phd in data analysis

PhD in Data Science: Program Overview

The field of data science has emerged from advances in computational speed, data availability, and novel analysis methods. It demands a new type of researcher: the rigorously trained, cross-disciplinary, and ethically responsible data scientist. 

Launched in Fall 2017, the pioneering CDS PhD Data Science program seeks to produce such researchers. It has gained recognition as one of the top and most selective data science doctoral programs globally, recently earning an NSF NRT training grant.

The CDS PhD program rigorously trains future data scientists to:

• Develop methodologies and use statistical tools to answer cross-disciplinary questions
• Clearly communicate to extract precise questions from big, heterogeneous, uncertain data
• Translate research insights into practical applications across sciences, medicine, industry, and government
• Understand the ethical implications of their work

Our mission is to nurture this new generation of data scientists in an environment where methodological innovations are developed and successfully applied to various domains. We believe that combining fundamental research on the principles of data science with translational projects involving domain experts creates a virtuous cycle, advancing both data science methodology and real-world applications.

The program has an innovative core curriculum, a novel data assistantship mechanism that provides training of skills transfer through rotations and internships, and communication and entrepreneurship modules. Students are exposed to a wider range of fields than in traditional PhD programs while working with our interdisciplinary faculty. We also offer a medical track for those interested in healthcare applications or developing novel theoretical models from medical questions.

Successful candidates are guaranteed financial support, including tuition and a competitive stipend, for up to five years in the fall and spring semesters.* 

We invite you to learn more through our webpage or by contacting [email protected] . *Students also have the opportunity to pursue their study and research with Data Science faculty at NYU Shanghai . With this opportunity, students generally complete their coursework in New York City before moving full-time to Shanghai for their research. For more information, please visit the NYU Shanghai Ph.D. page .

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Where To Earn A Ph.D. In Data Science Online In 2024

Updated: Apr 3, 2024, 2:15pm

Data science is among the most in-demand skill sets in the modern economy. Data science professionals help businesses make decisions by creating analytical models, combining elements of math, artificial intelligence, machine learning and statistics.

If you want to pursue a high-paying data science career or teach data science at the college level, you may want to earn a terminal degree in the field. Online Ph.D. in data science programs allow you to advance your career while balancing other responsibilities at work or home.

We found two online data science programs that met our ranking criteria. Read on to learn more about these schools and find answers to frequently asked questions about data science.

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Online Ph.D. in Data Science Option

Capitol technology university, national university.

Located just outside Washington, D.C., in South Laurel, Maryland, Capitol Technology University offers an online doctoral degree in business analytics and data science. The program includes a limited residency requirement: Students must complete a course in contemporary research in management on campus, during which they take a qualifying exam. The degree requires 54 to 66 credits, and students can graduate within three years.

All students must also complete a dissertation and an oral defense of their work. The program costs $950 per credit for both in-state and out-of-state learners. Retired and active duty military receive a tuition discount.

At a Glance

• School Type: Private
• Application Fee: $100
• Degree Credit Requirements: 54 to 66 credits
• Program Enrollment Options: Part-time
• Notable Major-Specific Courses: Management theory in a global economy; analytics and decision analysis
• Concentrations Available: N/A
• In-Person Requirements: Yes, for residency

Based in San Diego, California, National University (NU) offers a variety of online programs, including a Ph.D. in data science. NU’s program requires 60 credits and takes an estimated 40 months. NU aims for flexibility, delivering coursework asynchronously and offering a new start date each Monday. The curriculum comprises 20 courses covering data science principles and data preparation methods.

NU runs on the quarter system and charges $442 per quarter unit for graduate courses. The program does not include any in-person requirements.

• Application Fee: Free
• Degree Credit Requirements: 60 credits
• Notable Major-Specific Courses: Principles of data science, data preparation methods
• In-Person Requirements: No

How To Find the Right Online Ph.D. in Data Science for You

Consider your future goals.

A Ph.D. in data science makes sense if you want to become a college professor , conduct original research or compete for the highest-paying and most cognitively demanding business analytics and machine learning positions. If you plan to pursue other careers, you may not need a terminal degree in this field.

If you want to work in academia, make sure your chosen doctorate in data science includes a dissertation requirement. A dissertation allows you to perform original research and contribute to scholarship in your field before you graduate. In turn, you’ll get a sense of your chosen career and a head start on professional publication.

Understand Your Expenses and Financing Options

Per-credit tuition rates for the programs in our guide ranged from $442 to $950. A 60-credit degree from NU totals about $26,500, while the 66-credit option at Capitol Tech costs more than $62,000.

Private universities, including NU and Capitol Tech, tend to cost more than public schools. Graduate students at nonprofit private universities paid an average of $20,408 per year in 2022-23, according to the National Center for Education Statistics . Over the course of a typical three-year Ph.D. program, this translates to about $61,000. This roughly matches Capitol Tech’s tuition, while NU offers a more affordable program.

While a Ph.D. might help you land a lucrative role in the long run, the upfront investment is still significant. Make sure to fill out the FAFSA ® to access federal student aid. This application is the gateway to opportunities like scholarships, grants and loans. You can pursue similar opportunities through schools and nonprofit organizations.

As a doctoral student, you may be able to access graduate assistantships or stipends, but these are often reserved for on-campus students who teach undergraduates or assist professors with research.

Should You Enroll in a Ph.D. in Data Science Online?

Pursuing a Ph.D. in data science online suits a specific kind of learner. To decide if that’s you, ask yourself a few key questions:

• What’s my budget? In some cases, public universities allow students who exclusively enroll in online courses to pay in-state or otherwise discounted tuition rates. Even if you have to pay full price, distance learners generally save on costs associated with housing and transportation.
• What are my other commitments? Distance learning is often a good fit for parents and students who need to work full time while pursuing their degree. Learners with outside responsibilities might pursue a program with asynchronous course delivery, which eliminates scheduled class sessions.
• What’s my learning style? Distance learning requires a great deal of discipline, organization and time management. If you need external accountability or prefer the structure of a peer group or physical classroom, on-campus learning might offer a better fit.

Accreditation for Online Ph.D.s in Data Science

There are two important types of college accreditation to consider: institutional and programmatic.

Institutional accreditation is essential; it involves vetting schools to ensure the quality of their finances, academics, and faculty, among other areas. The Council for Higher Education Accreditation (CHEA) and U.S. Department of Education oversee the regional agencies that administer this process.

You should only enroll at institutionally accredited schools. Otherwise, you will be ineligible for federal financial aid. You can check a school’s accreditation status on its website or by visiting the directory on CHEA’s website .

Individual departments and degrees earn programmatic accreditation based on their curriculum, faculty and learner outcomes. However, this process has not been widely established for data science programs, so it shouldn’t make or break your enrollment decision. However, you can still keep an eye out for accreditation from the Data Science Council of America (DASCA).

Our Methodology

We ranked two accredited, nonprofit colleges offering online Ph.D.s in data science in the U.S. using 15 data points in the categories of student experience, credibility, student outcomes and affordability. We pulled data for these categories from reliable resources such as the Integrated Postsecondary Education Data System ; private, third-party data sources; and individual school and program websites.

Data is accurate as of February 2024. Note that because online doctorates are relatively uncommon, fewer schools meet our ranking standards at the doctoral level.

We scored schools based on the following metrics:

Student Experience:

• Student-to-faculty ratio
• Socioeconomic diversity
• Availability of online coursework
• Total number of graduate assistants
• Proportion of graduate students enrolled in at least some distance education

Credibility:

• Fully accredited
• Programmatic accreditation status
• Nonprofit status

Student Outcomes:

• Overall graduation rate
• Median earnings 10 years after graduation

Affordability:

• In-state graduate student tuition
• In-state graduate student fees
• Alternative tuition plans offered
• Median federal student loan debt
• Student loan default rate

We listed the two schools in the U.S. that met our ranking criteria.

Find our full list of methodologies here .

Frequently Asked Questions (FAQs) About Earning a Ph.D. in Data Science Online

Can i do a ph.d. in data science online.

Yes, you can. National University and Capitol Technology University both offer Ph.D. programs in data science that you can complete mostly or entirely online.

Is a Ph.D. worth it for data science?

It depends on your goals and circumstances. A Ph.D. in data science may be a good fit if you want to pursue a career in research or academia or compete for advanced, lucrative positions in business analytics, artificial intelligence or machine learning.

Is it okay to get a Ph.D. online?

Yes, as long as the program is accredited. Distance learning requires strong motivation and self-discipline, so it suits some students better than others.

Can you become a professor with an online Ph.D.?

Yes, you can. Online diplomas feature the same coursework and degree requirements as in-person degrees, and your degree won’t say “online”.

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DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. Cross-disciplinary work is encouraged. The PhD program prepares students for careers as university teachers and researchers as well as research statisticians and 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
• 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, 2 Probability for Statistical Inference 1, 2
• STAT 440 Applied Stochastic Processes for Statistics
• 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 data science and 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 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 approved non-STAT courses.

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

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

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PhD in Data Analytics Programs

Kritika Versha is a data engineer/analyst at Michigan Medicine.

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

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

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

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

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

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

Applications of a PhD in Analytics

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

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

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

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

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

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

Best PhD in Data Analytics Degree Programs for 2024

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

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

PhD in Data Analytics Curriculum

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

Components of PhD in Data Analytics

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

Credit Requirements

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

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

Pre-Candidacy Research Projects

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

Preliminary or Qualifying Examination

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

Teaching Requirement

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

Dissertation Proposal

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

Successful Dissertation Defense

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

Optional Requirements

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

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

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

PhD in Data Analytics Online

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

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

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

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

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

PhD in Big Data Analytics

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

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

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

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

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

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

Career Paths for Data Analytics PhDs

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

Academic Positions 

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

Industry Positions 

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

Frequently Asked Questions

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

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

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

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

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

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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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Natural Sciences and Mathematics

Mathematical sciences, doctor of philosophy in data science and statistics.

The program offers extensive coursework and intensive research experience in theory, methodology, and applications of statistics (see  degree requirements ). 

• Faculty members with broad and diverse research interests are available to supervise doctoral dissertations .
• Financial support in the form of assistantships, full tuition support, and scholarships and awards are provided. Additional scholarships are available for US citizens and permanent residents.
• Our students, both domestic and international, have a strong record of starting in full-time jobs right after graduation .
• Students have opportunities to participate in active  Statistics Seminar  series and the departmental  Colloquium series.
• To enhance career prospects, students can pursue  Graduate Certificate in Data Science , and possibly use the certificate courses to fulfill the PhD degree elective requirements.
• NSM Career Success Center  is available to support professional development and experiential learning of students.  
• GRE test score is not required for admission.

100% of our 22 PhD graduates since 2020, both domestic and international, secured full-time employment within a few months of receiving their degrees. 

Placement of 2022 & 2023 PhD Graduates

See a more complete list

Assistantships

Graduate Teaching Assistantships are offered to qualified PhD students on a competitive basis. These assistantships include a monthly stipend (currently set at $2,400) along with a full tuition waiver (covering 9 credit hours per term in the Fall and Spring semesters). The assistantship additionally covers the cost of health insurance purchased through the university and most fees. Graduate Research Assistantships for advanced PhD students are also available on some faculty members’ research grants. Typically, assistantship support is provided for five years and encompasses the Summer semester as well.

