and want to accelerate their expertise. This technical degree exposes students to coursework in in-demand areas like data visualization, machine learning, data mining, data analysis, communication and more. Applicable to many different fields and career paths, this master’s empowers professionals to harness the power of data and push their careers to new heights.

Ready to dive into the world of Data Science?

MS in Data Science

Build in-demand data skills through hands-on learning.

• Students will develop foundational knowledge and practical experience in the realm of data science.  
• Professional skills that will be learned include leadership, project management, and communication. 
• Technical skills that will be learned include machine learning, data mining, data analysis, and data visualization. 
• Students will gain expertise in the areas of data mining, data analysis, and data management.  
• Students will be able to apply technical skills in data science by completing hands-on projects. 

Source: LightcastTM (2023). Unique job postings for July 2022-2023. Projected growth for years 2023-2033.

Master of Science in Data Science

Curriculum overview.

The Master of Science in Data Science is 30 credit hours.   

Required Courses - 18 credit hours   15 credit hours in core courses and 3 credit hours of capstone 

GRAD 50500 – Foundations in Data Science (3 credits)  GRAD 50600- Big Data Tools and Technologies (3 credits)  GRAD 50700 – Cross Domain Data Communication and Visualization (3 credits)  GRAD 50800 – Data Analytics (3 credits)  GRAD 50900 – Applied Machine Learning: From Foundations to Latest Advances (3 credits)  GRAD 58900 – Capstone (3 credits) 

Industry Aligned Focus Areas – 9-12 credit hours   Students are encouraged to select a focus area from the data science-related topics below to enhance their professional experience.   

Applied Statistics (12 credits)   Managing IT Projects (12 credits)   IT Business Analysis (12 credits)   Spatial Data Science (12 credits)   

Free Electives – 3 credit hours, if needed   Any Graduate Level Course with advisor approval  

