phd data science worth it

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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Why You Should Get a PhD in Data Science

A Doctor of Philosophy (PhD) in Data Science will not pay off instantly. You will have to dedicate a few years to sharpening your skills and gaining more expertise in the field before being eligible for the highest-paying jobs.

If you believe in delayed gratification, then getting a PhD might be on the list of things you want to do. To aid in your consideration, you should be aware of the essential information provided in this guide about PhDs in Data Science.

PhD in Data Science: An Overview

As a graduate degree, a PhD in Data Science will involve research. You’ll need to be committed to working in the field, testing new methodologies, and experimenting with data science tools and technologies.

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Data science has a wide range of applications in which you can choose to specialize. Aside from the field of information technology, you can apply your expertise in the health and medical sciences, or you can work with mergers and acquisitions in business and finance. Below are some basic facts about data science according to research conducted by Stitch .

• 38% of data science professionals have a PhD.
• More than half of known data scientists are based in the US.
• In the last four years, data scientists have doubled in number.

What Is a Doctoral Degree in Data Science?

A doctoral degree is the highest level of data science education you can pursue. Everything there is to know about data science, from the theoretical to the practical aspects, should be taught to you by the end of your program.

After earning your doctoral degree, you are expected to have a wide span of knowledge and a deep understanding of the field. Your expertise entitles you to academic freedom and an impressive salary.

Benefits of a PhD in Data Science

Although a PhD takes years of time and research, you’ll have more freedom with your projects or research, whether in academia or industry. Since your PhD proves your expertise, you can be a leader in the field. What is more, a PhD program in data science leads to a higher salary than even the best data science master’s programs .

Data Science Job Prospects

While job prospects for professionals with a PhD in Data Science are few, you can look at it as quality over quantity. According to the Bureau of Labor Statistics, the field of data science is projected to grow by 31 percent from 2019 to 2029.

You can take on full-time or part-time jobs relevant to your specialization in academia, the government, or the tech industry. In these kinds of jobs, you’ll be directly analyzing data for your research department, your bosses, or your clients. If you prefer to be the boss, you can become a consultant instead.

Data Science Certifications

Earning a data science certification will give you a sense of accomplishment and fulfillment in both your personal and professional life. Certification validates your knowledge and expertise to potential employers and clients.

There is a long list of professional credentials available. All you need are the resources and the commitment. The certifications listed below range in cost from $99 to $850.

Cloudera Certified

If you’re interested in becoming a data engineer or a data analyst, you should know that Cloudera offers a professional credential for the former and an associate certification for the latter.

Data Science Council of America (DASCA)

The professional organization DASCA can certify you as a Senior Data Scientist or a Principal Data Scientist.

SAS Certified

Those who know how to use SAS have three SAS certification programs to choose from: AI and Machine Learning, Big Data, and Data Scientist.

Microsoft Certified: Azure

If you want to validate your data science and artificial intelligence skills using Microsoft Azure, check out Microsoft’s Data Scientist Associate and AI Fundamentals certifications.

Data Science Doctorate Jobs

Fine-tuning your skills with a PhD means that you will be veering away from the topics and tasks less relevant to your degree. Because you will have specialized knowledge, your pool of potential jobs becomes smaller. Below are the roles that you can take on after getting your PhD. Job growth and employment figures are estimates based on available BLS data.

Machine Learning Engineer

Machine learning engineers, a subcategory of computer and research scientists, work on building programs instead of focusing on just data analysis. In this profession, you design and develop algorithms and generate deep learning systems to streamline processes.

Salary: $112,806

Job Growth: 15%

Total Employment: 32,700

Data Scientist

As a data scientist, you handle complex data and transform it into results that the clients can understand. This role should not be confused with data analysts. A data scientist must have a higher level of experience with experimentation. You should only become a data scientist if you have good problem-solving and critical thinking skills.

Salary: $96,596

Job Growth: 31%

Total Employment: 33,200

Business Analyst

The work of a business analyst, or management analyst, involves the analysis of large datasets on behalf of a company. In this role, you use algorithms, frameworks, and other tools to analyze a company’s data. You’ll also work hand-in-hand with the management. If you are a business-minded person, this role will be perfect for you.

Salary: $61,764

Job Growth: 11%

Total Employment: 876,300

Data Analyst

Data analyst and data scientist careers might seem similar. The main difference lies in the depth of understanding of the problem at hand. Data analysts are more involved in handling, cleaning, analyzing, and visualizing large datasets.

If you want to become a data scientist, refining your skills as a data or operations research analyst can help you achieve that goal faster.

Salary: $61,617

Job Growth: 25%

Total Employment: 105,100

Graduate School Accreditation

Graduating from an accredited school catches the attention of future employers. Aside from that, you can also other benefits such as financial aid opportunities. PhD in Data Science salaries are also higher when the degree comes from an accredited school.

The US Department of Education and the Council for Higher Education Accreditation award graduate school accreditations.

National Accreditation

If the school you want to enroll in has national accreditation, it has met the quality criteria and other key national education standards.

Regional Accreditation

In a nutshell, regional accreditation is like national accreditation, but for a smaller geographical area.

Institutional Accreditation

Institutional accreditation is a type of quality assurance for a higher learning institution.

Specialized Accreditation

The opposite of institutional accreditation, a specialized one is only applicable to a specific course or department, not the entire school.

PhD Program Admissions

Applying to a PhD program can be confusing. Some schools might require a higher level of education and more research experience. Others may only ask for one of these, but you might have to fulfill course prerequisites before you start the program. Whatever the case for your chosen school, below are the requirements for a typical PhD program.

Transcripts

Transcripts from previous schools are required for most PhD applications. Most programs only accept PhD candidates who already have a Bachelor’s Degree in Data Science , and some require a master’s degree as well. A few institutions waive degree requirements for students with significant experience in the field.

Letters of Recommendation

A letter of recommendation is a valuable testament to your competencies. If you are still in your internship or working at a company, do your best so that your supervisors can vouch for your abilities. You can also ask for letters of recommendation from professors in your previous degree program.

Letter of Intent

If there is a requirement that can make you rethink your choice to apply for a PhD, it would be the letter of intent. This is the document where you lay out why you want a PhD and why you will be a good fit for the program. The admissions committee will use this letter to determine how motivated you are and how likely you are to finish the program.

Data Science PhD Degree Cost

Most PhDs cost around $30,000 per year. If you are to study for four years, that would be $120,000. Although you are not going to pay the entire sum upfront, this is still a large amount of money.

Fortunately, there are plenty of financial aid opportunities for prospective PhD students, so it is unlikely that you will have to pay much out of pocket. Even without aid, you can save money on a PhD program by choosing a school in your own state.