All admitted students are considered for assistantships; no separate application is necessary. 

Scholarships, Fellowships & Awards

PhD students are additionally supported through the following awards:

• NSM McDermott PhD Admission Fellowship  (for highly qualified new students, offered at the time of admission)
• Dean’s Fellowship  and  EEF Scholarship  (for highly qualified new students who are U.S. citizens and permanent residents, offered at the time of admission)
• Julia Williams Van Ness Merit Scholarship  and  Mei Lein Fellowship
• Outstanding Teaching Assistant of the Year Award
• Dean of Graduate Education Dissertation Research Award
• Best Dissertation Award ,  David Daniel Thesis Award , and  Outstanding Graduate Student Award

Conference Travel Support

NSM Conference Travel Award  and  Betty and Gifford Johnson Travel Award  are available to provide financial support to PhD students to present their research at professional conferences.

• How To Apply
• Frequently Asked Questions
• Scholarships & Awards
• Office of Admissions and Enrollment

Graduate Resources

• Mathematics Research
• Statistics & Actuarial Science Research
• Graduate Advisors
• Mathematics Courses
• Statistics Courses
• Actuarial Science Courses
• Qualifying Exam Archive
• Office of Graduate Education

Ready to start your application?

Before you apply, visit our  How to Apply  page to get familiar with the admission requirements and application process.

[email protected]

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

University of South Florida

Muma College of Business

Tampa | St. Petersburg | Sarasota-Manatee

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Phd in big data analytics.

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

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

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

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

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

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

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

Application deadline is February 1. 

Table of Contents

What is a phd in data analytics, phd in data science vs. phd in data analytics, a doctorate vs master’s degree in data analytics, why earn a phd in data analytics, phd in data analytics benefits, phd in data analytics disadvantages, careers for data analytics phd holders, phd in data analytics curriculum, considerations when choosing a phd program or college, phd in data analytics preparation courses, how much does a phd in data analytics cost, phd in data analytics - everything you need to know.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Become a Data Science & Business Analytics Professional

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

Post Graduate Program in Data Analytics

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

Data Analyst

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

Here's what learners are saying regarding our programs:

Felix Chong

Project manage , codethink.

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

Gayathri Ramesh

Associate data engineer , publicis sapient.

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

Career options for PhD in Data Science holders:

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

A general PhD in Data Analytics curriculum looks like this:

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

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

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

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

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

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

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

1. Is a PhD in data analytics worth it?

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

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

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

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

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

4. Which PhD is most in demand?

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

5. Do PhD students get paid enough?

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

Data Science & Business Analytics Courses Duration and Fees

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

Learn from Industry Experts with free Masterclasses

Data science & business analytics.

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

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Get Affiliated Certifications with Live Class programs

• PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

PhD in Data Science

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

Value Proposition Description

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

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

Applying to the Data Science PhD Program

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

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

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

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

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

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

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

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

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

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

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

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

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

MA 511. Applied Statistics for Engineers and Scientists

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

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

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

Software Tools & Labs

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

State-of-the-art software programs:

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

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

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

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

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

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

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

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

Faculty Profiles

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

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

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

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

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

The Data Science Community

Getting Involved

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

Refer a Friend

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

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

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

Advance Your Data Science Skills with a Graduate Certificate

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

Ready to Start Your Data Science Path?

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

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

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

Big Data Analytics (PhD)

Program at a glance.

• In State Tuition
• Out of State Tuition

Learn more about the cost to attend UCF.

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

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

The Ph.D. in Big Data Analytics requires 72 hours beyond an earned Bachelor's degree. Required coursework includes 30 credit hours of courses, 21 credit hours of restricted elective coursework, and 21 credit hours of dissertation research.

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

Statistical Colloquium Requirement - The department has a course, STA 7920 (Statistical Colloquium). This is a 0-credit course and should not impact your GPA. However, you will need at least 5 semesters of STA 7920 before you can graduate. With this course, you must attend the departmental colloquial.

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

Program Tracks/Options

• Statistics Track

Application Deadlines

• International

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Program Prerequisites

Students must have the following background and courses completed before applying to the Big Data Analytics PhD program. These courses are: MAC 2311C: Calculus with Analytic Geometry I, MAC 2312: Calculus with Analytic Geometry II, MAC 2313: Calculus with Analytic Geometry III, MAS 3105: Matrix and Linear Algebra or MAS 3106: Linear Algebra. These prerequisites are undergraduate courses offered through the Math department.

Degree Requirements

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

Required Courses

• STA6106 - Statistical Computing I (3)
• STA6236 - Regression Analysis (3)
• STA6326 - Theoretical Statistics I (3)
• STA6327 - Theoretical Statistics II (3)
• STA6246 - Linear Models (3)
• STA6107 - Statistical Computing II (3)
• STA6366 - Statistical Methodology for Data Science I (3)
• STA6367 - Statistical Methodology for Data Science II (3)
• STA7920 - Statistical Colloquium
• STA7348 - Bayesian Modeling and Computation (3)
• STA7722 - Statistical Learning Theory (3)
• STA7734 - Statistical Asymptotic Theory in Big Data (3)

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

• Other courses may be included in a Plan of Study with departmental approval. Other electives can be used at the discretion of the student advisor and/or Graduate Coordinator.
• STA6226 - Sampling Theory and Applications (3)
• STA6237 - Nonlinear Regression (3)
• STA6346 - Advanced Statistical Inference I (3)
• STA6347 - Advanced Statistical Inference II (3)
• STA6507 - Nonparametric Statistics (3)
• STA6662 - Statistical Methods for Industrial Practice (3)
• STA6705 - Data Mining Methodology III (3)
• STA6707 - Multivariate Statistical Methods (3)
• STA6709 - Spatial Statistics (3)
• STA6857 - Applied Time Series Analysis (3)
• STA7239 - Dimension Reduction in Regression (3)
• STA7719 - Survival Analysis (3)
• STA7935 - Current Topics in Big Data Analytics (3)
• CAP5610 - Machine Learning (3)
• CAP6307 - Text Mining I (3)
• CAP6315 - Social Media and Network Analysis (3)
• CAP6318 - Computational Analysis of Social Complexity (3)
• CAP6737 - Interactive Data Visualization (3)
• COP5537 - Network Optimization (3)
• COP6526 - Parallel and Cloud Computation (3)
• COP6616 - Multicore Programming (3)
• COT6417 - Algorithms on Strings and Sequences (3)
• COT6505 - Computational Methods/Analysis I (3)
• ECM6308 - Current Topics in Parallel Processing (3)
• EEL5825 - Machine Learning and Pattern Recognition (3)
• EEL6760 - Data Intensive Computing (3)
• FIL6146 - Screenplay Refinement (3)
• ESI6247 - Experimental Design and Taguchi Methods (3)
• ESI6358 - Decision Analysis (3)
• ESI6418 - Linear Programming and Extensions (3)
• ESI6609 - Industrial Engineering Analytics for Healthcare (3)
• ESI6891 - IEMS Research Methods (3)
• STA5825 - Stochastic Processes and Applied Probability Theory (3)
• COP6731 - Advanced Database Systems (3)
• STA5104 - Advanced Computer Processing of Statistical Data (3)
• STA5176 - Introduction to Biostatistics (3)
• STA5703 - Data Mining Methodology I (3)
• STA6223 - Conventional Survey Methods (3)
• STA6224 - Bayesian Survey Methods (3)
• STA6704 - Data Mining Methodology II (3)
• STA6714 - Data Preparation (3)
• MAP6195 - Mathematical Foundations for Massive Data Modeling and Analysis (3)
• MAP6197 - Mathematical Introduction to Deep Learning (3)
• COP5711 - Parallel and Distributed Database Systems (3)
• CNT5805 - Network Science (3)

Dissertation

• Earn at least 21 credits from the following types of courses: STA 7980 - Dissertation Research The student must select a dissertation adviser by the end of the first year. In consultation with the dissertation adviser, the student should form a dissertation advisory committee. The dissertation adviser will be the chair of the student's dissertation advisory committee. In consultation with the dissertation advisor and with the approval of the chair of the department, each student must secure qualified members of their dissertation committee. This committee will consist of at least four faculty members chosen by the candidate, three of whom must be from the department and one from outside the department or UCF. Graduate faculty members must form the majority of any given committee. A dissertation committee must be formed prior to enrollment in dissertation hours. The dissertation serves as the culmination of the coursework that comprises this degree. It must make a significant original theoretical, intellectual, practical, creative or research contribution to the student's area within the discipline. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor. The dissertation will be completed through a minimum of 15 hours of dissertation research credit.

Examinations

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

Qualifying Examination

• The qualifying examination is a written examination that will be administered by the doctoral exam committee at the start of the fall term (end of the summer) and at the start of the spring term. The courses required to prepare for the examination are STA 6246, STA 6366, STA 6367, STA 6326, STA 6327 and STA 6236. Students must obtain permission from the Graduate Program Coordinator to take the examination. Students normally take this exam just before the start of their second year and are expected to have completed the exam by the end of their second year. To be eligible to take the Ph.D. qualifying examination, the student must have a minimum grade point average of 3.0 (out of 4.0) in all the coursework for the Ph.D. The exam may be taken twice. If a student does not pass the qualifying exam after the second try, he/she will be dismissed from the program. It is strongly recommended that the student select a dissertation adviser by the completion of 18 credit hours of course work, and it is strongly recommended that the student works with the dissertation adviser to form a dissertation committee within two semesters of passing the Qualifying Examination. To pass the exam, students need to pass all 4 parts. Students must take all 4 parts of the qualifying exam in their first attempt and must have completed all courses covered by the exam.

Candidacy Examination

• The candidacy exam is administered by the student's dissertation advisory committee and will be tailored to the student's individual program to propose either a research‐ or project‐based dissertation. The candidacy exam involves a dissertation proposal presented in an open forum, followed by an oral defense conducted by the student's advisory committee. This committee will give a Pass/No Pass grade. In addition to the dissertation proposal, the advisory committee may incorporate other requirements for the exam. The student can attempt candidacy any time after passing the qualifying examination, after the student has begun dissertation research (STA7919, if necessary), but prior to the end of the second year following the qualifying examination. The candidacy examination can be taken no more than two times. If a student does not pass the candidacy exam after the second try, he/she will be removed from the program After passing the candidacy examination and meeting other requirements, the student can register for Doctoral Dissertation (STA7980). A minimum of 21 Doctoral Dissertation credit hours are required. The Candidacy Examination can be attempted after passing the qualifying examination. The Candidacy Examination must be completed within one years after passing the qualifying examination. A student must successfully pass the Candidacy Examination within at most two attempts.

Admission to Candidacy

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

Masters Along the Way

• PhD Students can obtain their Master's degree in Statistics & Data Science - Data Science Track along the way to their PhD degree. To satisfy the requirements for the MS degree, the student must complete the following requirements: 1 - Complete the 24 hours of required courses for the MS degree - Data Science track. 2.- Complete 12 credit hours from the elective list for the MS degree - Data Science track, except STA 5205, STA 5505 and STA 5738. The student has the option of choosing between thesis option or non-thesis option.