TOTAL CREDITS  30

Required Courses

GRAD 50500 – Foundations in Data Science (3 credits) Course Description: Foundations in Data Science is the inaugural course in the Data Science portfolio. Tailored for students with a technical background, this course provides a comprehensive introduction to key concepts, statistical techniques, and tools foundational to the field of data science. The syllabus integrates hands-on experience, ethical considerations, programming refresher, and agile project management principles to equip students with a robust foundation for their data science journey. Learning Outcomes: – Demonstrate a comprehensive understanding of core data science concepts and articulate the significance of data in various domains. – Acquire skills to perform exploratory data analysis, identify patterns, and make informed decisions based on statistical inference. – Critically analyze ethical dilemmas arising in data-driven contexts, apply ethical decision-making models, and navigate the legal implications of data handling and analysis. – Demonstrate a comprehensive understanding of programming fundamentals and the application of agile methodologies to manage and execute data science projects. – Access, navigate, and employ Purdue’s computing resources available through Purdue’s Rosen Center for Advanced Computing (RCAC). GRAD 50600 – Big Data Tools and Technologies Courses (3 credits) Course Description: This course is designed to equip students with the essential skills to handle big data effectively. It covers proficiency in big data technologies, scalable data processing techniques, and the integration of big data tools into data science workflows. Learning Outcomes: – Demonstrate proficiency in big data technologies. – Apply scalable data processing techniques to handle large datasets. – Integrate big data tools into comprehensive data science workflows. – Demonstrate proficiency with cloud computing. GRAD 50700 – Cross Domain Data Communication and Visualization (3 credits)  Course Description: This course focuses on the proficient use of data communication strategies and competencies. The course will focus on identifying data narratives, generating stories from data, illustrating with powerful and self-explanatory visualization, and basic principles of ethical use of non-data narrative frames for data communication. Designed for students with a technical background, this course aims to enhance students’ ability to extract and communicate meaning and narratives from raw data and visually represent it. Learning Outcomes: – Create effective and aesthetically pleasing data visualizations. – Communicate complex data findings clearly to diverse audiences. – Utilize interactive visualization techniques for dynamic exploration of datasets. – Proficiency of integrating ethics and data privacy when communicating with data. GRAD 50800 – Data Analytics (3 credits)  Course Description: This course provides an in-depth exploration of advanced data analysis techniques, predictive modelling, ensemble methods, and proficiency in data manipulation and transformation. It is designed for students with a technical background. Learning Outcomes: – Apply advanced data analysis techniques to extract meaningful insights from complex datasets. – Implement predictive modelling and ensemble methods for accurate data-driven predictions. – Demonstrate proficiency in data manipulation and transformation techniques. GRAD 50900 – Applied Machine Learning: From Foundations to Latest Advances (3 credits)  Course Description: This course provides an in-depth exploration of machine learning algorithms and data mining techniques, building on the foundational concepts introduced in the GRAD 50300 Foundations of Data Science course. Students will develop a comprehensive understanding of various machine learning algorithms, focusing on practical applications and hands-on experience. Additionally, the course will cover data mining techniques for dimension reduction and pattern discovery. Learning Outcomes: – Demonstrate a comprehensive understanding of popular machine learning algorithms, including supervised and unsupervised learning techniques. – Demonstrate the ability to analyze mathematical foundations and principles behind machine learning models. – Understand the nuances of applying various machine learning models, emphasizing the trade-offs between performance, computational complexity, and interpretability. – Students will actively explore unsupervised learning techniques, emphasizing clustering algorithms, and recognizing their vital role in solving real-world challenges. – Demonstrate proficiency into the principles of deep learning and investigate recent advancements, such as optimization using diffusion models, learning from unlabeled data using consistency models, and decentralized data processing through federated learning. GRAD 58900 – Capstone (3 credits) Course Description: The capstone course aims to provide students with an opportunity to integrate their accumulated knowledge, technical, and social skills to identify and solve a real-world data science problem, with an emphasis on the application domain. The capstone course for the Master of Science in Data Science provides students with practical experience applying the collective set of skills developed through the program to complete a professional project in support of a private, public, or non-profit partner. Students, in teams of 3-5 students each, will work with a product owner to scope out the project via a project charter that will include a timeline, milestones, and metrics that will yield benefits and a strategy for measuring the outcome of the project compared to a baseline. Learning Outcomes: – Identify the public, non-profit, or business objectives in a complex problem. – Evaluate and define an applied problem using data science that requires practical analysis and recommendations / and analytic output to a stakeholder. – Design a professional-level applied project that provides meaningful input to a targeted beneficiary. – Evaluate the proposed solution and interpret results concerning the “business” objectives. – Demonstrate an understanding of translational communication skills by communicating technical information to both technical and non-technical audiences.

Industry Aligned Focus Areas

Students are encouraged to select a focus area from the data science-related topics below to enhance their professional experience.  

Applied Statistics (12 credits)    Required  STAT 51600 – Basic Probability and Applications   STAT 51700 – Statistical Inference   Choose 2 of the following 4 courses below: STAT 51400 – Design of Experiments   STAT 52000 – Time Series and Applications  STAT 52500 – Intermediate Statistical Methodology   STAT 52600 – Advanced Statistical Methodology  

IT Business Analysis (12 credits) Required:  CNIT 53000 BAE – IT Business Analysis (3 credits) Three courses from the list below:   CNIT 53100 RMA – IT Requirements Analysis & Modeling (3 credits) CNIT 53200 EA – IT Enterprise Analysis (3 credits) CNIT 53500 ABA – Advanced Topics in IT Business Analysis (3 credits) CNIT 57000 BDA – IT Data Analytics (3 credits) CNIT 58500 PCM – Organizational and Change Management for IT Projects (3 credits)

Managing Information Technology Projects (12 credits) Required:   CNIT 55200 PME – IT Project Management   Choice of:  CNIT 55100 EPM – IT Economics  CNIT 58000 ATP – Advanced Topics in IT Project Management   CNIT 58200 EST – IT Estimating-Scheduling-Control   CNIT 58300 PPM – IT Program and Portfolio Management   CNIT 58500 PCM – Organizational and Change Management for IT Projects  CNIT 58600 RMP – IT Requirements Management  