PhD Fellowships

A PhD fellowship is a type of award that students can use to fund their research. Usually, a fellowship includes a tuition waiver and a yearly stipend. A PhD fellowship will not require you to hold down a teaching or research assistantship. You’ll also have more freedom to pursue your own research interests.

Teaching/Research Assistantships

A PhD candidate can also apply for a teaching or research assistantship to help finance their studies. These assistantships are a type of work-study program. The guidelines vary by university.

Student Loans

Student loans should be your last resort in financing your doctorate. While you won’t have to work while studying, you will owe a lot of money after you graduate.

Fully-Funded Programs

Fully-funded programs are incredible options for PhD students. Not worrying about tuition and basic living expenses could definitely boost a person’s academic performance. Some fully-funded programs also offer a stipend and health insurance to keep students motivated to finish. Below are some fully-funded doctoral programs for you to check out.

• University of Southern California | PhD in Data Science and Operations
• University of Nevada, Reno | PhD in Statistics and Data Science
• Kennesaw State University | PhD in Analytics and Data Science

Data Science PhD Program Requirements

The workload for a PhD in Data Science is similar to other PhD programs. Since it can be overwhelming, we have divided the requirements into sections for ease of understanding.

Typical coursework for a PhD student consists of about 30 credits, which is about the same as a master’s program. The main difference between a master’s and a PhD is that the latter imposes additional requirements, which include qualifying exams and a dissertation.

To become a PhD candidate, you’ll need to take a certain number of qualifying exams. Each exam is a measure of your level of expertise in the field. If you pass your qualifying exams, you will be permitted to start your dissertation project.

There are different types of qualifying exams. An oral exam tests your understanding of the field and your ability to demonstrate your understanding to a panel of experts. Theoretical and practical exams will test your theoretical and hands-on expertise.

Apprenticeship

Some universities require their PhD students to log a specific number of apprenticeship hours in teaching or research. If your chosen program features this requirement, you may have to complete your hours before you start your dissertation.

Dissertation

A dissertation is a document of around 10,000 words that shows your expertise in your chosen specialization. At this point in your education, you should be able to contribute original research to the field of data science.

Data Science PhD Courses

Before you begin your dissertation, you’ll need to complete your coursework. The number of core classes that you’ll need to take varies by university. Below are the topics that will fortify your fundamental knowledge of data science.

Data Science

As a PhD candidate, you should already have an excellent grasp of the foundations of data science. But to mentally prepare students, an introduction to data science is almost always part of a graduate program. Some of the topics included in this course are data formats, wrangling exploration, visualization, statistical methods, and data governance.

Probability and Statistics for Data Science

Probability and statistics are broad topics with wide applications. To set boundaries, the professor will limit the material to data science and its applications. This course might be split into two separate classes in some universities.

Students learn about multiple and random variables and expectation convergences in the probability portion of this course. In the statistical portion, students learn about models, estimation, hypothesis testing, Bayesian methods, and linear and logistic regression.

Machine Learning

Machine learning falls under the artificial intelligence (AI) umbrella. So, some core concepts of AI might also be included in this course. Along with machine learning, you’ll also learn about deep learning, neural networks, and k-nearest neighbors algorithms.

A course on big data is designed to help you deal with large datasets with ease. The major subtopics you should be taking notes on are relational databases, distributed storage, distributed computation, applications, and algorithms.

Online PhD in Data Science

Pursuing a PhD in Data Science online is not as common as traditional education. However, there are some good online data science programs out there. Below are some of the benefits of getting your PhD online.

Cheaper Tuition

If you opt to get a PhD online, you’ll accumulate less student debt. You won’t have to pay for the university facilities and amenities. You can save money on lodging and transportation and devote more resources to your dissertation.

Flexible Attendance

There will be more options for your classes if you enroll in an online program. You can attend courses in the evenings or on the weekends, whichever works for you.

Financial Aid Opportunities

Just because you are studying online doesn’t mean you won’t have financial assistance available to you. Fortunately, online PhD students can still apply for grants and loans to fund their studies.

Data Science Education Pathways

You can get a decent data science education at either a traditional university or a bootcamp. Let’s take a look at both options so you can decide which one will work best for you.

Advanced Degrees

Many universities offer Master’s Degrees and PhDs in Data Science. This option requires you to take classes for several years and conduct original research on a specific topic.

Coding Bootcamps

Coding bootcamps are relatively new in data science education. Although data science bootcamps are comparable with traditional schools in that they provide bachelor’s-level education through intensive classes and training, few of them are as exacting as a PhD program.

But the field of data science is changing rapidly, and not all PhD programs can provide master’s students with the industry-led training they crave. If you have a Master’s in Data Science and you’re looking for a more advanced bootcamp, try NYC Data Science Academy .

Data Science PhD Program Options

Now that you have an idea of what a PhD in Data Science can offer you in your professional journey, what you’ll want to know next are your program options. Some program options are solely for data science, while others are in combination with big data.

Your chosen program will dictate how many years you should dedicate to your PhD. To help you out with your decision-making, we have compiled a list of the top program options for data science PhD students.

PhD in Data Science

In this program, techniques such as predictive and big data analytics will be your daily fare. The goal is to be able to implement these methods and apply new strategies for powering different industries in society.

PhD in Computational Science and Statistics

If you are a person who uses statistical data and evidence to support your opinion, then this program is for you. Here you will formulate, model, analyze, and solve research problems using computational and statistical methodologies. If you want to make your quantitative skills more impactful in the field, find a PhD in Computational Science and Statistics.

PhD in Data Science, Analytics, and Engineering

This type of program is for professionals who want to be involved in developing new systems and algorithms for dealing with high dimension, high volume, and heterogeneous data streams. Usually, graduates of this program are employed in the life sciences or in health care.

PhD in Business Analytics and Data Science

For business-inclined professionals with backgrounds in data science, this PhD program might be for you. Students in this program develop high-level decision-making and data science technical skills to design business-appropriate analytical models.

Data Science Professional Organizations

Professional organizations will broaden your career network and create opportunities for improvement. One of the perks of belonging to a professional organization is getting updates on the newest developments and innovations in data science. They also host meetings, seminars, and other professional events where you can interact with fellow data scientists.

Below are the top data science professional organizations that you might want to get involved with. If you have a specific area of interest, you might want to join the pool of experts in that specialization.

Association of Data Scientists (ADaSci)

This professional organization in data science emphasizes continuous learning. If you share the same sentiment, then there is no doubt that ADaSci is for you.

