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. 

But, the path to obtaining a PhD is filled with many years of potentially costly study that can be discouraging to those looking for rapid career progression. Before jumping into a doctoral program, then, it’s important to define what your goals are and how a PhD may (or may not) fit into them. 

In this article, you’ll learn more about PhDs in data science, the different factors you should consider before joining one, and types of programs to consider. At the end, you’ll also find some suggested online courses to help you get started today. 

PhD in Data Science: Overview 

A Doctor of Philosophy (PhD) is the 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, here’s what you should know about a Data Science PhD: 

PhD vs. Master’s Degree in Data Science

There are two 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 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 was 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. 

Skills and curriculum 

Every PhD program is unique with its own requirements and focus. Nonetheless, they do have similar features, such as course, credit, and teaching requirements. To help you get a better understanding of how a doctoral graduate program in data science might be, here’s an 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.

• Core courses in topics like probability, statistics, machine learning, big data, inference, and research. 

• 39 credit hours for 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 their fourth semester. 

• Complete all steps for approval of their PhD dissertation. 

Is a PhD in Data Science worth it? 

A PhD can open doors to new career opportunities and boost your employment prospects. But, it can also take a lot of time and money to complete. Everyone’s personal and professional goals are different, so consider these things when deciding if you should pursue a PhD in Data Science:  

Cost and time

The amount of time and money it takes to complete a PhD are perhaps the most concrete considerations one makes when deciding whether or not they should pursue a doctoral degree. According to research conducted by Education Data Initiative, the average cost of a doctorate degree is $114,300 and takes roughly four to eight years to complete [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 what you’re getting into. 

Data Science PhD salary 

While there are no official statistics on the salary gains data scientist earn by getting a PhD, the median salary for all data scientists is much higher than the national average in the United States. According to the U.S. Bureau of Labor Statistics (BLS), for example, the median salary for data scientists was $100,910 as of May 2021 [3]. 

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. Nonetheless, a PhD will likely prepare you for more advanced positions that could offer higher pay than less specialized roles. 

Data Science PhD programs 

There are several types of doctoral programs that you might consider if you would like to obtain a PhD in data science. These include: 

PhD in data science online

An online PhD program may appeal to individuals who are interested in a more flexible program that allows them to complete their coursework at their own pace. Often, online programs can also be cheaper than their in-person counterparts, though they often offer less opportunities for networking and mentorship. If you’re an independent, self-starter looking for a program that can fit into their 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 in-person ones.

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

Alternatives 

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 much 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.

Learn more about data science 

Whatever your educational goals, data science requires extensive knowledge and training to enter the profession. To prepare for your next career move, then, you might consider taking a flexible online course through Coursera. 

The University of Colorado Boulder’s Data Science Foundations: Data Structures and Algorithms Specialization teaches course takers how to design algorithms, create applications, and organize, store, and process data efficiently. Their online Master of Science in Data Science, meanwhile, teaches broadly applicable foundational skills alongside specialized competencies tailored to specific career paths in just two years of instruction.