Spatial Data Science (12 credits)   ABE 65100 – Environmental Informatics  AGRY 54500 – Remote Sensing of Land Resources  ASM 54000 – Geographic Information System Application  FNR 58700 – Advanced Spatial Ecology and GIS  

Elective Courses

Technical/Professional Electives (Courses vary between 1-3 Credit Hours)   ** Course list is subject to change   

STAT 58200 – Stat Consulting & Collaboration  

ABE 65100 – Environmental Informatics  

AGEC 68700 – Problem Solving and Project Management for Decision Makers  

AGRY 54500 – Remote Sensing of Land Resources  

ASM 54000 – Geographic Information System Application  

CE 59700 – Data Science for Smart Cities   

CNIT 57000 – IT Data Analytics  

CNIT 58100 – Enterprise Data Management  

CNIT 58100 – Information Security Governance 

COM 60311 – Seminar in Crisis Communication 

CS 50023 – Data Engineering  

CS 50025 – Foundations of Decision Making 

CS 57700 – Natural Language Processing 

ECE 56900 – Introduction to Robotic Systems  

ECE 59500 – Computer Vision for Embedded Systems 

ECE 59500 – Natural Language Processing  

ECON 57700 – Quantitative Economics and Python  

EDPS 53100 – Introduction to Measurement and Instrument Design 

FNR 58700 – Advanced Spatial Ecology and GIS   

ILS 69500 – Computational Text Analysis 

MATH 51100 – Linear Algebra with Applications  

MGMT 52500 – Marketing Analytics  

MGMT 52600 – Project Management  

MGMT 56800 – Supply Chain Analytics 

MGMT 58600 – Python Programming  

MGMT 59000 – R for Analytics  

STAT 50100 – Experimental Statistics I   

STAT 50600 – Statistical Programming and Data Management  

STAT 51200 – Applied Regression Analysis  

STAT 51700 – Statistical Inference  

STAT 52600 – Advanced Statistical Methodology    

STAT 52700 – Intro to Computing for Statistics  

Career Outlook

Get your career started by working in one of the following roles: Data Scientist, Software Developer, Machine Learning Engineer, Data Analyst, Quantitative Analyst, Data Engineer, and more!    Purdue University’s rigorous online programs allow you to earn a prestigious Purdue degree anytime and from anywhere. These programs give you access to outstanding faculty and top-quality curriculum in a convenient, flexible format to move your career and the world forward.

Ready to Become a Boilermaker?

Are you ready to join the Purdue  innovators  and  changemakers  always striving to make giant leaps forward in our industries and fields? Start your application today!

You are not alone in taking your next giant leap. Get your questions answered, receive application help, or plan your degree journey by speaking with an enrollment counselor. Request more information today. 

NYU Center for Data Science

Harnessing Data’s Potential for the World

CDS-Courant Undergraduate Research Program (CURP)

PROGRAM DATES: JANUARY 24 – MAY 18, 2022 LOCATION: RESEARCH PROJECTS WILL TAKE PLACE REMOTELY FELLOWSHIP AWARD: $3,500 APPLICATION DEADLINE: OCTOBER 31, 2021

The NYU Center for Data Science

The Center for Data Science (CDS) is the focal point for New York University’s university-wide efforts in Data Science. The Center was established in 2013 to advance NYU’s goal of creating a world-leading Data Science training and research facility, and arming researchers and professionals with the tools to harness the power of Big Data. Today, CDS counts 20 jointly appointed interdisciplinary faculty housed on three floors of our magnificent 60 5th Avenue building, one of New York City’s historic properties. It is home to a top-ranked MS in Data Science program, one of the first PhD programs in Data Science, and a new undergraduate program in Data Science, as well as a lively Fellow and Postdoctoral program. It has over 70 associate and affiliate faculty from 25 departments in 9 schools and units. With cross-disciplinary research and innovative educational programs, CDS is shaping the new field of Data Science.

Data Science for Everyone: Book & Video Series

The universality of data science as a discipline is highlighted with the introduction of a comprehensive textbook and accompanying video series, building upon the foundation laid by the esteemed “Data Science for Everyone” course. These educational materials provide extensive coverage on critical concepts ranging from “how to think like a data scientist” to core subjects such as statistics, programming, and machine learning approaches.