Becoming a member entitles you to free access to ADaSci’s Lattice Journal publications, as well as priority access to its conferences and continuing education programs. Even getting a job as a fresh graduate of a PhD program will come easy since you will have the first word on new job openings and internships.

American Statistical Association (ASA)

Also known as the Big Tent of Statistics, ASA aims to promote the practice and profession of statistics. An ASA membership can give you more career and learning opportunities in statistics. You also have free access to ASA journals, the Significance magazine, and Amstat News to keep you updated on what’s going on in the field.

If you are a K-12 educator who is genuinely interested in statistics and can commit to a professional organization, your best option is ASA.

Handling large volumes of data might be daunting to some. If the volume, velocity, and variety of big data are already part of your daily activities, then you might want to join a professional organization that could help. DASCA believes that this generation is the era of big data. If you share the same belief, then this professional society is for you.

DASCA offers three credentials for three of the top professions in data science: Big Data Analytics, Big Data Engineering, and Data Scientifics. ADaSci has similar offerings, but DASCA offers a wider perspective on the field by incorporating relevant big data concepts.

Institute for Operations Research and the Management Sciences (INFORMS)

There is also an organization for professionals in operations research and management science. INFORMS is the largest professional organization in the field and you can expect that your network will expand fastest here.

With 16 journals, 11 events, and 165 communities, INFORMS has over 12,500 members. For 25 years, the organization has helped decision-makers from different industries achieve the full potential of their organization or project.

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Best Doctorates in Data Science: Top PhD Programs, Career Paths, and Salaries

If you are tech-savvy and like to stay up-to-date on the latest developments in the computing field, you might be interested in getting a data science job. The industry is interdisciplinary, with those working within it proficient in statistics, computer science, and operations research.

As such, this career isn’t pursued casually, as extensive education is required to become a data scientist and enter the industry. While some jobs can be obtained with a master’s degree, earning one of the best PhDs in Data Science is a much better option if you want the freedom to be able to conduct your own research.

Find your bootcamp match

Within your data science PhD studies, you will spend the latter two years of the four-year program doing your own unique research and writing a dissertation on your findings, preparing you to do the same in the real world. Those with a master’s degree, by contrast, don’t have as many creative liberties and don’t usually develop their own research, but rather analyze existing studies.

With the amount of schooling and skills required to work within the field, you might be wondering what a PhD in Data Science Salary looks like. Below, we’ll discuss the top schools for getting a PhD in Data Science, as well as the career outlook once you get your degree.

What Is a PhD in Data Science?

A PhD in Data Science is a four- or five-year, full-time degree pursued after a bachelor’s or master’s degree. Faculty in university PhD programs often like students to have a prior master’s degree, but if not, they might offer integrated master’s and PhD studies.

Within a PhD in Data Science program, you’ll spend the first two years of your program learning foundational knowledge, taking advanced courses in statistics, computer programming, data mining, research methodology, and so much more. The latter two years of the degree involve conducting your own unique research. You will then record your findings in a dissertation and defend your research before a committee to get your doctorate.

How to Get Into a Data Science PhD Program: Admission Requirements

You can get into a Data Science PhD program by meeting a university’s admission requirements, which will differ between each school. Typically, the minimum educational requirement is a bachelor’s degree in a related STEM degree, but most programs prefer a prior master’s degree.

If you don’t have a master’s degree, it is highly recommended to at least be proficient in a coding language and to have taken classes in calculus, statistics, and engineering. You will also need to have a minimum 3.0 GPA across your postsecondary studies and send the school your academic transcripts. There are also supplemental materials you would need for an application. These include a statement of purpose, two or three letters of recommendation, GRE scores, and a professional resume.

PhD in Data Science Admission Requirements

• A postsecondary degree in a related field
• Academic transcripts
• Graduate record examination (GRE) scores
• Coursework in data structures, algorithms, calculus, and linear algebra
• A background in a programming language
• Letters of recommendation
• Admission essays
• Personal statement

Data Science PhD Acceptance Rates: How Hard Is It to Get Into a PhD Program in Data Science?

It can be hard to get into a PhD program in Data Science. PhD programs within universities are very exclusive. While they receive a sea of applications, most schools only accept about a dozen of them. For example, Yale, one of the best schools in the country, had over 300 applicants but only made around 13 offers. As such, it is wise to apply for multiple PhD programs in order to increase your chances of getting an offer of admission.

How to Get Into the Best Universities

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Best PhDs in Data Science: In Brief

Best Universities for Data Science PhDs: Where to Get a PhD in Data Science

Since data science is a new field of scientific inquiry, it can be difficult to find the best universities for getting a data science PhD. In order to help you on your educational path, we’ve listed the 10 best schools for a data science PhD, below.

Arizona State University (ASU) was ranked the nation's most innovative university by US News & World Report. Originally founded in 1885, ASU has grown to now offer more than 160 programs at the graduate level in everything from art to engineering. The graduate school is well-known for its research work.

PhD in Data Science, Analytics, and Engineering

This program is geared toward those who want to work in either the data science industry, academia, or government to solve real-world problems through data-informed methods. The 12 credits of core courses within this program include data mining, statistics, security and assurance of information, and database management. 

If you want to focus on engineering, you’ll take nine credits across cloud computing, database systems, and databases for web and other multimedia. If you want to focus on analytics, you'll take Machine Learning Statistics, Regression Analytics, and Data Visualization. 

As a culmination of your studies, you’d produce a thesis. This requires you to propose a topic of study to the dissertation supervisory committee, and upon passing their comprehensive exam, you can begin your research. Ten days before you are to defend your dissertation, which must come less than a year after completing your 60th credit, you’ll submit a version of it to the committee for review.

PhD in Data Science, Analytics, and Engineering Overview

• Program Length: 4-6 years
• Acceptance Rate: 88% (school acceptance rate)
• Tuition: $11,720/year (in state); $23,544/year (out of state)
• PhD Funding Opportunities: Awards, fellowships, and scholarships

PhD in Data Science, Analytics, and Engineering Admission Requirements

• Application
• Application fee
• Official transcripts 
• Three letters of recommendation 
• Letter of intent  
• GRE scores 

Bowling Green State University (BGSU) offers more than 20 PhD programs in a variety of disciplines including engineering, psychology, business, and music. Since its beginnings in 1910, BGSU has been noted for its engineering and scientific research, being one of eight universities in the nation with the Carnegie Foundation’s Community Engagement Classification.

PhD in Data Science

This research-oriented program is interdisciplinary, incorporating teachings from applied statistics, operations research, and computer science. A unique aspect of BGSU’s program is that you’ll need to take ethics classes in order to understand the moral ramifications of gathering data and in communications to learn to effectively present their findings. Before beginning your 16- to 30-credit dissertation, you will need to pass the qualifying examination that involves oral and written sections. 