Independent Learning

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

Grand Total Credits: 72

Application requirements, financial information.

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

Fellowship Information

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

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Current Students

• Mendoza Alumni
• The Notre Dame MBA
• Dual Degrees
• Accountancy (MSA)
• Business Analytics (MSBA)
• Digital Marketing (MSDM)
• Finance (MSF)
• Management (MSM)
• Nonprofit Administration (MNA)
• Global Executive MBA
• Executive Master of Nonprofit Administration (EMNA)

Ph.D. in Analytics

• PhD in Management
• Undergraduate (BBA)

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

Why Attain a PhD in Analytics?

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

Why Notre Dame?

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

Faculty Productivity and Reputation

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

Research Labs and Centers

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

Partnerships with Industry

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

Cutting-edge Curriculum

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

Institutional Prestige

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

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

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

Program Structure

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Ahmed Abbasi

Frequently Asked Questions

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

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

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

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

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

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

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

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

2024 Best Online PhD in Data Analytics [Doctorate Programs]

A PhD in Data Analytics is a terminal degree that prepares students to become experts in data-based careers.

People who enter a PhD in Data Analytics program are usually passionate about making discoveries and performing in-depth research. Since these programs are heavily research-based, many students who enroll do so in hopes of entering the world of academics.

Editorial Listing ShortCode:

Graduates may also enter fields like research, business, healthcare, and government. Read on to see if a PhD program in data analytics is right for you!

Universities Offering Online Doctorate in Data Analytics Degree Programs

Methodology: The following school list is in alphabetical order. To be included, a college or university must be regionally accredited and offer degree programs online or in a hybrid format.

Capitol Technology University

Capitol Technology University offers a PhD in Business Analytics and Data Science that can be completed mostly online, with just one course required on campus. The program is flexible to meet the needs of busy professionals. All of Capitol’s instructors work in the field and know how to apply theoretical knowledge in the real world.

Capitol Technology University is accredited by the Middle States Commission on Higher Education.

Colorado Technical University

Colorado Technical University offers a Doctor of Computer Science in Big Data Analytics. The program is mostly online but does require a residency component. Work on a dissertation is integrated throughout coursework, so students can potentially finish within 3 years. Scheduling is flexible, and there are multiple start dates offered each year.

Colorado Technical University is accredited by the Higher Learning Commission.

DePaul University

DePaul University offers a PhD in Computer and Information Sciences with tracks in Computing and Information Systems. Most classes in the program are offered during the evenings and online, and the program can accommodate both full-time and part-time students. Students in the program must write and defend a dissertation and have access to labs on campus.

DePaul University is accredited by the Higher Learning Commission.

Florida Atlantic University

Florida Atlantic University offers a Professional PhD in Computer Science with a concentration in Data Science and Analytics that can be earned on campus and online. On-campus classes are available during the weekends and evenings. The program can typically be finished in 3 years. The curriculum is designed to prepare students for careers in research or technical leadership.

FAU is accredited by the Southern Association of Colleges and Schools Commission on Colleges.

Grand Canyon University

Grand Canyon University offers a DBA in Data Analytics (Quantitative Research). The program requires the completion of 60 credits of 8 week courses. Potential courses include Using Business Analytics for Competitive Advantage, Predictive Modeling, and Enterprise Data Complexity. The program is mostly online, but students are required to come to campus during the dissertation process.

Grand Canyon University is accredited by the Higher Learning Commission.

National University

National University offers a Doctor of Philosophy in Data Science. The program is 100% online and designed to allow students to study on their own time. New classes start every week, so students can enroll whenever it is most convenient. The program requires the completion of 20 courses.

National University is accredited by the WASC Senior College and University Commission.

Simmons University

Simmons University offers a PhD in Library and Information Science. Courses are available online and on campus, and online courses may be either synchronous or asynchronous. The program is designed to be completed with a small cohort and allow students to graduate with a portfolio of their research and other accomplishments.

Simmons University is accredited by the New England Commission of Higher Education.

University of North Texas

The University of North Texas offers a Doctor of Philosophy in Information Science with a concentration in Data Science. The program offers courses that are available online and in blended formats and highly interactive residential experiences. The curriculum is research-oriented and highly interdisciplinary.

The University of North Texas is accredited by the Southern Association of Colleges and Schools Commission on Colleges.

University of the Cumberlands

The University of the Cumberlands offers a PhD in Information Technology. The curriculum explores how to solve complex problems in the IT industry. Coursework can be completed online, and the program is designed to allow students to complete their courses on their own schedule without leaving their current careers.

The University of the Cumberlands  is accredited by the Southern Association of Colleges and Schools Commission on Colleges.

University of the Southwest

The University of the Southwest offers a DBA in Data Analytics. The program is 100% online, with no required residencies. It provides a complimentary laptop after enrollment. The curriculum explores how to apply quantitative research to inform business decisions and solve problems.

The University of the Southwest is accredited by the Higher Learning Commission.

Online PhD in Data Analytics Programs

A PhD in Data Analytics is a terminal degree program that teaches students how to gather and use data to inform the decisions of an organization.

This program is strategic for professionals looking to elevate their data expertise because it is the highest degree you can receive in a data-related field. If you have a love for numbers and data and have already completed your undergrad in a related discipline, then you might consider a PhD as the next step in your professional development.

One of the main factors of a PhD in Analytics program is that it is heavily research focused. Your education will also expose you to various courses that help you develop your data expertise. You’ll likely take advanced courses in subjects like:

• Probability
• Machine learning
• Interference

That said, PhD programs give you practical exposure through the emphasis on research. Professionals who are more interested in a practice-oriented degree may consider earning a DBA in Data Analytics, which is a Doctor of Business Administration in Data Analytics.

Should you pursue a PhD in Data Analytics, you can expect the back half of your education to involve the completion of a dissertation. This can serve as the culmination of your education and be a testament to your newfound expertise.

Because data science plays a vital role in business, there are various positions that graduates can pursue. Some of these career paths include:

• Data scientist
• Data analyst
• Data Architect
• Data Engineer
• Statistician
• College professor

These represent only a few of the potential occupations available in this field. You can assess your academic and career goals to help determine if a PhD in Data Analytics is right for you.

Data Analytics Careers and Salaries

Careers in data analytics are plentiful, and PhD graduates are often viewed as experts in the field. According to the Bureau of Labor Statistics , here are the median wages of potential career paths related to data analytics and data science.

No career or salary is guaranteed by simply having a degree, but a PhD in Data Analytics could help you further your expertise and skills in this growing field.

Professionals who can digest complex data and effectively communicate their findings often work with other team members to find constructive solutions to organizational issues. As can be expected, these graduates usually enter data-based fields.

PhD in Data Analysis Curriculum & Courses

A data science degree program will expose you to many courses with the aim of making you an expert in gathering, assessing, and relaying data. Below is a list of example courses you may encounter in your studies:

• Introduction to the Theory of Algorithms: This course will familiarize you with different analysis methods for algorithms.
• Introduction to Research Methods: This course teaches you about different analysis, measurement, design, and research strategies for business research.
• Social, Ethical, and Legal Systems: You’ll learn about how factors like social, cultural, legal, and political environments influence the behavior of an institution.
• Cognitive Psychology: This course focuses on the theory and research of subjects like language, higher mental processes, and higher memory.
• Data Visualization: In this course, you’ll study readings and lectures to learn about visualization tools and principles.
• Principles of Data Science: In this course, you’ll learn how to use data science to make business decisions.
• Scholarly Communication for Technology Leaders: You’ll learn how to communicate effectively with various audiences.
• Quantitative Research Design: This course will teach you to use research design to assess the value of quantitative analysis projects.
• Distributed Systems: You’ll study emerging technologies that are used in distributed computing.
• Data Reporting: You’ll learn about the appropriate data reporting methods used in data science fields.

These represent only a few of the courses you may encounter. Every program is different, so you may want to view the coursework of a prospective data analytics PhD program to ensure that it aligns with your interests and career goals.

Admissions Requirements

The admissions criteria for a PhD degree program are usually more stringent. Here is a list of common PhD requirements:

• Bachelor’s degree in a quantitative field
• Application form and fee
• Minimum college GPA of 3.0, on average
• GRE or GMAT scores (only some schools require them)

Some PhD programs prefer or require applicants to also hold a masters in data analytics online or on campus. In some cases, a masters in a related field, such as an on-campus or online masters in data science, is acceptable.

It’s strategic to research the specific admissions requirements of the schools you’re interested in to ensure that you can fulfill the necessary criteria.

Accreditation

Attending a school with regional accreditation can be an advantage to professionals who are seeking higher education. Accreditation status is given to institutions that have passed a series of evaluations that testify to their quality of educational offerings and services.

Employers widely recognize degrees that come from accredited postsecondary institutions. Some may even prioritize job candidates with accredited degrees. Attending an accredited school is also a common eligibility requirement for several forms of financial aid.

Financial Aid and Scholarships

Many students seek the assistance of financial aid resources to help them meet the cost of their doctoral education.

There are many forms of financial aid, including federal aid, state aid, scholarships, and grants. Federal aid is administered by the federal government, often in the form of student loans. You can file a Free Application for Federal Student Aid (FAFSA) to see if you’re eligible for this type of need-based aid.

Scholarships and grants can be offered by a number of sources, such as professional organizations, public institutions, and even your school. Some universities offer opportunities such as assistantships for doctoral students.

If you’re currently employed, you could also see if your employer offers tuition benefits for workers who are furthering their education.

What Is a Data Analytics PhD Degree?

A data analytics PhD degree is an interdisciplinary program that develops a student’s ability to process complex information through the use of systems and technologies. Students can gain an in-depth knowledge of research methods as well.

The interdisciplinary approach to these programs allows you to develop and enhance both human and technical skills. This knowledge will also be supported by further development of your analytical skills, helping you become a well-rounded professional.

If a curriculum based on technical skills, research, and presentation sounds appealing, then a data analytics doctoral degree might be a good fit for you.

What Can You Do with a PhD in Data Analytics?

A PhD in Analytics degree can open various doors within data-related fields. Many graduates go on to have careers in academia, often as educators or researchers, while others enter advanced industry roles.

Many technology companies are in need of data analysis professionals. These professionals can also be found in areas like business, healthcare, and government. Common roles include data scientist, data engineer, statistician, business analyst, and financial analyst. A PhD in this field could help you qualify for senior and leadership roles as well.

According to the Bureau of Labor Statistics, there are approximately 13,500 new job openings for data scientists each year.

How Long Does It Take to Get a PhD in Data Analytics Online?

A PhD in Data Analytics programs generally take between 3 and 5 years to complete with full-time enrollment. That said, most PhD in Analytics degrees require a dissertation, which could likely extend the duration of your study.