Free Online Resource: Yann LeCun’s Deep Learning Course

Yann LeCun’s Deep Learning Course covers the latest techniques in both deep learning and representation learning, focusing on supervised/self-supervised learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Free Online Resource: Mathematical Tools for Data Science

Mathematical Tools for Data Science, developed by CDS Associate Professor Carlos Fernandez-Granda, provides an introduction to tools from several areas of mathematics such as linear algebra, Fourier analysis, probability theory, and convex optimization, which are useful in data science.

CDS Shines at NeurIPS 2023

Celebrating Success: CDS MS Graduates Forge Impressive Paths in Data Science

Micah Goldblum’s Survey Offers a Deeper Look Into Deep Learning

Hiring Clinical Faculty for 2024: Interview with current CDS Clinical Assistant Professor Louis Mittel

Upcoming events, events in august–november 2024.

Search Utah State University:

Data science - bs.

About This Degree

Data Science is an interdisciplinary field that includes the management, analysis, and visualization of data to make evidence-based decisions, and draws primarily from the fields of mathematics, statistics, and computer science.

Much of today’s STEM innovations are data-driven, with artificial intelligence (AI) and machine learning playing an increasing role. This degree provides students the skills critically needed by employers in industry, academia, and government to effectively analyze data and understand modern machine learning methods and AI systems.

What You Will Learn

The program includes core courses in mathematics, statistics, and computer science, in addition to core and elective courses in data science and AI.

A rigorous foundation in these areas will prepare students to:

• use modern computing tools, programming languages, and algorithms to build, clean, manage, process, and analyze large datasets;
• accurately interpret and analyze data to facilitate forecasting, prediction, and decision making; and
• understand the underlying mechanics, assumptions, strengths, and weaknesses of conventional and modern data science and AI methods so that students can apply the methods appropriately and develop new data science methods when needed.

At a Glance

College: College of Science

Department: Computer Science Department

USU Locations:

• Logan campus

Faculty: View profiles of faculty members on the department directory .

Learn More: Program website

Program Requirements

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Career And Outcomes

Career opportunities.

• Data Scientist
• Data Engineer
• Machine Learning Engineer
• Statistician

These positions exist in nearly all fields and areas including private industry, national labs, government, and academia.

Job Outlook

Request for information and advising.

We will build on your goals and experiences while working together to design an individualized semester-by-semester plan. Your personalized plan will help you see a path from where you are now on to graduation. Even if you are unsure of the major you would like to pursue, we can help with resources and ideas.

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USU Locations

LOGAN CAMPUS

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Visit campus.

Schedule a campus tour and come see what it's all about.

Cost and Aid

Learn about tuition, scholarships, and other financial aid opportunities.

How to Apply

Start your degree path and apply now.

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Study math in a versatile degree program while you enjoy the collaborative environment of a close-knit department with broad applications, from computational math to actuarial science to data- and technology-driven analyses.

Gain valuable expertise in all areas of statistics — including probability, quality control, analysis, and more — as you prepare for a career as a statistician for a variety of companies or an actuary focusing on finance-related industries.

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Data Science

Data Science is a rapidly growing field. Our graduates have the right foundation to manage and analyse big data, driving innovation in organisations across all industries.

Subject overview

The last decade has seen an explosion in the amount of data available. It has evolved into the most important asset for many companies and people. Big data is ubiquitous, but to extract information, individuals require the ability to both manage and analyse the data. This ability to turn data into information, knowledge and innovative products often separates success from failure. 

Data scientists have the skill set to drive innovation and affect the success of start-ups, established businesses and organisations, governments and science projects, as well as media, broadcasting and cultural events.

The Data Science major will provide initial preparation for students wishing to pursue a career in this area. It ties together courses from Computer Science, Statistics and Mathematics to provide the necessary background and training.

The Department of Statistics is the birthplace of the R Project. Founded in 1996 by Associate Professors Robert Gentleman and Ross Ihaka. R is a programming language and environment for statistical computing and graphics. It is taught around the world and is used by Ivy League universities, Google, Uber, and many more organisations. Learn more about the R Project .