PhD in Data Science Overview

• Program Length: 4-5 years
• Acceptance Rate: 79% (school acceptance rate)
• Tuition and Fees: $523/credit (in state); $856/credit (out of state)
• PhD Funding Opportunities: Assistantships, scholarships, fellowship 
• Payment of $45 application fee
• Minimum GPA of 3.0
• Official or unofficial transcripts from previous institutions 
• Graduate Record Examination (GRE) scores
• Graduate Management Admission Test (GMAT) scores
• Three letters of recommendation
• Resume 

Chapman offers a variety of graduate programs, with 66 master’s and seven doctoral degrees in disciplines like business, law, education, and health sciences. It was founded in 1861 and is known for its research, with more than 31,000 citations from its 5,283 publications. Chapman University is also known for its strong alumni network, which can help graduates find jobs and networking opportunities.

PhD in Computational and Data Sciences

The PhD in computational and data sciences is designed for students who want to work in fields like population genetics, earth systems, biotechnology, bioinformatics, and economic science. The curriculum includes coursework in mathematical modeling, mining data, data analysis, and computational science, as well as research and thesis guidance from faculty. 

The program is structured so that students can specialize in an area of computational science that interests them, such as scientific computing, data science, or machine learning, allowing students to also choose their dissertation topic. Before becoming doctoral candidates, students take qualifying exams for their core curriculum and do presentations on their elective courses. 

PhD in Computational  Data Sciences Overview

• Acceptance Rate: 60% (school acceptance rate)
• Tuition and Fees: $32,400 tuition
• PhD Funding Opportunities: Assistantships, work-study, loans 

PhD in Computational and Data Sciences Admission Requirements

• Proof of satisfactory coursework in computer programming, differential equations, and statistics
• $60 application fee
• Two letters of recommendation 
• 750-word statement of interest

This circa 1889 school offers more than 130 programs at the graduate level, with 1,687 students currently taking on one of its 50-plus doctoral programs. The school has various innovation clusters of research types, including those related to environmental sustainability, innovations in health, data science and cyberinfrastructure, transportation, and advanced manufacturing. 

PhD in Biomedical Data Science and Informatics

This joint program through the college and the Medical University of South Carolina (MUSC) aims to teach students how to remedy issues in medicine through the combined study of information and computer sciences. Courses within the program will cover statistical theory, data management, machine learning, and bioinformatics. 

Students spend the first two years doing coursework, the third year completing professional development training and research electives, and the fourth year solely researching. Research, seminars, and lab rotations will consist of 24 credit hours. Before completion of the program you’ll need to take a qualifying exam alongside proposing, writing, and defending your dissertation. 

PhD in Biomedical Data Science and Informatics Overview

• Acceptance Rate: 49% (school acceptance rate)
• Tuition and Fees: $691/credit (in state); $1491/credit (out of state) 
• PhD Funding Opportunities: Assistantships, scholarships and fellowships

 PhD in Biomedical Data Science and Informatics Admission Requirements

• Bachelor’s degree in a STEM field, with one year of calculus and biology classes
• Graduate record examination (GRE) scores 
• Prior computer programming work experience or coursework
• Work or research experience (recommended) 
• Personal essay
• Two or three letters of recommendation 

This university provides more than 30 doctoral programs just within the College of Science. Something unique to the circa 1949 school—known for its research in physics, immunology, molecular medicine, and biodiversity—is that its staff encourages research teams to incorporate members across various disciplines, bringing the insight and strengths of those respective fields together.

PhD in Computational Sciences and Informatics

Throughout this 72-credit program in the Department of Computational and Data Sciences, you’ll choose two out of four core courses in statistical and scientific visualization, advanced computing, databases, or numerical methods, and then choose from a rotating list of emphasis courses. Emphasis classes might cover topics like knowledge mining, statistical inference, or Bayesian inference decision theory. 

By the end of your first year, you’ll need to obtain a research advisor, then get your proposal approved by the department committee to be considered a candidate for a PhD. A month before defending their dissertations, students conduct a pre-defense to get final revision recommendations. 

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PhD in Computational Sciences and Informatics Overview

• Acceptance Rate: 91% (school acceptance rate)
• Tuition and Fees: $12,594/year (in state); $33,906/year (out of state)
• PhD Funding Opportunities: Fellowships, assistantships, lecturer positions, faculty grants, scholarships, work-study 

PhD in Computational Sciences and Informatics Admission Requirements

• Mathematics background
• Knowledge of programming languages such as C, C++, and Python
• Personal statement 

Though Harrisburg only joined the Commonwealth of Pennsylvania in 2001, the private, not-for-profit university has grown to have an enrollment of 4,000 students from over 100 countries. While it only offers three PhD degree programs in data science, computational science, and information systems engineering, it is known for its impressive research in supercomputer datamining, aquaponics, and virtual reality. 

PhD in Data Sciences 

This program strives to teach PhD candidates diverse methods of data science and train them to be able to apply their analytical knowledge across disciplines beyond data science. The first two years of the program are Harrisbug's Analytics Master's Degree, and students apply for the actual PhD in the final semester of that program. 

If, however, students have a prior master’s degree from Harrisburg in computer science, they can transfer those credits and complete this four-to-five year doctoral degree in a shorter period. After completing the classwork portion of the degree, taking labs, seminars, classes, and doing fieldwork, they’ll begin their dissertation research. The defense of their dissertation will function as their final exam.

PhD in Data Sciences Overview

• Acceptance Rate: N/A
• Tuition and Fees : $800/credit hour (in state); $4,800/credit hour (out of state)
• PhD Funding Opportunities: Scholarships, grants, loans, work-study

PhD in Data Sciences Admission Requirements

• GRE/GMAT (strongly recommended)
• Essay on career goals
• Proof of research potential (courses or projects) 
• Minimum master’s degree GPA of 3.3
• A letter of intent 

The 1969-founded Indiana University-Purdue University Indianapolis (IUPUI) is an eponymous merger between the two schools and offers 550 programs across all levels. Of those, 57 are PhDs, covering everything from American studies and economics to addiction neuroscience and epidemiology. Some of their latest research breakthroughs were in the fields of informatics and computing, cardiology, nanosystems, and artificial intelligence. 

With data science being a field in its infancy, IUPU’s School of Informatics and Computing strives to have its graduates be leaders within this ever-evolving industry. Students will take classes in system analysis and design, monitoring social media, and data mining and visualization. 

