Data Science PhD Programs

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

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

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Featured Analytics Programs

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

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

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

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

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

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

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

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

Trending Research Areas in Data Science

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

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

Best PhD in Data Science Programs for 2024

1. Boise State University

   Boise, Idaho

   
   Program: Data Science Ph.D.
   Credits: 60
   Cost per credit: $1,007
   Delivery Method: Campus
   GRE Required: Required
   Total Tuition: $60,420
   DASCA Designation: No
   Learn more: Program details

2. Capitol Technology University

   Laurel, Maryland

   
   Program: Doctor of Philosophy (PhD) in Business Analytics and Data Science
   Credits: 54-66
   Cost per credit: $950
   Delivery Method: Online
   GRE Required: Not required
   Total Tuition: $62,700
   DASCA Designation: No
   Learn more: Program details

3. Chapman University

   Irvine, California

   
   Program: Ph.D. in Computational and Data Sciences
   Credits: 70
   Cost per credit: $1,910
   Delivery Method: Campus
   GRE Required: Required
   Total Tuition: $133,700
   DASCA Designation: No
   Learn more: Program details

4. Clemson University

   Clemson, South Carolina

   
   Program: MS & PhD in Biomedical Data Science and Informatics
   Credits: 65-68
   Cost per credit: $840
   Delivery Method: Campus
   GRE Required: Required
   Total Tuition: $57,120
   DASCA Designation: No
   Learn more: Program details

5. Colorado Technical University

   Colorado Springs, Colorado

   
   Program: Doctor of Computer Science - Big Data Analytics
   Credits: 100
   Cost per credit: $598
   Delivery Method: Online
   GRE Required: Not required
   Total Tuition: $59,800
   DASCA Designation: No
   Learn more: Program details

6. Florida Atlantic University

   Boca Raton, Florida

   
   Program: Professional Ph.D. in Computer Science: Data Science and Analytics (DSA) Concentration
   Credits: 42
   Cost per credit: $800
   Delivery Method: Campus & online
   GRE Required: Optional
   Total Tuition: $33,600
   DASCA Designation: No
   Learn more: Program details

7. George Mason University

   Fairfax, Virginia

   
   Program: Computational Sciences and Informatics, PhD
   Credits: 72
   Cost per credit: $1,487
   Delivery Method: Campus
   GRE Required: Required
   Total Tuition: $107,064
   DASCA Designation: No
   Learn more: Program details

8. Harrisburg University of Science and Technology

   Harrisburg, Pennsylvania

   
   Program: Data Sciences Ph.D.
   Credits: 36
   Cost per credit: $830
   Delivery Method: Campus
   GRE Required: Required
   Total Tuition: $29,880
   DASCA Designation: No
   Learn more: Program details

9. Icahn School of Medicine at Mount Sinai

   New York, New York

   
   Program: Genetics and Data Science, PhD
   Credits: 64
   Cost per credit: $1,750
   Delivery Method: Campus
   GRE Required: Not required
   Total Tuition: $112,000
   DASCA Designation: No
   Learn more: Program details

10. Indiana University

    Indianapolis, Indiana

    
    Program: Data Science Ph.D.
    Credits: 90
    Cost per credit: $683
    Delivery Method: Campus
    GRE Required: Required
    Total Tuition: $61,470
    DASCA Designation: No
    Learn more: Program details

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

Data Science PhD Program Overview and Curriculum

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

Admission Process

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

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

Post-Admission Process

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

Online PhD Data Science Degree Programs

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

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

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

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

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

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

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

Career Paths and Outcomes

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

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

Academic Outcomes

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

Industry Outcomes

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

Frequently Asked Questions