Dissertations often take anywhere between 1 and 2 years to complete, which is why much of the second half of your degree will be focused on its completion. Obtaining a doctorate in data analytics online might allow for a more flexible schedule, which could either accelerate or extend your program. Studying part-time, for instance, could extend the length of your program.

What’s the Difference Between Business Analytics vs. Data Analytics Doctoral Programs?

A PhD in Business Analytics and a PhD in Data Analytics share many similarities, but here are some differences between the two.

Understanding these differences may help you choose the curriculum that’s best suited to your career goals.

Is a PhD in Data Analytics Worth It?

Yes, a PhD in Data Analytics is worth it for many students. Data analysts perform vital and respected roles in various organizations and industries. Those with a PhD can have expertise that has taken years to develop, often making them valued professionals.

According to the Bureau of Labor Statistics, employment for data scientists is expected to increase by 36% over the next ten years, which is much faster than average. Having a PhD is also a necessity for most academic roles, such as postsecondary educators or researchers.

Getting Your PhD in Data Analytics Online

Data science and analytics is a viable career choice for many people, and there is a range of roles related to this field. A PhD in Data Analytics is the highest degree in the field, and it is heavily focused on numbers, statistics, and research.

Earning an online masters or doctoral degree or even an online graduate certificate in data analytics could help you advance your qualifications for positions in industry and academia. Graduates may use their expertise to help various types of organizations make data-driven decisions. Others go on to pursue teaching or research roles at the postsecondary level.

You can begin researching accredited universities today to see which data analytics PhD program best aligns with your interests and goals.

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7 Best Universities in Canada to Study Ph.D. in Data Science

A Ph.D. degree is the highest academic qualification anyone can earn. And with Harvard Business Review declaring data scientist to be the sexiest job of the 21 st century, a Ph.D. in Data Science should probably be called the sexiest university degree of the century.

Universities in Canada, like the ones in other highly advanced countries, are offering such degrees in abundance. Here, we have listed the seven best Canadian universities that offer a Ph.D. degree in Data Science.

Top Schools Offering Ph.D. in Data Science Programs in Canada

1. university of british columbia.

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Beginning its journey over a hundred years ago, the University of British Columbia (UBC) offers higher education to nearly 65,000 students, including over 17,000 international students.

It has two main campuses sited in Vancouver and Okanagan of the province of British Columbia. It is recognized as one of the top 40 universities in the world and top three universities in Canada by a number of major university rankings.

Nobel laureates like economist Robert Mundell and physicist Bertram Brockhouse are a couple of notable alumni.

UBC’s Department of Computer Science administers a Ph.D. in Computer Science program. Its research areas include a number of concepts relevant to Data Science like Data Integration, Text Mining, Web Databases, and Optimization.

It is an on-campus program that is only available in a full-time format. It takes place at the Vancouver campus and can be completed within three to five years.

2. HEC Montréal

HEC Montréal is actually not a university; rather, it is a graduate school of the Université de Montréal. Founded in 1907, the business school currently has over 14,000 students.

All of its classes take place in its two buildings in Montreal, Quebec: the Côte-Sainte-Catherine building and the Decelles building.

Despite being a French-language institution, it also offers programs that have English as the medium of instruction.

Louis R. Chênevert, the former CEO of United Technologies Corporation and president of Pratt & Whitney, graduated from this institution.

HEC Montréal offers a Ph.D. in Administration – Data Science program in collaboration with three other universities: McGill University , Concordia University , and Université du Québec à Montréal (UQAM). The partnership provides students with access to resources from all four Montreal-based institutions.

The English-language program requires 90 credits to be completed in full-time study mode. The duration of the program is typically four to five years.

3. University of Waterloo

Next on our list of universities in Canada with a Ph.D. in Data Science is the University of Waterloo . Founded in 1957, the University of Waterloo (UWaterloo) has a student population north of 40,000.

Around 20 percent of all undergraduate students and 40 percent of all graduate students have come from outside Canada. According to U.S. News and World Report Best Global Universities 2019, the university located in Waterloo is Canada’s best for Computer Science and second-best for Engineering.

It is the alma mater of Rasmus Lerdorf, the programmer who authored the first two versions of the PHP scripting language.

UWaterloo’s Ph.D. in Computer Science includes research areas like Databases, Information Retrieval, and Machine Learning that are relevant to the field of Data Science.

Students may take on this on-campus program in either full-time or part-time study mode. Admission takes place thrice a year at the start of the academic terms Fall, Winter, and Spring.

4. University of Alberta

The University of Alberta is a top-tier Canadian research university based primarily in Edmonton, the capital of Alberta. It was established in 1908, the same year that UBC came into being. The total student enrolment stands at over 40,000.

Nobel-winning physicist Richard E. Taylor and former Canadian prime minister Joe Clark graduated from this university.

Alberta’s Department of Computing Science runs a Ph.D. in Computing Science that has a wide range of research areas. A few of them, including Database Systems and Machine Learning, are related to the field of Data Science.

There’s also a Ph.D. in Statistical Machine Learning offered by the department in collaboration with the Department of Mathematical and Statistical Sciences. These are full-time programs that are ideally completed within five years.

5. Dalhousie University

Founded over two hundred years ago, Dalhousie University (Dal) is one of the oldest universities in the Great White North. Its student population is around 19,000, which includes nearly 4,000 international students representing over 115 nationalities.

Each of its three campuses is located in Halifax, the capital of Nova Scotia. The university is the alma mater of Nobel-winning astrophysicist Arthur B. McDonald, Canadian PMs R.B. Bennett, and Brian Mulroney, as well as former Xerox Corporation CEO and chairman Charles Peter McColough.

Dal offers a Ph.D. in Computer Science which allows students to specialize and develop deep expertise in Data Analytics. The Faculty of Computer Science’s Big Data Analytics & Machine Learning research cluster, which is based in the Institute for Big Data Analytics, helps students to conduct independent and original research. The Ph.D. program takes around three to four years to complete.

6. University of New Brunswick

The University of New Brunswick (UNB) started off in 1785 as Canada’s first English-language university. It has two campuses: one situated in New Brunswick’s capital Fredericton, and the other in the port city of Saint John in the same province. The university has more than 10,000 students hailing from over 100 countries. It was recognized as the country’s most entrepreneurial university in 2014 by Startup Canada.

UNB’s Faculty of Computer Science offers a Ph.D. in Computer Science one of the many research areas is Data Management, Analytics, and Mining. The program can be completed in three years. While it is ideally a full-time program, students could be permitted to take on the program on a part-time basis under certain conditions.

7. Ryerson University

Beginning its journey in 1948 as a postsecondary institute with merely 250 students, Ryerson University has grown into a full-fledged university with over 45,000 students.

It is located in Toronto, Ontario, and is the province’s most applied-to university relative to available accommodation.

Four Seasons Hotels and Resorts founder and chairman Isadore Sharp and former Maple Leaf Sports & Entertainment president and COO Tom Anselmi are a couple of its notable alumni.

Ryerson’s Faculty of Science administers a Ph.D. in Computer Science which provides students with the option of specializing in Data Science and related subjects like Machine Learning and Data Mining. The nominal duration of the thesis-based program is three years. The format for this program is full-time.

FAQS About Studying Ph.D. in Data Science

Which university in canada is best for a ph.d. in data science.

When it comes to Canada, the University of British Columbia would probably be your best pick for getting a Ph.D. in data science. Requiring nearly 5-6 years of your time, this professional degree program will challenge its students to analyze data from a wide range of domains while allowing each student to solve real-world problems created by partnered businesses.

How Long Does It Take to Earn a Ph.D. in Data Science?

If you want to graduate with a Ph.D. in data science, the time required ranges from 4 years to 9 years, depending on the person. To go down this track, you would probably apply for a Doctor of Philosophy in Computer Science and take the track of data analysis, which is a major part of data science.

If you just want to complete a Master’s degree in data science, you only need to spend 10 months of your time in this accelerated professional degree program. Analyzing data from a real-world environment, the experience that you would get from these types of programs would be second to none!

We hope that this article on universities in Canada with Ph.D. in Data Science was helpful. Make sure to also check out our  Data Science Programs for International Students  for some of the currently open data science courses around the world!

About the Author: Hyun Lee

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Topic selection & proposal development, enquire now, software implementation using matlab, questionnaire designing & data analysis, chapters writing & journal papers, 12 unexplored data analysis tools for qualitative research.

Welcome to our guide on 5 lesser-known tools for studying information in a different way – specifically designed for understanding and interpreting data in qualitative research. Data analysis tools for qualitative research are specialized instruments designed to interpret non-numerical data, offering insights into patterns, themes, and relationships.

These tools enable researchers to uncover meaning from qualitative information, enhancing the depth and understanding of complex phenomena in fields such as social sciences, psychology, and humanities.

In the world of research, there are tools tailored for qualitative data analysis that can reveal hidden insights. This blog explores these tools, showcasing their unique features and advantages compared to the more commonly used quantitative analysis tools.

Whether you’re a seasoned researcher or just starting out, we aim to make these tools accessible and highlight how they can add depth and accuracy to your analysis. Join us as we uncover these innovative approaches, offering practical solutions to enhance your experience with qualitative research.

Tool 1:MAXQDA Analytics Pro

MAXQDA Analytics Pro emerges as a game-changing tool for qualitative data analysis, offering a seamless experience that goes beyond the capabilities of traditional quantitative tools.

Here’s how MAXQDA stands out in the world of qualitative research:

Advanced Coding and Text Analysis: MAXQDA empowers researchers with advanced coding features and text analysis tools, enabling the exploration of qualitative data with unprecedented depth. Its intuitive interface allows for efficient categorization and interpretation of textual information.

Intuitive Interface for Effortless Exploration: The user-friendly design of MAXQDA makes it accessible for researchers of all levels. This tool streamlines the process of exploring qualitative data, facilitating a more efficient and insightful analysis compared to traditional quantitative tools.

Uncovering Hidden Narratives: MAXQDA excels in revealing hidden narratives within qualitative data, allowing researchers to identify patterns, themes, and relationships that might be overlooked by conventional quantitative approaches. This capability adds a valuable layer to the analysis of complex phenomena.

In the landscape of qualitative data analysis tools, MAXQDA Analytics Pro is a valuable asset, providing researchers with a unique set of features that enhance the depth and precision of their analysis. Its contribution extends beyond the confines of quantitative analysis tools, making it an indispensable tool for those seeking innovative approaches to qualitative research.

Tool 2: Quirkos

Quirkos , positioned as data analysis software, shines as a transformative tool within the world of qualitative research.

Here’s why Quirkos is considered among the best for quality data analysis: Visual Approach for Enhanced Understanding: Quirkos introduces a visual approach, setting it apart from conventional analysis software. This unique feature aids researchers in easily grasping and interpreting qualitative data, promoting a more comprehensive understanding of complex information.

User-Friendly Interface: One of Quirkos’ standout features is its user-friendly interface. This makes it accessible to researchers of various skill levels, ensuring that the tool’s benefits are not limited to experienced users. Its simplicity adds to the appeal for those seeking the best quality data analysis software.

Effortless Pattern Identification: Quirkos simplifies the process of identifying patterns within qualitative data. This capability is crucial for researchers aiming to conduct in-depth analysis efficiently.