PhD candidates can collaborate with professors on groundbreaking research in information infrastructures, Android science, computer security, machine learning, dataset integration, and computational social science. After earning this interdisciplinary degree, doctoral graduates will be ready to work in academia, health care, or even business intelligence. 

• Acceptance Rate: 84% (school acceptance rate)
• Tuition and Fees: $425/credit (in state) ; $1,350.00/credit (out of state)
• PhD Funding Opportunities: Faculty grants, work-study, loans, internal funding, foundation or corporate funding, funding agencies
• GPA of 3.5 or higher 
• GRE scores in the 70th percentile or higher for all sections
• Bachelor’s degree (master’s degree preferred)
• Completed classes in computer programming, statistics theory, linear algebra, and multivariable calculus
• Online application
• 500- to 750-word statement of purpose

Founded in 1966, Kennesaw provides more than 170 programs to its 40,000-plus students. Its 11 doctoral programs include studies in computer science, education, engineering, international diplomacy, business administration, and more. The core of the university's studies relates to technology and computing, medicine, human well-being and development, and sustainability. 

Doctoral Degree in Analytics and Data Science 

This interdisciplinary program combines business, math, stats, and computer science to make for well-rounded PhD candidates. Furthering that mission, the school also teaches written and oral communication skills to help graduates thrive in business or research fields. 

In the 78-credit program, students will take classes on machine learning, mining data, analyzing big data, and graph theory in their first year. This is followed by 21 credits of electives in their second year. Though students often participate in research projects during their first two years, the latter two of their programs will involve independently-led studies for their dissertation. 

Doctoral Degree in Analytics and Data Science Overview

• Acceptance Rate: 82% (school acceptance rate)
• Tuition and Fees: Qualified students are given a research stipend and waived tuition
• PhD Funding Opportunities: Foundations and institutes, corporate programs, scholarships, grants, loans, clearinghouses, coalitions, research stipends

Doctoral Degree in Analytics and Data Science Admission Requirements

• Master’s degree in computational-related discipline
• If no master’s degree, apply to the combined Master’s Degree in Applied Statistics or Computer Science program
• Strong proficiency in a programming language like Python
• Online application and $60 application fee
• Official transcripts from previous colleges or universities 
• The graduate record examination (GRE) scores 
• Statement of purpose 
• Completion of math courses through Calculus II
• SAS Certification (preferred) 

This 1874-founded school has 150 graduate programs, with 50-plus PhD programs in disciplines like medicine, physics, economics, and chemical physics. They have research programs in more than just pure and applied mathematics, as they also perform studies on wildfires, disinfectants, and autoregulation. 

PhD in Statistics and Data Science 

Prospective employees in academia, business, or government should consider this interdisciplinary research-based program in the Department of Mathematics and  Statistics in the College of Science. The 72 credit hours of this degree are broken up into 48 hours of classwork covering topics like linear models, statistical theory and computing, and quantitative methods, 30 of which should be at the 700-level. There are 24 dissertation credits and 24 master’s classes from a previously finished graduate degree. 

In order to continue within the PhD program after the third year, candidate hopefuls will need to pass a written qualifying test. After the qualifying test, students need to score highly on an oral exam in their chosen concentration before submitting and defending their dissertations.

PhD in Statistics and Data Science Overview

• Program Length: 4-6 years (8 years max)
• Acceptance Rate: 88% for overall school
• Tuition and Fees: $305.50/credit 
• PhD Funding Opportunities: Work-study, assistantships, scholarships, stipends, tuition waiver, subsidized medical plan

PhD in Statistics and Data Science Admission Requirements

• Online application 
• Bachelor’s and master’s degree transcripts
• Mathematics test scores (recommended)
• Financial aid application

Founded in 1701, Yale University is one of the oldest universities in the United States and is ranked fifth-best school in the nation by US News & World Report. Yale has 12 different professional schools and 73 different graduate degree programs. The university is especially well-known for its research in the humanities, environmental science, social sciences, and biotechnology. 

PhD program in Statistics and Data Science 

Students entering this degree program will focus on probability, statistics, information theory, data mining, machine learning, neural networks, and more as their foundational studies. After that, students take elective classes on one-off special topics classes that change between semesters. 

Those in the program need to take an oral and practical exam in their first year and begin their dissertation work in either their second or third year. This is usually a five-year program, with students getting a dissertation fellowship in their fifth year. Yale is a very exclusive school, and last year only made between 12 and 14 offers to the 300 applicants it received. As such, applying to other schools, in addition to Yale, would be a wise choice. 

PhD program in Statistics and Data Science Overview

• Program Length: 5 years
• Acceptance Rate: 5% (school acceptance rate)
• Tuition and Fees: $45,700/year (waived through provided fellowship)
• PhD Funding Opportunities : PhD students get a fellowship that covers all tuition through first five years in addition to an annual stipend of $36,000, Teaching fellowships, stipends, and health care benefits

PhD program in Statistics and Data Science Admission Requirements

• Graduate record examination (GRE) scores (optional)
• Strong mathematical background 
• Unofficial transcripts from previous colleges

Can You Get a PhD in Data Science Online?

Yes, you can get a PhD in Data Science online. There are a few fully-online PhD programs in data science provided by schools like Northcentral University. If you wish to pursue your PhD online but haven’t been accepted into a program for data science, you can consider a computer science program that has a concentration in data science. Since data science is a subset of computer science, you would learn the same foundational skills in either program.

Best Online PhD Programs in Data Science

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

It typically takes four to five years to complete a PhD in Data Science. While four years is the standard for most schools, some programs take a fifth year to complete due to the exhaustive research conducted. Most of the programs we’ve covered above require students to complete between 70 to 80 credits.

While that only requires between eight and 10 credits per semester, students’ schedules are filled with doing research, being a teacher’s assistant, and completing a fellowship. The amount of coursework required, the research component, and the dissertation are all factors that can affect the time it takes to earn a PhD in Data Science.

Is a PhD in Data Science Hard?

Yes, a PhD in Data Science is hard as it involves taking incredibly technical classes and conducting your own novel research within data science. The academic discipline is the merging of computer science, statistics, operations research, and more, meaning that successful students must be proficient in a wide range of technical skills.

How Much Does It Cost to Get a PhD in Data Science?

It costs, on average, $19,314 per year to get a PhD in Data Science , according to the National Center of Education Statistics. The cost will change depending on the type of school a student attends. If a doctoral student studies at a public university, the tuition is only $12,171, on average. By contrast, if a doctoral student studies at a private institution, tuition costs about $14,208 at for-profit universities, and $27,776 for nonprofit universities.