Where can Data Science take you?

On the world stage, Data Science is a rapidly growing field with an unmet demand for suitably qualified graduates.

• Data analyst
• Data scientist
• Database administrator
• Information officer
• Insight manager
• Statistician

Meet a graduate

I really loved the blend of Statistics and Computer Science that Data Science offered; the programme gives you a solid skill set in both areas. This is extremely useful as it opens up so many career paths and gives you a lot to choose from depending on which skills you enjoy more.

Jasmine Chhor, Powered Data Consultant

Read Jasmine's full story here .

Explore your study options in Data Science

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Expert Data Science

About the role.

Major accountabilities:

• Apply state-of-the-art bioinformatic and data science methods to derive novel insights and progress our early drug discovery projects in collaboration with project teams.
• Enable molecular disease understanding and hypothesis generation through the integration of different genome-scale data types in close collaboration with data scientists with complementary expertise (e.g., cheminformatics, imaging analysis, protein structural informatics).
• Serve as a bridge between valuable data assets and project teams to enrich early preclinical hypothesis generation, where possible with insights translated from late-stage clinical data.
• Drive experimental design and communicate analysis outcomes to broad scientific audiences comprising both experimental and computational scientists.
• Act as a broker between biological questions from project teams and the application of appropriate computational tools to identify solutions.

Role Requirements

• Master's or PhD in bioinformatics/computational biology, or a wet-lab molecular biology degree with strong hands-on experience in analysing genomics data; alternatively, a degree in a quantitative subject (e.g., computer science, data science, mathematics, physics, chemistry) in combination with demonstrable experience in life sciences/drug discovery.
• Experience in handling genomics data, including both bulk and single cell technologies.
• Demonstrated ability to integrate data across data modalities in order to answer scientific questions, and/or to formulate new biological hypotheses.
• Hands-on experience with deep learning methods highly desirable.
• Ability to present complex data science concepts in digestible terms to diverse scientific audiences leveraging innovative data visualization.
• Strong scientific curiosity, initiative, and learning agility.
• Ability to work as part of an interdisciplinary team (i.e., biologists, chemists, data scientists), with strong communication skills.
• Expertise working in Linux high performance computing and cloud environments.
• Expertise in scripting languages, experience with Python and R scientific stacks.
• Familiarity with best practices in computational reproducible research, literate progamming (e.g., jupyter, R markdown), version control.
• Hands-on experience using major public biomedical research databases (e.g., NCBI, UniProt, OpenTargets, and others).

Why Novartis? Our purpose is to reimagine medicine to improve and extend people’s lives and our vision is to become the most valued and trusted medicines company in the world. How can we achieve this? With our people. It is our associates that drive us each day to reach our ambitions. Be a part of this mission and join us! Learn more here: https://www.novartis.com/about/strategy/people-and-culture You’ll receive: You can find everything you need to know about our benefits and rewards in the Novartis Life Handbook. https://www.novartis.com/careers/benefits-rewards Commitment to Diversity and Inclusion: Novartis is committed to building an outstanding, inclusive work environment and diverse teams' representative of the patients and communities we serve. Accessibility and accommodation Novartis is committed to working with and providing reasonable accommodation to all individuals. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the recruitment process, or in order to receive more detailed information about the essential functions of a position, please send an e-mail to inclusion.switzerland@novartis.com and let us know the nature of your request and your contact information. Please include the job requisition number in your message. Join our Novartis Network: If this role is not suitable to your experience or career goals but you wish to stay connected to hear more about Novartis and our career opportunities, join the Novartis Network here: https://talentnetwork.novartis.com/network

Why Novartis: Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting and inspiring each other. Combining to achieve breakthroughs that change patients’ lives. Ready to create a brighter future together? https://www.novartis.com/about/strategy/people-and-culture

Join our Novartis Network: Not the right Novartis role for you? Sign up to our talent community to stay connected and learn about suitable career opportunities as soon as they come up: https://talentnetwork.novartis.com/network

Novartis is committed to building an outstanding, inclusive work environment and diverse teams' representative of the patients and communities we serve.