The tool’s intuitive design fosters a seamless exploration of data, making it an indispensable asset in the world of analysis software. Quirkos, recognized among the best quality data analysis software, offers a visual and user-friendly approach to qualitative research. Its ability to facilitate effortless pattern identification positions it as a valuable asset for researchers seeking optimal outcomes in their data analysis endeavors.

Tool 3: Provalis Research WordStat

Provalis Research WordStat stands out as a powerful tool within the world of qualitative data analysis tools, offering unique advantages for researchers engaged in qualitative analysis:

WordStat excels in text mining, providing researchers with a robust platform to delve into vast amounts of textual data. This capability enhances the depth of qualitative analysis, setting it apart in the landscape of tools for qualitative research.

Specializing in content analysis, WordStat facilitates the systematic examination of textual information. Researchers can uncover themes, trends, and patterns within qualitative data, contributing to a more comprehensive understanding of complex phenomena.

WordStat seamlessly integrates with qualitative research methodologies, providing a bridge between quantitative and qualitative analysis. This integration allows researchers to harness the strengths of both approaches, expanding the possibilities for nuanced insights.

In the domain of tools for qualitative research, Provalis Research WordStat emerges as a valuable asset. Its text mining capabilities, content analysis expertise, and integration with qualitative research methodologies collectively contribute to elevating the qualitative analysis experience for researchers.

Tool 4: ATLAS.ti

ATLAS.ti proves to be a cornerstone in the world of qualitative data analysis tools, offering distinctive advantages that enhance the qualitative analysis process:

Multi-Faceted Data Exploration: ATLAS.ti facilitates in-depth exploration of textual, graphical, and multimedia data. This versatility enables researchers to engage with diverse types of qualitative information, broadening the scope of analysis beyond traditional boundaries.

Collaboration and Project Management: The tool excels in fostering collaboration among researchers and project management. This collaborative aspect sets ATLAS.ti apart, making it a comprehensive solution for teams engaged in qualitative research endeavors.

User-Friendly Interface: ATLAS.ti provides a user-friendly interface, ensuring accessibility for researchers of various skill levels. This simplicity in navigation enhances the overall qualitative analysis experience, making it an effective tool for both seasoned researchers and those new to data analysis tools. In the landscape of tools for qualitative research, ATLAS.ti emerges as a valuable ally. Its multi-faceted data exploration, collaboration features, and user-friendly interface collectively contribute to enriching the qualitative analysis journey for researchers seeking a comprehensive and efficient solution.

Tool 5: NVivo Transcription

NVivo Transcription emerges as a valuable asset in the world of data analysis tools, seamlessly integrating transcription services with qualitative research methodologies:

Efficient Transcription Services: NVivo Transcription offers efficient and accurate transcription services, streamlining the process of converting spoken words into written text. This feature is essential for researchers engaged in qualitative analysis, ensuring a solid foundation for subsequent exploration.

Integration with NVivo Software: The tool seamlessly integrates with NVivo software, creating a synergistic relationship between transcription and qualitative analysis. Researchers benefit from a unified platform that simplifies the organization and analysis of qualitative data, enhancing the overall research workflow.

Comprehensive Qualitative Analysis: NVivo Transcription contributes to comprehensive qualitative analysis by providing a robust foundation for understanding and interpreting audio and video data. Researchers can uncover valuable insights within the transcribed content, enriching the qualitative analysis process.

In the landscape of tools for qualitative research, NVivo Transcription plays a crucial role in bridging the gap between transcription services and qualitative analysis. Its efficient transcription capabilities, integration with NVivo software, and support for comprehensive qualitative analysis make it a valuable tool for researchers seeking a streamlined and effective approach to handling qualitative data.

Tool 6: Dedoose

Web-Based Accessibility: Dedoose’s online platform allows PhD researchers to conduct qualitative data analysis from anywhere, promoting flexibility and collaboration.

Mixed-Methods Support: Dedoose accommodates mixed-methods research, enabling the integration of both quantitative and qualitative data for a comprehensive analysis.

Multi-Media Compatibility: The tool supports various data formats, including text, audio, and video, facilitating the analysis of diverse qualitative data types.

Collaborative Features: Dedoose fosters collaboration among researchers, providing tools for shared coding, annotation, and exploration of qualitative data.

Organized Data Management: PhD researchers benefit from Dedoose’s organizational features, streamlining the coding and retrieval of data for a more efficient analysis process.

Tool 7: HyperRESEARCH

HyperRESEARCH caters to various qualitative research methods, including content analysis and grounded theory, offering a flexible platform for PhD researchers.

The software simplifies the coding and retrieval of data, aiding researchers in organizing and analyzing qualitative information systematically.

HyperRESEARCH allows for detailed annotation of text, enhancing the depth of qualitative analysis and providing a comprehensive understanding of the data.

The tool provides features for visualizing relationships within data, aiding researchers in uncovering patterns and connections in qualitative content.

HyperRESEARCH facilitates collaborative research efforts, promoting teamwork and shared insights among PhD researchers.

Tool 8: MAXQDA Analytics Plus

Advanced Collaboration:  

MAXQDA Analytics Plus enhances collaboration for PhD researchers with teamwork support, enabling multiple researchers to work seamlessly on qualitative data analysis.

Extended Visualization Tools:  

The software offers advanced data visualization features, allowing researchers to create visual representations of qualitative data patterns for a more comprehensive understanding.

Efficient Workflow:  

MAXQDA Analytics Plus streamlines the qualitative analysis workflow, providing tools that facilitate efficient coding, categorization, and interpretation of complex textual information.

Deeper Insight Integration:  

Building upon MAXQDA Analytics Pro, MAXQDA Analytics Plus integrates additional features for a more nuanced qualitative analysis, empowering PhD researchers to gain deeper insights into their research data.

User-Friendly Interface:  

The tool maintains a user-friendly interface, ensuring accessibility for researchers of various skill levels, contributing to an effective and efficient data analysis experience.

Tool 9: QDA Miner

Versatile Data Analysis: QDA Miner supports a wide range of qualitative research methodologies, accommodating diverse data types, including text, images, and multimedia, catering to the varied needs of PhD researchers.

Coding and Annotation Tools: The software provides robust coding and annotation features, facilitating a systematic organization and analysis of qualitative data for in-depth exploration.

Visual Data Exploration: QDA Miner includes visualization tools for researchers to analyze data patterns visually, aiding in the identification of themes and relationships within qualitative content.

User-Friendly Interface: With a user-friendly interface, QDA Miner ensures accessibility for researchers at different skill levels, contributing to a seamless and efficient qualitative data analysis experience.

Comprehensive Analysis Support: QDA Miner’s features contribute to a comprehensive analysis, offering PhD researchers a tool that integrates seamlessly into their qualitative research endeavors.

Tool 10: NVivo

NVivo supports diverse qualitative research methodologies, allowing PhD researchers to analyze text, images, audio, and video data for a comprehensive understanding.

The software aids researchers in organizing and categorizing qualitative data systematically, streamlining the coding and analysis process.

NVivo seamlessly integrates with various data formats, providing a unified platform for transcription services and qualitative analysis, simplifying the overall research workflow.

NVivo offers tools for visual representation, enabling researchers to create visual models that enhance the interpretation of qualitative data patterns and relationships.

NVivo Transcription integration ensures efficient handling of audio and video data, offering PhD researchers a comprehensive solution for qualitative data analysis.

Tool 11: Weft QDA

Open-Source Affordability: Weft QDA’s open-source nature makes it an affordable option for PhD researchers on a budget, providing cost-effective access to qualitative data analysis tools.

Simplicity for Beginners: With a straightforward interface, Weft QDA is user-friendly and ideal for researchers new to qualitative data analysis, offering basic coding and text analysis features.

Ease of Use: The tool simplifies the process of coding and analyzing qualitative data, making it accessible to researchers of varying skill levels and ensuring a smooth and efficient analysis experience.

Entry-Level Solution: Weft QDA serves as a suitable entry-level option, introducing PhD researchers to the fundamentals of qualitative data analysis without overwhelming complexity.

Basic Coding Features: While being simple, Weft QDA provides essential coding features, enabling researchers to organize and explore qualitative data effectively.

Tool 12: Transana

Transana specializes in the analysis of audio and video data, making it a valuable tool for PhD researchers engaged in qualitative studies with rich multimedia content.

The software streamlines the transcription process, aiding researchers in converting spoken words into written text, providing a foundation for subsequent qualitative analysis.

Transana allows for in-depth exploration of multimedia data, facilitating coding and analysis of visual and auditory aspects crucial to certain qualitative research projects.

With tools for transcribing and coding, Transana assists PhD researchers in organizing and categorizing qualitative data, promoting a structured and systematic approach to analysis.

Researchers benefit from Transana’s capabilities to uncover valuable insights within transcribed content, enriching the qualitative analysis process with a focus on visual and auditory dimensions.

Final Thoughts

In wrapping up our journey through 5 lesser-known data analysis tools for qualitative research, it’s clear these tools bring a breath of fresh air to the world of analysis. MAXQDA Analytics Pro, Quirkos, Provalis Research WordStat, ATLAS.ti, and NVivo Transcription each offer something unique, steering away from the usual quantitative analysis tools.

They go beyond, with MAXQDA’s advanced coding, Quirkos’ visual approach, WordStat’s text mining, ATLAS.ti’s multi-faceted data exploration, and NVivo Transcription’s seamless integration.

These tools aren’t just alternatives; they are untapped resources for qualitative research. As we bid adieu to the traditional quantitative tools, these unexplored gems beckon researchers to a world where hidden narratives and patterns are waiting to be discovered.

They don’t just add to the toolbox; they redefine how we approach and understand complex phenomena. In a world where research is evolving rapidly, these tools for qualitative research stand out as beacons of innovation and efficiency.

PhDGuidance is a website that provides customized solutions for PhD researchers in the field of qualitative analysis. They offer comprehensive guidance for research topics, thesis writing, and publishing. Their team of expert consultants helps researchers conduct copious research in areas such as social sciences, humanities, and more, aiming to provide a comprehensive understanding of the research problem.

PhDGuidance offers qualitative data analysis services to help researchers study the behavior of participants and observe them to analyze for the research work. They provide both manual thematic analysis and using NVivo for data collection. They also offer customized solutions for research design, data collection, literature review, language correction, analytical tools, and techniques for both qualitative and quantitative research projects.

Frequently Asked Questions

• What is the best free qualitative data analysis software?

When it comes to free qualitative data analysis software, one standout option is RQDA. RQDA, an open-source tool, provides a user-friendly platform for coding and analyzing textual data. Its compatibility with R, a statistical computing language, adds a layer of flexibility for those familiar with programming. Another notable mention is QDA Miner Lite, offering basic qualitative analysis features at no cost. While these free tools may not match the advanced capabilities of premium software, they serve as excellent starting points for individuals or small projects with budget constraints.

