Getting a PhD in Data Science: What You Need to Know

A Doctor of Philosophy (PhD) is the highest degree that a professional can obtain in the field of data science. Focused primarily on equipping degree holders with the skills and knowledge required to conduct original research, a PhD prepares degree holders for advanced professional positions in both industry and academia. 

While a PhD can offer many exciting career opportunities, completing advanced study takes commitment. A PhD can take several years, and you may incur costs depending on the financial package you receive. Because of this, it’s important to carefully consider what a PhD entails and how it can benefit your short- and long-term career plans. 

To decide whether to embark on this pathway, learn more about PhDs in data science, the different factors you should consider before joining a program, and the types of programs to consider. Plus, find some suggested online courses to help you start learning today. 

PhD in data science: Overview 

A Doctor of Philosophy (PhD) is a terminal degree in the field of data science, meaning it is the highest possible degree that can be obtained in the subject. Holding a PhD in data science, consequently, signals your mastery and knowledge of the field to both potential employers and fellow professionals. 

At a glance, consider a few key features of a data science PhD: 

Data science PhD vs. master’s degree 

You can choose between two main graduate degrees in the field of data science: a master’s in data science and a PhD in data science. While both of these degrees can have a beneficial impact on your job prospects, they also have key differences that might impact which one is better for you. 

A master’s degree in data science is a graduate degree between a bachelor’s and PhD, which usually takes between one and two years to complete. A master’s degree expands on what you learned in undergraduate school through more advanced courses in topics such as machine learning, data analytics, and statistics. Often, a master’s student in data science also pursues original research and completes a capstone project, which highlights what they learned in their program.

A PhD in data science is a research degree that typically takes four to five years to complete, but can take longer depending on a range of personal factors. In addition to taking more advanced courses, PhD candidates devote a significant amount of time to teaching and conducting dissertation research with the intent of advancing the field. At the conclusion of their doctoral program, a PhD holder in data science will complete a dissertation representing a significant contribution to the field. 

Typically, bachelor’s degree holders entering a PhD program are able to earn their master’s degree as a part of their doctoral program. Those entering a master’s program, however, will usually have to apply for a PhD program, even if it’s in the same department. 

Can you get a PhD in data science without a master’s degree?

While it’s often possible to get a PhD without a master’s degree, some programs may favor candidates with a master’s. Earning a master’s degree can help you build skills that help you in your PhD, allow you to explore different areas, and make you a competitive candidate for degree programs and job opportunities. However, going directly into a PhD program can help you save money and time, so it’s a pathway worth considering if it’s of interest to you.

Skills and curriculum for data scientist PhD programs

Every PhD program is unique, with its own requirements and focus. Nonetheless, these programs do have similar features, such as course, credit, and teaching requirements. To help you get a better understanding of possible requirements for a doctoral graduate program in data science, consider the following example curriculum from NYU [1]: 

• Complete 72 credit hours while maintaining a cumulative grade point average of 3.0 (out of 4.0) each semester.

• Complete core courses in topics like probability, statistics, machine learning, big data, and inference and representation. 

• Complete 39 credit hours of elective courses in such topics as deep learning, natural language processing, and computational cognitive modeling. 

• Complete teaching requirements.

• Pass a comprehensive exam. 

• Pass the Depth Qualifying Exam (DQE) by May 15 of the fourth semester. 

• Complete all steps for approval of a PhD dissertation. 

Is a PhD in data science worth it? 

A PhD can open doors to new career opportunities and boost your employment prospects. Although it can require a significant investment of time and money to complete, it may be the right path for you to achieve your personal and professional goals. Consider these things when deciding if a PhD in data science is right for you: 

How long does a PhD in data science take? 

Doctoral degrees take between five and 11 years to complete [2]. In addition to the amount of time it takes to complete a PhD, finances are another concrete factor you might consider when deciding whether or not to pursue a doctoral degree. According to research conducted by the Education Data Initiative, the average doctorate degree holder owes $84,540 in student debt [2]. 

The exact amount of time and money you might spend obtaining your doctoral degree will depend on your own circumstances and program. Before applying for a doctoral degree, make sure to review each program’s graduation requirements and costs so you have a clear understanding of the commitment.

PhD in data science salary: How much can you expect to make? 

A data scientist with a PhD earns a total median salary of $266,000 annually [3]. This figure includes base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation. 

Typically, the entry-level degree to get a data science position is a bachelor’s degree, meaning that even just an undergraduate degree could help you land a job that earns a higher-than-average salary. According to the US Bureau of Labor Statistics (BLS), for example, the median salary for data scientists was $112,590 as of 2024 [4]. Nonetheless, a PhD will likely prepare you for more advanced positions that could offer higher pay than less specialized roles. 

Data science PhD programs 

If you would like to obtain a PhD in data science, you might consider several types of doctoral programs. These include: 

Online PhD in data science

An online PhD program may appeal to you if you want a more flexible program that allows you to complete your coursework at your own pace. Often, online programs can also be cheaper than their in-person counterparts, although they often offer fewer opportunities for networking and mentorship. If you’re an independent self-starter looking for a program that can fit into your already busy life, then you might consider an online PhD program. 

PhD in data science in-person

An in-person PhD program is a more traditional educational method in which you attend classes on campus with your peers and instructors. In addition to providing doctoral-level instruction, you will also have more opportunities to network and gain more personalized instruction than you will likely encounter through online programs. In-person programs tend to be more expensive and inflexible than online ones.

If you prefer real-world instruction, networking opportunities, and a more structured experience, then you might consider an in-person doctoral program. 

Alternatives to a PhD in data science

As an alternative to a PhD program, you might also consider obtaining a master’s degree. While covering some of the same material as a doctoral program, a master’s usually takes less time and money to complete.

If you’re motivated primarily by the desire to boost your chances of landing a job and gaining financial stability, then a master’s degree program might better help you achieve your goals.

Read more: Is a Master's Degree Worth It?

Check out our free data science career resources 

Explore vital data science concepts, discover the latest trends, and hear from industry professionals by exploring Coursera’s free digital resources:

• Bookmark for quick access: Data Science Terminology and Definitions

• Watch on YouTube: AI Quick Tip: 3 Data Science Hacks

• Hear from an industry professional: Meet the Statistics Teacher Who's Going Back to School for Data Science