How to Pay for a PhD in Data Science: PhD Funding Options

There are many different avenues students can look into to pay for a PhD in Data Science. Some schools, such as Yale and Kennesaw State University, waive tuition for eligible students, and might even give students a yearly stipend. Another common option is to do an assistantship, in which you’d work within your data science department by teaching or doing research.

Students can also apply for various scholarships or grants to help cut down the cost of tuition. While scholarships for undergraduate students are typically merit-based, PhD funding is achieved through a student’s specific field, supporting their research and cutting tuition costs.

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What Is the Difference Between a Data Science Master’s Degree and PhD?

The difference between a data science master’s degree and PhD is that the former program only takes about two years to complete, while the latter is the educational step past a master’s degree that takes at least four years to complete. In fact, the first two years of a PhD program are usually a master’s degree program.

As such, some schools prefer applicants to have master’s degrees to cut down on the length of time. A master’s degree, and the first two years of a PhD program, are more so classroom-based. For PhD students, this is when students learn the foundations they’ll need to conduct their own research in the final two years of their program. 

Master’s vs PhD in Data Science Job Outlook

The job outlook for people with a Master’s or PhD in Data Science is very positive. Data science is a new scientific field, so workers within its industries are in high demand. For example, computer and information research scientists , which have a minimum requirement of a master’s degree, should see their careers grow by 22 percent between 2020 and 2030, according to the US Bureau of Labor Statistics (BLS).

Medical scientists , which have a minimum educational requirement of a PhD, should see job growth of 17 percent between 2020 and 2030. While The PhD job outlook is lower in this instance, a PhD is highly desirable, which is evident by the salary discrepancy below.

Difference in Salary for Data Science Master’s vs PhD

A PhD typically leads to a higher salary than a master’s degree. For example, the US Bureau of Labor Statistics reports that computer and information research scientists average $131,490 per year, while medical scientists in the scientific research and development services industry make $129,800. By contrast, those with a master’s degree tend to earn an average salary of $106,000 , according to PayScale.

While the BLS states that the minimum educational requirement for that job is a master’s degree, typically those with master’s degrees work in analyzing existing data. With a PhD, you can conduct research within this innovative field.

Related Data Science Degrees

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Why Should You Get a PhD in Data Science?

You should get a PhD in Data Science because you will be one of the pioneering leaders in this budding field. While a master’s program teaches students how to analyze data, a PhD program empowers students to do their own research. Read below for why you should get a PhD in Data Science.

Reasons for Getting a PhD in Data Science

• Lead the Field. Data science is a new field, so those that get a doctoral degree will be at the forefront of new developments. As you’ll be analyzing data, it’s incredibly exciting to think that your PhD research will be groundbreaking.
• Ever-evolving job: Technology is constantly advancing at incredible speeds, so being the one to learn about these advancements will never get old. With artificial intelligence technologies on the rise, one can only imagine in just 10 years’ time how different our understanding of data science will be.
• Specialize in interest. As students go along their educational paths, they go from learning foundational knowledge to increasingly specific information. Thus, if you’re passionate about a subset of data science but didn’t get to focus on it in your bachelor’s or master’s degree program, a PhD is the perfect opportunity to study, research, and work within your interests.
• High salaries. As the field of data science grows, the need for data science experts will also increase. PhD graduates will be uniquely equipped for the industry’s changing landscape and will be highly sought-after.
• Research opportunities. While this is an enriching hands-on experience, it lays the groundwork for you to be able to conduct your own studies in the latter two years of your program. You will be able to follow your passions rather than just helping a faculty member succeed in their work.
• Job Market. The BLS projects that job openings in computer and information research sciences will grow by 22 percent from 2020 to 2030. In getting a PhD, you will be a more competitive applicant than those with a lesser degree. It’s likely that you can even negotiate higher salaries because of your specialties.

Getting a PhD in Data Science: Data Science PhD Coursework

Data science is an interdisciplinary field, involving bioinformatics, computer science, statistics, and operations research. As such, the coursework PhD students undertake is diverse, including data mining, bioinformatics, ethics, and data visualization. Below, we’ll discuss some of the common classes found throughout most PhD programs in data science.

Introduction to Data Science

This class will teach you the baseline information you’ll need to know to advance in your data science career. Since you’re in an advanced degree program, you’ll likely be working with real data from case studies. You’ll take that information and learn how to build and manage databases, visualize data, and run statistical analyses.

Data Mining

Raw data, though important, isn’t useful until it can be contextualized and analyzed. Data mining is also called “knowledge discovery,” meaning that mining is the process of digging through mounds of data to learn information. Students will code, select and visualize data, use machine learning, and clean information to make novel findings.

Bioinformatics

As the combination of terms implies, bioinformatics is where biology and informatics meet and involves the study of biological data. This field of study is essential for those that want to go into medicine as data scientists. If you haven’t yet completed your bachelor’s degree, pursuing one of the best undergraduate degrees in bioinformatics is a wise choice.

Ethics of Data Science

Since data science involves collecting and storing information, mostly on people, there are possible moral ramifications to this. Within an ethics class, you’ll learn the proper methodology for conducting research to assure that your work meets codes of conduct.

Data Visualization

An important aspect of conducting research is being able to articulate your discoveries. Through visualization in programming languages like R, you’ll learn how to plot data and make reports. This process helps you organize your findings as well as snuff out any errors made during computation.

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How to Get a PhD in Data Science: Doctoral Program Requirements

You can get a PhD in Data Science by meeting your chosen university’s degree requirements. Though these can vary, there are commonalities across different schools, such as completing a set number of credits, taking exams, and crafting a dissertation. We’ll now go into more detail about these common components of the doctoral degree.

Data Science PhD programs typically require the completion of 70 and 80 credit hours. This is often split down the middle, with the first half of credits being done in a classroom, and the latter half being done through your research and dissertation. 

While full-time undergraduate students take 15 or so credits per semester, the number of PhD students is lower as they conduct work outside of classroom hours through assistantships. Candidates typically complete this degree studying full-time for four to five years, taking between eight and 10 credits per semester. Most schools have a cap on the maximum number of years a student has to complete their PhD. For example, University of Nevada’s maximum allowance is eight years.

Before entering a PhD program, students already have a bachelor’s or master’s degree in a relevant STEM discipline. With the basics in coding languages, a statistical method, calculus, and engineering out of the way, doctorate students can take a deep dive into more difficult and focused courses. Some examples of classes PhD students will take include machine learning, data visualization, and bioinformatics. 

Often students will be able to choose a specialization to narrow the focus of their research. This allows them to take more niche classes on topics like asymptotics, stochastic processes, and Bayes theorem. After completing two years of classwork, students then begin their dissertation studies.