2. Which software is used to Analyse qualitative data?

For a more comprehensive qualitative data analysis experience, many researchers turn to premium tools like NVivo, MAXQDA, or ATLAS.ti. NVivo, in particular, stands out due to its user-friendly interface, robust coding capabilities, and integration with various data types, including audio and visual content. MAXQDA and ATLAS.ti also offer advanced features for qualitative data analysis, providing researchers with tools to explore, code, and interpret complex qualitative information effectively.

3. How can I Analyse my qualitative data?

Analyzing qualitative data involves a systematic approach to make sense of textual, visual, or audio information. Here’s a general guide:

Data Familiarization: Understand the context and content of your data through thorough reading or viewing.

Open Coding: Begin with open coding, identifying and labeling key concepts without preconceived categories.

Axial Coding: Organize codes into broader categories, establishing connections and relationships between them.

Selective Coding: Focus on the most significant codes, creating a narrative that tells the story of your data.

Constant Comparison: Continuously compare new data with existing codes to refine categories and ensure consistency.

Use of Software: Employ qualitative data analysis software, such as NVivo or MAXQDA, to facilitate coding, organization, and interpretation.

4. Is it worth using NVivo for qualitative data analysis?

The use of NVivo for qualitative data analysis depends on the specific needs of the researcher and the scale of the project. NVivo is worth considering for its versatility, user-friendly interface, and ability to handle diverse data types. It streamlines the coding process, facilitates collaboration, and offers in-depth analytical tools. However, its cost may be a consideration for individuals or smaller research projects. Researchers with complex data sets, especially those involving multimedia content, may find NVivo’s advanced features justify the investment.

5. What are the tools used in quantitative data analysis?

Quantitative data analysis relies on tools specifically designed to handle numerical data. Some widely used tools include:

SPSS (Statistical Package for the Social Sciences): A statistical software suite that facilitates data analysis through descriptive statistics, regression analysis, and more. Excel: Widely used for basic quantitative analysis, offering functions for calculations, charts, and statistical analysis.

R and RStudio: An open-source programming language and integrated development environment used for statistical computing and graphics.

Python with Pandas and NumPy: Python is a versatile programming language, and Pandas and NumPy are libraries that provide powerful tools for data manipulation and analysis.

STATA: A software suite for data management and statistical analysis, widely used in various fields.

Hence, the choice of qualitative data analysis software depends on factors like project scale, budget, and specific requirements. Free tools like RQDA and QDA Miner Lite offer viable options for smaller projects, while premium software such as NVivo, MAXQDA, and ATLAS.ti provide advanced features for more extensive research endeavors. When it comes to quantitative data analysis, SPSS, Excel, R, Python, and STATA are among the widely used tools, each offering unique strengths for numerical data interpretation. Ultimately, the selection should align with the researcher’s goals and the nature of the data being analyzed.

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Check Out These 5 Indian Institutes Offering A PhD In Data Science

• Published on February 13, 2019
• by Disha Misal

Data Science has become popular in India. Many people are shifting their careers from different fields to data science and AI. A PhD in data science is very demanding and is considered extremely prestigious. India is realising its importance in industries and now has several institutes offering a PhD in data science.

Table of contents

1. indian statistical institute (isi) :, 2. iit gandhinagar :, 3. iisc bangalore :, 4. iit delhi :, 5. bits pilani :.

Here are some institutes that currently have their applications open for a PhD in India.

The PhD program at ISI provides the students with an exceptional academic environment through a variety of educational and research opportunities. The internationally recognized faculty members actively work in several exciting research areas. PhD students are expected to publish papers in conferences and journals of international repute and are encouraged to collaborate with researchers all over the world, travel to conferences and training programs, and have internships with leading government and industry research labs. ISI has a unique opportunity for outstanding students to apply to the PhD program directly after the completion of their Bachelor’s degree. It also offers a unique flexibility in accepting students without the need for an undergraduate/postgraduate degree in computer science.

The online application has been started on 5th of February and the deadline is on 12th of March. They have their own admission test which will be on 12th of May. You can find the exam and admission details here .

IIT Gandhinagar, under its computer science engineering department is offering specialisation in data science PhD. Specialisations are open in the area of Data science, machine learning, NLP , game theory, data mining. Admission is offered on the basis of an interview, which is supplemented by a written test if necessary. The Institute invites a limited number of candidates for a written test and interview based on the academic records, statement of purpose. The final selection will be mainly based on academic credentials, written test and/or interview.

Application Deadline for a PhD at IIT-Gandhinagar this year is 12th February. Written Tests and/or Interviews will be held sometime around 15-17th March. Here are the details.

IISc has a department dedicated to data sciences called the Department of Computational and Data Sciences (CDS) that offers research-based degree programs. The research programs are M.Tech. (Research) and Ph.D. The Ph.D program at CDS is the flagship program of the department, and it is these doctoral programmes that have helped IISc secure the rank as top university and top academic institution in India. The Ph.D. students contribute the most to the success of the Institute.

PhD admission involves a written test followed by and interview of the shortlisted candidates. The applications start in October every year. Here are the details.

IIT Delhi has a research group called Data Analytics and Intelligence Research ( DAIR ), under the department of Computer Science Engineering, which deals with data. It is focused on combining and integrating various fields of data sciences such as machine learning, data management, and data mining towards the goal of building intelligent software systems. They are involved in NLP, statistical relational learning, social network analytics and crowdsourcing. They have various data science and ML specialisations like Algorithms and Complexity Theory, AI and ML, Databases and Data Analytics, Architecture and Embedded Systems, Graphic and Vision, and many others.

Here are the details.

BITS Pilani Goa campus provides a PhD course with a specialised subject as data science. They select candidates based on a test followed by an interview. They also have a part-time PhD program for people working in reputed research organisations, academic institutes and industries, situated close to the vicinity of one of the campuses of BITS Pilani.

The registrations generally start in the month of December every year. You can find the details here .

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• applications of data mining , data mining , data mining tools , entrance exams , IISc , interview

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11 Tips For Writing a Dissertation Data Analysis

Since the evolution of the fourth industrial revolution – the Digital World; lots of data have surrounded us. There are terabytes of data around us or in data centers that need to be processed and used. The data needs to be appropriately analyzed to process it, and Dissertation data analysis forms its basis. If data analysis is valid and free from errors, the research outcomes will be reliable and lead to a successful dissertation. 

So, in today’s topic, we will cover the need to analyze data, dissertation data analysis, and mainly the tips for writing an outstanding data analysis dissertation. If you are a doctoral student and plan to perform dissertation data analysis on your data, make sure that you give this article a thorough read for the best tips!

What is Data Analysis in Dissertation?

Even f you have the data collected and compiled in the form of facts and figures, it is not enough for proving your research outcomes. There is still a need to apply dissertation data analysis on your data; to use it in the dissertation. It provides scientific support to the thesis and conclusion of the research.

Data Analysis Tools

There are plenty of indicative tests used to analyze data and infer relevant results for the discussion part. Following are some tests  used to perform analysis of data leading to a scientific conclusion:

11 Most Useful Tips for Dissertation Data Analysis

Doctoral students need to perform dissertation data analysis and then dissertation to receive their degree. Many Ph.D. students find it hard to do dissertation data analysis because they are not trained in it.

1. Dissertation Data Analysis Services

The first tip applies to those students who can afford to look for help with their dissertation data analysis work. It’s a viable option, and it can help with time management and with building the other elements of the dissertation with much detail.

Dissertation Analysis services are professional services that help doctoral students with all the basics of their dissertation work, from planning, research and clarification, methodology, dissertation data analysis and review, literature review, and final powerpoint presentation.

One great reference for dissertation data analysis professional services is Statistics Solutions , they’ve been around for over 22 years helping students succeed in their dissertation work. You can find the link to their website here .

Following are some helpful tips for writing a splendid dissertation data analysis:

2. Relevance of Collected Data

It involves  data collection  of your related topic for research. Carefully analyze the data that tends to be suitable for your analysis. Do not just go with irrelevant data leading to complications in the results. Your data must be relevant and fit with your objectives. You must be aware of how the data is going to help in analysis. 

3. Data Analysis

For analysis, it is crucial to use such methods that fit best with the types of data collected and the research objectives. Elaborate on these methods and the ones that justify your data collection methods thoroughly. Make sure to make the reader believe that you did not choose your method randomly. Instead, you arrived at it after critical analysis and prolonged research.

Data analysis involves two approaches –  Qualitative Data Analysis and Quantitative Data Analysis.   Qualitative data analysis  comprises research through experiments, focus groups, and interviews. This approach helps to achieve the objectives by identifying and analyzing common patterns obtained from responses. 

The overall objective of data analysis is to detect patterns and inclinations in data and then present the outcomes implicitly.  It helps in providing a solid foundation for critical conclusions and assisting the researcher to complete the dissertation proposal. 

4. Qualitative Data Analysis

Qualitative data refers to data that does not involve numbers. You are required to carry out an analysis of the data collected through experiments, focus groups, and interviews. This can be a time-taking process because it requires iterative examination and sometimes demanding the application of hermeneutics. Note that using qualitative technique doesn’t only mean generating good outcomes but to unveil more profound knowledge that can be transferrable.

Presenting qualitative data analysis in a dissertation  can also be a challenging task. It contains longer and more detailed responses. Placing such comprehensive data coherently in one chapter of the dissertation can be difficult due to two reasons. Firstly, we cannot figure out clearly which data to include and which one to exclude. Secondly, unlike quantitative data, it becomes problematic to present data in figures and tables. Making information condensed into a visual representation is not possible. As a writer, it is of essence to address both of these challenges.

This method involves analyzing qualitative data based on an argument that a researcher already defines. It’s a comparatively easy approach to analyze data. It is suitable for the researcher with a fair idea about the responses they are likely to receive from the questionnaires.

In this method, the researcher analyzes the data not based on any predefined rules. It is a time-taking process used by students who have very little knowledge of the research phenomenon.

5. Quantitative Data Analysis

The Presentation of quantitative data  depends on the domain to which it is being presented. It is beneficial to consider your audience while writing your findings. Quantitative data for  hard sciences  might require numeric inputs and statistics. As for  natural sciences , such comprehensive analysis is not required.

Following are some of the methods used to perform quantitative data analysis. 

6. Data Presentation Tools

Since large volumes of data need to be represented, it becomes a difficult task to present such an amount of data in coherent ways. To resolve this issue, consider all the available choices you have, such as tables, charts, diagrams, and graphs. 

7. Include Appendix or Addendum

After presenting a large amount of data, your dissertation analysis part might get messy and look disorganized. Also, you would not be cutting down or excluding the data you spent days and months collecting. To avoid this, you should include an appendix part. 

The data you find hard to arrange within the text, include that in the  appendix part of a dissertation . And place questionnaires, copies of focus groups and interviews, and data sheets in the appendix. On the other hand, one must put the statistical analysis and sayings quoted by interviewees within the dissertation. 

8. Thoroughness of Data

Thoroughly demonstrate the ideas and critically analyze each perspective taking care of the points where errors can occur. Always make sure to discuss the anomalies and strengths of your data to add credibility to your research.

9. Discussing Data

Discussion of data involves elaborating the dimensions to classify patterns, themes, and trends in presented data. In addition, to balancing, also take theoretical interpretations into account. Discuss the reliability of your data by assessing their effect and significance. Do not hide the anomalies. While using interviews to discuss the data, make sure you use relevant quotes to develop a strong rationale. 

10. Findings and Results

Findings refer to the facts derived after the analysis of collected data. These outcomes should be stated; clearly, their statements should tightly support your objective and provide logical reasoning and scientific backing to your point. This part comprises of majority part of the dissertation. 

11. Connection with Literature Review

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Wrapping up.

Writing data analysis in the dissertation involves dedication, and its implementations demand sound knowledge and proper planning. Choosing your topic, gathering relevant data, analyzing it, presenting your data and findings correctly, discussing the results, connecting with the literature and conclusions are milestones in it. Among these checkpoints, the Data analysis stage is most important and requires a lot of keenness.

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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PhD programme

A PhD in statistics gives you a very strong foundation in the future labour market, where there is more demand for competency in data analysis.

The PhD programme in statistics at Uppsala University provide both a broadening and deepening in statistics, providing skills in modern statistics methods and research areas.

Within in the PhD programme in statistics, one should also apply statistical methods on practical applications, thereby increasing the proficiency with which to analyse data.

Main research areas

We mainly supervise our PhD students within our four main research areas: high dimensional data, casual inference, structural equation modeling and time series econometrics.

High dimensional data

Collecting large amount of data is today a norm rather than an exception – partly dictated by the complexity of modern problems, and partly due to its convenient availability. The field of multivariate statistics has correspondingly grown to cope with the questions associated with such data. Originally motivated by the field of genetics, the investigation of high-dimensional data has now crept into as diverse areas of applications as engineering, psychology and behavioural sciences, biological sciences, and even agriculture and finance. To address the modern challenges, the statistical methodology is currently in the process of unprecedented development on all aspects: theory, computation and application. The field is set to grow unabatedly, and has secure future prospects.

Responsible researcher: Rauf Ahmad

Causal inference

Causal inference aims to deepen the understanding of social phenomena, or of analyses of efficiencies of different treatments (such as, medicinal, work-life or environmental treatments). Statistics cannot by itself create knowledge of various phenomena, but is rather used to test theories under various assumptions. Thematic theory, together with statistical theory and knowledge of how data is collected, jointly form the causal analysis. For that reason, it is necessary to understand thematic questions, and that in the development of new methods jointly consider how data together with statistical theory can improve analysis of causal questions, such as testing of theories as well as pure effect evaluations.

Currently, we are working with researchers in medicine, economics, psychology and engineering. Some of our thematic work include: (i) test of theories for how mass media affect voters' ability to hold elected officials accountable, (ii) analysis of the effect of family friendly workplaces on wages and income for men and women, (iii) analysis of the effect of air pollution on childrens' health, (iv) test of gender differences in preferences and (v) analysis of electricity consumption and changes in electricity tariffs and consumer information related to energy savings. Methodologically, we have made contributions to, among others, the design of randomized experiments and identification of causal effects using observational data, and register data in particular. Examples of problems we have investigated concern situations when the timing of a treatment is a choice (as opposed to a randomized experiment when the time for intervention is the same for all treated and untreated individuals) and where there might exist measurement errors, both in control variables and in the timing of treatment.

Responsible researchers: Per Johansson and Ingeborg Waernbaum

Structural equation modeling

Structural equation modeling (SEM) is a multivariate statistical analysis technique that simultaneously unites Factor Analysis and Multiple Regression Analysis. It analyses the causal relationships among observed variables and latent constructs, including linear and nonlinear effects. SEM includes two basic types of models. The measurement model represents the theory that specifies how a set of observed variables measure the latent constructs. The structural model represents the theory that shows how latent constructs are causally related to each other.

SEM can apply to various data types, cross-sectional data, longitudinal data, time-series data, or multilevel data. For example, the cross-sectional models help us to assess causal and mediation hypotheses; Latent Growth Curve Models often apply to analyse potential changes in the latent construct of interest over time; Item Response Theory Models usually analyse the patterns of individual behaviours and questionnaire responses, and Multilevel Models can assess the cause of variations between different data levels. SEM has been widely applied in social sciences and spread to other natural sciences in recent decades, e.g., information science and medical research.

The Department of Statistics in Uppsala has a long tradition of structural equation modeling, and it is known as the birthplace of SEM. Professor Emeritus Karl G. Jöreskog is the pioneer in SEM, and the LISREL (linear structural relations) program (Jöreskog and Sörbom) was the first software for analysis of Structural Equation Models. Nowadays, Professor Fan Wallentin and Associate Professor Shaobo Jin with colleagues carry on this rich tradition and actively contribute to the field.

Responsible researcher: Fan Yang Wallentin

Time series econometrics

The Department of Statistics has a long tradition of research within times series econometrics. The tradition started in 1942 when Herman Wold become professor at the department. Econometrics is the field concerning applications of statistical methods on problems within economics, and the methods are developed for applied problems in economics. Time series econometric deal with economic data sorted over time, and the aim is to analyse associations between different economic variables and their development over time. For instance, the government need to know how the gross domestic product (GDP), inflation and unemployment will develop in order to make the national budget.

One research area is to improve forecast to just these variables (GDP, inflation and unemployment). We are living in a ever-changing world affected by various decision. These decisions result in associations between economic variables changing over time. Another research area is how to model structural changes over time using non-linear models. With such models, it is possible to get increased understanding of various economic phenomena.

Responsible researcher: Johan Lyhagen

Programme structure

The PhD programme consists of four years of full-time studies, and consists of 90 credits of coursework and a thesis worth 150 credits. Of the 90 coursework credits, 34.5 consists of mandatory courses, with the rest being elective courses.

The mandatory courses are: Inference theory (15 credits), Asymptotic theory (7.5 credits), Philosophy of science (5 credits), Scientific communication (5 credits) and Research ethics (2 credits), or equivalent courses. For PhD students who does not have prerequisite knowledge in probability theory, the course Probability theory (7.5 credits) is also mandatory.

The remaining courses are elective courses but should be within one or more of the areas of statistical methodology and/or applications. Courses both within and outside for the thesis area should be included, with at least 15 credits for each.

Qualifications

Basic and specific qualifications to be admitted to the PhD programme is described in the general study plan. Aside from basic qualifications for a PhD programme, the applicant must have passed results on courses of 90 credits in statistics, of which at least 60 credits at advanced level.

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UVA Doctoral Students Showcase Potential of Advanced Data Science Research

Doctoral students from the University of Virginia’s School of Data Science kicked off the new academic year with a research showcase, highlighting the broad range of areas that advanced studies in data science can help illuminate. 

The event also served as a celebration of the doctoral program and what it means to the mission of the School of Data Science. 

"The Ph.D. program is kind of the perfect mixing of our research endeavors and our educational endeavors," said Thomas Stewart , an associate professor of data science and Ph.D. program director, in opening remarks.

Don Brown , senior associate dean for research and the Quantitative Foundation Distinguished Professor in Data Science, noted that the event was "arguably the best part of the summer," as faculty, staff, and students are given the opportunity to see first-hand the high-level research that Ph.D. students at the School of Data Science had been pursuing in recent months.

Brown also urged audience members as they listened to the oral presentations from second-year students and viewed the research posters from third-year students to ask challenging questions, saying that was "the biggest gift you can give the person" who was presenting.

Oral presentations were delivered by 13 second-year Ph.D. students, whose research covered everything from health care chatbots to deep dives into a wide range of methodological techniques, as well as issues pertaining to large language models. 

Later, 13 third-year students presented research posters that addressed critical issues such as health care, K-12 education, the environment, and many others. 

Following the presentations, an awards ceremony was held, one that both recognized the research that had just been discussed and that celebrated the achievements of the School of Data Science doctoral program — its students, faculty, and staff — over the previous year. Stewart also highlighted a sampling of recent research publications from the School's doctoral students. 

As the day wound down, Jeffrey Blume , Quantitative Foundation Associate Dean for Academic and Faculty Affairs in Data Science, urged the newest group of School of Data Science Ph.D. students in attendance to take inspiration from what they had just seen.

"For those students who are new and who are here, you get a sense of what people are doing their first couple of years, so it's something to look forward to. We look forward to seeing your names up here on publications and listening to your work," he said. 

The 2024-25 academic year will mark the third full year of UVA’s data science doctoral program , which launched in fall 2022. And, for the first time, students in all academic programs will be able to take classes and collaborate with faculty and classmates at the new home of the School of Data Science, which held its grand opening in April .  

Awards 

Best Oral Presentations

• Ethan Nelson , who discussed his work that examined whether signal neural networks can evolve to capture how signals travel between neurons over time
• Ahson Saiyed , who presented his enhanced benchmark, called TAXI 2.0, for evaluating the capacity of knowledge editing methods to leverage batched property edits to make consistent categorical knowledge edits in large language models

Best Research Poster

• Karolina Naranjo-Velasco , who presented her work on facilitating data-drive approaches to legal text analysis of documents from the Colombian Constitutional Court
• Jason Wang , who presented his work assessing computer vision-based worker poster analysis methods

Leadership and Service Award

• Beau LeBlond , a third-year Ph.D. student 

Outstanding Teaching Award

• Jonathan Kropko , a Quantitative Foundation Associate Professor of Data Science

Outstanding Mentoring Award

• Stephen Baek , a Quantitative Foundation Associate Professor of Data Science

Outstanding Student Support Award

• Kylen Baskerville , program manager for the School of Data Science

Full list of second-year oral presentations

• Supervising faculty: Stephen Baek
• Supervising faculty: Don Brown
• Supervising faculty: Heman Shakeri
• Supervising faculty: Jeffrey Blume
• Supervising faculty: John Darrell Van Horn
• Supervising faculty: Sheng Li
• Supervising faculty: Teague Henry
• Supervising faculty: Tom Hartvigsen
• Supervising faculty: Sheng Li and Tom Hartvigsen
• Supervising faculty: Alex Gates

Full list of third-year poster presentations

• Zhanwen Chen : Video Understanding through Video-to-Text Representation Learning
• Supervising faculty: Thomas Stewart
• Supervising faculty: Jonathan Kropko
• Supervising faculty: William Basener
• Supervising faculty: Jess Reia and Jeffrey Blume
• Supervising faculty: William Basener  

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Purdue’s online data science master’s addresses burgeoning demand for trained data scientists

The interdisciplinary degree is accessible for working professionals from both technical and nontechnical backgrounds

WEST LAFAYETTE, Ind. — Data scientists who can make sense of today’s epic floods of data to generate actionable insights and communicate them to a variety of audiences are in demand in almost any field, from retail business and industry to health care, government, education, and more.

The U.S. Bureau of Labor Statistics estimates that jobs for data scientists will grow 36% by 2031. Nationally, there were nearly 125,000 data scientist jobs added from 2013-2023. Yet many of those jobs — with many more openings coming — went unfilled for a lack of trained data scientists. The bottom line: Nearly every industry today requires data scientists, and the number of these positions is expected to grow.