In order to be considered candidates for a PhD, students will need to pass exams between the first and third year of their degree program. The tests, which often consist of a verbal exam and written assessment, determine what the candidate-hopefuls have learned so far and whether they will be effective researchers with the school’s department.

After completing two years of coursework and passing their qualifying exams, PhD candidates begin research for their eventual dissertations. Candidates collaborate with a chosen faculty member to help guide them in their approved topic of study. Students then write about their findings in a dissertation or thesis, which they will need to defend in front of a committee before being considered doctors. 

Potential Careers With an Data Science Degree

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PhD in Data Science Salary and Job Outlook

The salary and job outlook for those with a PhD in Data Science is very positive. The field of study is fairly new, and while there might not be many jobs that specifically require a PhD, having a terminal degree will make you a competitive applicant for prospective employers.

Data science PhD holders can work as medical scientists in the scientific research and development services industry or as information and computer research scientists. For the former industry, which requires a PhD, there will be a 17-percent increase in job openings between 2020 and 2030, per the US Bureau of Labor Statistics (BLS). The US BLS reports that computational and informational research sciences will see a 22-percent increase in job openings during that decade. Average salaries for those professionals are $116,430 and $131,490, respectively.

Though the salary for those in computational and informational research sciences is higher and typically only requires a master’s degree, those with a PhD are more likely to work in those positions. This is because PhD holders often conduct research, having done so in their doctoral programs, while former graduate students often analyze existing findings instead.

What Can You Do With a PhD in Data Science?

With a PhD in Data Science, there are a plethora of jobs within reach . The field of study is interdisciplinary, meaning that you’d be equipped with the skills to thrive in careers relating to computer science, bioinformatics, engineering, data management, and so much more. Let’s discuss further some of the highest-paying jobs that you can get with a PhD in Data Science.

Best Jobs with a PhD in Data Science

• Computer and Information Research Scientist
• Mathematicians/Statistician
• Medical Scientist
• Machine Learning Engineer
• Data Scientist

What Is the Average Salary for a PhD in Data Science?

The average salary for a PhD in Data Science is around $131,000. Payscale reports this is the average salary for those with a PhD in Computer Science, and since data science is a specialization of computer science, one can infer the salaries would be similar.

While $131,000 can be the expected salary for those with a Doctorate in Computer Science—and data science, by extension—the average salary you might earn will depend on a few variables. These include the amount of work experience you have, the industry you are working in, the organization you are working for, and the region of the country you are working in.

Highest-Paying Data Science Jobs for PhD Grads

Best Data Science Jobs with a Doctorate

The best data science jobs with a doctorate are as a computer and information research scientist, mathematician or statistician, medical scientist, machine learning engineer, or data scientist. All of the above careers earn over $100,000 per year, but the actual salary a job might offer can differ.

These professionals are found across health care, corporate, and scientific fields and work to optimize the computer systems for their organization. This is done through distilling overly-complicated algorithms, troubleshooting issues with other engineers, and conducting research into developing new electronic programs. 

Though these jobs usually have a minimum education requirement of a master’s degree, those with a PhD are likely to also populate this sector and will likely be given preference by employers. This is because those with a PhD conduct research more frequently than those with a master’s degree, who usually analyze existing data. 

• Salary with a Data Science PhD: $131,490
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Texas, Arizona, Washington, Massachusetts

While both statisticians and data scientists analyze sets of information, a difference between the two is that while the field of computational statistics can be broad, data science is more focused on computer science and machine learning. They do, however, use the same methodology for analysis, so with your PhD in Data Science, you’d be equipped to be a statistician in a variety of industries.

According to the US Bureau of Labor Statistics, statisticians and mathematicians that work in technical, professional, and scientific services make on average $129,800, and those that work in life, engineering, and physical science development and research earn $114,770 per year. 

• Salary with a Data Science PhD: $129,800
• Job Outlook: 33% job growth from 2020 to 2030
• Number of Jobs: 44,800
• Highest-Paying States: Connecticut, New York, Massachusetts, Wyoming, and California  

Working in what the BLS calls the “ scientific research and development services industry ,” you could use your data science know-how in the medical field, especially if you concentrated or did your dissertation in health care. You’d likely data mine through patient information, analyzing it to then make recommendations to those in your health system organization. 

• Salary with a Data Science PhD: $116,430
• Job Outlook: 17% job growth from 2020 to 2030
• Number of Jobs: 133,900
• Highest-Paying States: Maine, New Jersey, Tennessee, Connecticut, Delaware

Artificial intelligence is growing alongside the data science field. Pursuing this career would allow you to be able to help foster artificial intelligence (AI) programs. You’d code your own AI system, teaching it how to analyze large amounts of data and how the system should respond to it. 

• Salary with a Data Science PhD: $112,709
• Job Outlook: 22% job growth from 2020 to 2030 
• Number of Jobs: 33,000 (for computer and information research scientists) 
• Highest-Paying States: Oregon, Texas, Arizona, Washington, Massachusetts (for computer and information research scientists) 

Those that work within this field combine their knowledge of informatics, computer programming, data mining and management, and more to conduct research by studying data sets. Data Scientists can work in business, devising avenues to optimize profits by looking at reports.health care, presenting their studies to guide decisions bettering the medical system; and academia, working to innovate this budding field of study. 

• Salary with a Data Science PhD: $108,660
• Number of Jobs: 105,980
• Highest-Paying States: New Jersey, New York, Delaware, Washington, California

Is a PhD in Data Science Worth It?

Yes, a PhD in Data Science is much worth it. Though not all data science jobs require a PhD, with some upper-level careers only requiring a master’s degree, you would have an advantage over others with lower levels of education. You’d have experience conducting your own research method, which would prepare you for running your own studies in the real world.

Most with master’s degrees don’t actually develop their own studies, rather, they analyze existing information. A PhD would give you a competitive edge, making you a more impressive candidate to prospective employers. You’d be more likely to get hired, and more plausibly able to negotiate a higher salary.

Data science is a new field of inquiry, so by having a doctorate in it, you would be at the forefront of the technological advancements within the industry. You would likely make at least $100,000 yearly in data science and have the interdisciplinary skills to work in other industries if you desire.

Additional Reading About Data Science

[query_class_embed] https://careerkarma.com/blog/introduction-to-data-science/ https://careerkarma.com/blog/how-to-get-a-job-in-data-science/ https://careerkarma.com/blog/data-science-degree/

PhD in Data Science FAQ

Yes, many data scientists have PhDs, but it is not a requirement for many jobs in the industry. Some require only master’s degrees instead, but there are advantages to having a PhD. With a PhD, you’ll have conducted your own research to get your doctorate, allowing you to more easily create your own studies in the real world. Those with a master’s degree, by contrast, usually only analyze existing studies, giving you less creative liberties with what you work on.