Purdue University’s new 100% online Master of Science in data science degree addresses the need and the high demand for a trained data science workforce that can harness the power of data to drive innovation, efficiency and competitiveness. The interdisciplinary master’s program is designed for working professionals with a technical background but includes a pathway to entry for professionals from nontechnical fields.

“This data science master’s program is specifically designed for online delivery and optimal online learning, making it accessible to professionals around the world,” said Dimitrios Peroulis, Purdue senior vice president for partnerships and online. “The interdisciplinary curriculum is diverse, customizable to a student’s needs and tailored for practical application immediately.”

Purdue’s online master’s in data science features core courses covering foundations of data science, machine learning and data mining, big data technologies and tools, data analysis, and data visualization and communication.

Students do a capstone project pairing them with an industry mentor and a collaborative team to manage a data science project from inception to completion. That includes developing project timelines, allocating resources and adapting strategies based on the project’s evolution. The capstone, modeled after curriculum from The Data Mine , Purdue’s award-winning data science learning community, is an opportunity to apply knowledge acquired throughout the master’s program to solve complex, real-world problems.

The online master’s program also features the opportunity to earn industry-aligned certificates along the way to earning a master’s degree. Options include education, leadership, and policy; smart mobility and smart transportation; data science in finance; spatial data science; geospatial information science; managing information technology projects; IT business analysis; and applied statistics.

The program was developed by an interdisciplinary cohort of expert faculty from Purdue’s flagship campus, including the colleges of Agriculture, Education, Engineering, Health and Human Sciences, Liberal Arts, Pharmacy, Science, and Veterinary Medicine, along with the Mitch Daniels School of Business, the Purdue Polytechnic Institute, the Purdue Libraries, and the Office of the Vice Provost for Graduate Students and Postdoctoral Scholars.

“Purdue’s new online MS in data science program leverages the real-world experience of faculty working across several distinct disciplines,” said Timothy Keaton, assistant professor of practice in Purdue’s Department of Statistics, who was involved in developing the new degree. “This cooperation between experts in the application of data science in diverse fields provides a great opportunity to create engaging and meaningful coursework that incorporates many different potential areas of interest for our students.”

Students will develop expertise in programming languages, gaining the ability to design and implement data-driven solutions; learn to apply advanced technologies, including cloud computing and big data frameworks, to effectively handle and process large-scale datasets; gain a deep understanding of machine learning algorithms and models, applying them to real-world scenarios; and become proficient in collecting, cleaning, and analyzing diverse datasets.

The curriculum also is designed to teach learners data visualization and communication methods for creating compelling visual representations of complex data to effectively convey insights, along with the application of storytelling techniques to communicate findings clearly to both technical and nontechnical audiences. The program covers adherence to ethical standards in data science, privacy, transparency and fairness as well.

The program draws on Purdue’s expertise in myriad aspects of data science. Known for its emphasis on practical programs with proven value, Purdue has been rated among the Top 10 Most Innovative Schools for six years running by U.S. News & World Report and is the No. 8 public university in the U.S. according to the latest QS World University Rankings.

“The breadth and depth of topics that data science encompasses necessitate graduate programs that incorporate expertise from a variety of disciplines and then integrate this into a curriculum to meet the needs of its students,” said John Springer, a Purdue computer and information technology professor who was involved in developing the new degree. “Purdue’s unique approach to the development and delivery of its new online master’s program wholly fulfills these requirements by utilizing a highly interdisciplinary team of Purdue faculty backed by Purdue’s outstanding team of instructional designers.”

For more information about Purdue’s 100% online Master of Science in data science degree, visit the program website .

About Purdue University

Purdue University is a public research institution demonstrating excellence at scale. Ranked among top 10 public universities and with two colleges in the top four in the United States, Purdue discovers and disseminates knowledge with a quality and at a scale second to none. More than 105,000 students study at Purdue across modalities and locations, including nearly 50,000 in person on the West Lafayette campus. Committed to affordability and accessibility, Purdue’s main campus has frozen tuition 13 years in a row. See how Purdue never stops in the persistent pursuit of the next giant leap — including its first comprehensive urban campus in Indianapolis, the Mitch Daniels School of Business, Purdue Computes and the One Health initiative — at https://www.purdue.edu/president/strategic-initiatives .

Media contact: Brian Huchel, [email protected]

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Master of Science in Data Science (MSDS)

Normal Period of Study Full-time: 1 year; Part-time: 2 years

Mode of Study Combined

Mode of Funding Self-financed (non-UGC-funded)

Programme Leader Professor Matthias Hwai-yong TAN

Deputy Programme Leader Professor Li ZENG

Click here to download leaflet

Programme Aims

The programme aims to produce data-analytic graduates to meet the growing demand for high-level data science skills and to prepare graduates to apply data science techniques to knowledge discovery and dissemination in organisational decision-making. It is also intended to help data analytic professionals upgrade their technical management and development skills, and to provide a solid path for students from related quantitative fields to rapidly transition to data science careers.

Programme Intended Learning Outcomes (PILOs):

Upon successful completion of this Programme, students should be able to:

• Apply knowledge of science and engineering appropriate to the data science discipline
• Understand theoretical foundation of contemporary techniques and apply them for managing, mining and analysing data across multiple disciplines
• Comprehend computational tools and use data-driven thinking to discover new knowledge and to solve real-world problems with complex structures
• Recognise the need for and engage in continuous learning about emerging and innovative data science techniques and ideas
• Communicate ideas and findings in written, oral and visual forms and work in a diverse team environment

Course List

Core Electives (15 credit units)

Electives (15 credit units)

The full MSc degree award requires 30 credit units, with the completion of taught courses only; or taught courses plus the dissertation project.

Remarks: Programme electives will be offered subject to availability of resources and sufficient enrolment.

Student Handbook

(Note: The handbooks are updated as at the beginning of the corresponding academic years.)

Admission Requirements

Applicant must be a degree holder in Engineering, Science or other relevant disciplines, or its equivalent

Non-local candidates from an institution where the medium of instruction is NOT English should fulfil one of the following English proficiency requirements.

• a score of 79 (Internet-based test) in the Test of English as a Foreign Language (TOEFL) @# ; or
• an overall band score of 6.5 in International English Language Testing System (IELTS) @ ; or
• a minimum score of 450 in band 6 in the Chinese mainland’s College English Test (CET6); or
• other equivalent qualifications @  TOEFL and IELTS scores are considered valid for two years. Applicants are required to provide their English test results obtained within the two years preceding the commencement of the University's application period. #  Applicants are required to arrange for the Educational Testing Service (ETS) to send their TOEFL results directly to the University. The TOEFL institution code for CityU is 3401.

Tuition Fees

HK$9,900 per credit (for local and non-local students admitted in 2024/25) Credit Units Required for Graduation:  30

Fellowships Scheme

Fellowship awards are available for local students admitted to this programme under the University Grants Committee (UGC) Targeted Taught Postgraduate Programmes Fellowships Scheme supported by the HKSAR Government. This programme in the priority area of “Emerging Research” is one of the targeted programmes listed under the Fellowships Scheme. A maximum of 7 Fellowships, subject to a cap of HK$120,000 as prescribed by the UGC for the settlement of the tuition, would be awarded for the 2024/25 intake. Local students admitted to the programme in full-time, part-time or combined study mode will be invited to submit applications for the fellowships. Please click to view more details about the  Fellowships Scheme .

Career Prospects

Our MSDS programme offers comprehensive and rigorous training for students seeking a profession in data science. Our graduates have embarked on exciting and highly rewarding careers such as data scientists, data analysts, data engineers, AI engineers, professional consultants, managers, and other data expert positions. These careers, which have excellent prospects for growth and high compensation, are in high demand in industries such as finance and banking, technology, real estate, insurance, education, e-commerce, retail and marketing, and transportation and logistics.

Our graduates from the past few years have found employment in prestigious companies that include members of the Big Four accounting firm, tech giants, retail giants, and international banks. Moreover, they are shouldering critical roles that involve the use of data science to aid highly impactful tasks such as strategic business and operations decision-making, and innovative product and process development. Their careers spread across Hong Kong, the USA, and Mainland China (e.g. Beijing, Ningbo, Chongqing, Shanghai, and Shenzhen, etc.) Around 60% of our surveyed graduates receive a monthly salary of over HKD$30,000. Some of our graduates are also furthering their studies in PhD programmes at world-renowned universities.

For general enquiries, please contact the Department of Data Science (DS) at [email protected] For application enquiries, please contact the School of Graduate Studies (SGS) at [email protected] For student visa matters, please contact the Global Engagement Office (GEO) at  [email protected]

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Spatial Data Science Graduate Credit Certificate Program

The Certificate in Spatial Data Science prepares students who have prior experience with the application of Geographic Information Systems (GIS) to research, design, and create novel analytical and visualization solutions to complex problems that intersect people and the environment. The program engages key theories, emerging research, and contemporary spatial analysis techniques so that students can develop new spatial data science software, analytical methods, and cartographic products to visualize and communicate analytical results.

Effective Semester: Summer 2024 Expiration Semester: Summer 2029

Admission Requirements

Applicants apply for admission to the program via the Graduate School application for admission . Requirements listed here are in addition to Graduate Council policies listed under GCAC-300 Admissions Policies . International applicants may be required to satisfy an English proficiency requirement; see GCAC-305 Admission Requirements for International Students for more information.

Intermediate-level experience with professional applications of geographic information systems is expected as pre-requisite knowledge. Coursework to establish that pre-requisite knowledge is available through related coursework and credentials offered in Geography, including its Postbaccalaureate Certificate in GIS program.

Certificate Requirements

Requirements listed here are in addition to requirements listed in Graduate Council policy GCAC-212 Postbaccalaureate Credit Certificate Programs .

Students earn the certificate by completing five courses -- three required and two electives. Students who successfully complete the program earn 15 academic credits.

Students admitted to the Department of Geography's Master of Spatial Data Science or Master of Geographic Information Systems degree programs may count up to 15 credits of certificate program courses toward their degree, subject to restrictions outlined in GCAC-309 Transfer Credit . Certificate students who wish to have certificate courses applied towards a graduate degree must apply and be admitted to that degree program. Admission to a Master’s degree program is a separate step and is not guaranteed.

Graduate courses carry numbers from 500 to 699 and 800 to 899. Advanced undergraduate courses numbered between 400 and 499 may be used to meet some graduate degree requirements when taken by graduate students. Courses below the 400 level may not. A graduate student may register for or audit these courses in order to make up deficiencies or to fill in gaps in previous education but not to meet requirements for an advanced degree.

Learning Outcomes

• KNOW: Graduates will be able to characterize the state of the art in spatial data science methodologies and technologies.
• APPLY/CREATE: Graduates will be able to create new analytical and visualization solutions to complex spatial data science problems that intersect people and the environment.
• COMMUNICATE: Graduates will demonstrate effective scientific writing, presentation, and cartographic communication skills.
• THINK: Graduates will be able to explain and contextualize the distinguishing characteristics of spatial data sources, spatial analysis methods, and geographic visualizations.
• PROFESSIONAL PRACTICE: Graduates will be able to evaluate spatial data science problems to address ethical and professional challenges.

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