There are many career options for those with a PhD in Data Science, as the course of study is interdisciplinary. This means that those with the degree are taught technical skills that can apply to multiple different industries. Often, those with a data science degree go on to work in either business, medicine, or academia.

The US Bureau of Labor Statistics (BLS) reports that the median annual salary for data scientists is $131,490 . This report states that those within this job often have a master’s degree, meaning that with your PhD, you’d be a more impressive candidate and be able to negotiate a higher salary. Additionally, the BLS estimates that demand for data scientists will grow by 22 percent from 2020 to 2030, a much higher rate than the national average of eight percent.

A PhD is the best degree to become a data scientist if you want to conduct your own studies. Many within data science have lesser degrees, usually a master’s and sometimes a bachelor’s, which in turn gives them less responsibility within the field. If you have a scientific inquiry that you are passionate about and want the freedom to study what you want, a PhD is the best degree to obtain.

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Stuart Davie and Tom Hassall

Do i need a phd to be a data scientist, i want to be a data scientist. should i do a phd.

There’s a lot of conflicting advice out there about whether or not you should do a PhD to get into data science. Some of this stems from the fact that “data science” is a relatively new label, which is used to describe the intersection of many different fields, previously considered to be separate. 

In short, the answer is…it depends.

A PhD is a great way to get deep exposure to a number of core data science domains. That said, because the field of data science is moving so fast, experience is often more important than a degree – and the actual research of a PhD might be outdated in a relatively short amount of time (although the experience of a PhD will certainly be useful regardless, especially if you want to get into research at some point!)  

At Peak, you definitely don’t need a PhD. We look for skills, values, and potential for data science when we are recruiting, rather than purely at your academic achievements. It is hard to know what the field will look like in five years, so we want adaptable people who are enthusiastic about change. 

We know that good data scientists can come from anywhere, and this is reflected in the diversity of our data science team’s academic backgrounds. In our team, just over half have a PhD. Just under half have a Master’s degree, and 5% of the team have neither.

The academic backgrounds of the Peak team are also quite varied. In fact, 82% of the team didn’t study data science specifically. Any subject that teaches coding, maths, and technical research can act as a great spring-board for a career in data science. Maths, the Physical Sciences, and the Computer Sciences are a few academic domains with a particular focus on these skills – and the bulk of our team are from these sorts of backgrounds. That said, the team at Peak covers quite a broad range of disciplines – everything from Astronomy to Neuroscience!

As detailed in another recent blog post on landing a first job in data science , at Peak we want data scientists who are good at problem solving, coding, and maths. Doing a PhD in a relevant field is a good way to learn some of these skills, but there are other avenues (such as a Master’s in data science or a graduate scheme) available too. 

Why did you choose a PhD?

There was a large range of responses to this question. It is fairly common for a PhD to be a next step for people who are not sure what they want to do, and enjoy studying. To some people, it feels like a natural extension – they love their area of study and a PhD allowed them to really follow their curiosity! 

Some members of the team had planned for a PhD since they knew what a PhD was, and targeted specific research groups known to be the best in the field. Others saw a PhD as a good way to change the direction of their career.

What was the best thing about doing a PhD?

PhDs are a great way of learning a lot of skills independently. You will learn project management, teamwork, coding, statistics, and you should become an expert in your particular field. On top of the extra skills, a PhD itself can make your CV look a lot better, and can make you stand out from the crowd as you will get to add extra letters before or after your name (something that proved to be a strong motivator for people in the team while doing their PhD, but also something they stopped using pretty quickly!). 

Some of the opportunities you will get during a PhD, such as public speaking, and solving really hard problems, can also improve your confidence.

On top of that, for many, day-to-day PhD life can be really fun. As well as continuous learning, PhDs often involve a good amount of travel, can sometimes have a lot of social interaction with people who have similar interests, and often grant the freedom to work the hours that you choose to, take holidays as you like, and work remotely as much as you want!

What was the worst thing about doing a PhD?

It might be surprising to learn that not every data scientist at Peak enjoyed their PhD, and some actually left mid-degree to pursue something else. In fact, there were four main challenges faced during a PhD that were consistently raised by the team. These were:

• A feeling of a lack of impact
• A change in career direction

PhDs can be hard, and stressful. When you are writing up a PhD, it can take a few months of working 12-hour days (including weekends.) This work will usually fall entirely on you, with nobody to help. Unlike a regular work environment, where there can be a lot of small things due regularly, a PhD will often have very large things due less regularly, which can make the deadlines much higher pressure when they do come along.

Often, you and your supervisor will be the only people in the department working on the specific problem of your PhD. If your supervisor leaves mid-degree, or your communication with them breaks down for some reason, you can feel very isolated. It can be very difficult to find support within the university system sometimes, so having a good personal support network can be key to whether you enjoy your PhD.

Unfortunately, many PhDs have little tangible impact, especially while the degree is being completed. Newton spoke evocatively of standing upon the shoulders of giants, but in many ways the biggest giant in modern science is a bunch of regular sized people on top of each others’ shoulders in an oversized trench coat! In particular, in niche fields, or fields with few practical applications, the years of hard work somebody puts into a PhD could result in a piece of work that is only read by a few people.

Career direction

Often, it takes until halfway through a PhD for people to decide that the academic career path is not for them. The PhD itself gives a better view of what academic life is like than what an undergraduate student experiences, which can be enough to help people change their mind. This is especially true in data science, where a lot of research and innovation is being driven within industry. In fact, we have several data scientists in the team who left their degrees early so they could get into industry faster! 

So, which option is right for you? We asked the team to share their thoughts…

General advice

If you do plan to complete a PhD in before moving into the data science industry, here is some advice from the team .

? Be prepared, and make sure you are motivated enough to finish. A PhD can get very difficult towards the end. Final years are often filled with 12-hour days, six or seven days a week.

? A PhD is not necessarily the best thing for a career afterwards, especially in data science. There are usually other paths available to the job you want which might be better, especially if the PhD project available to you isn’t a perfect match for the role you want. Don’t do a PhD just because it’s the next natural step.

? Make sure you get on with the supervisor, and have a passion for the subject. A lot of people who end up struggling with their PhDs do so because they don’t have a good working relationship with supervisors. Definitely try and meet them beforehand.

? Make sure you join a good research group – it makes travelling to conferences much more fun. This isn’t just ‘good’ in the academic sense either (though that might help.) If there is an opportunity to chat with the research group informally beforehand, it can be helpful to get a feel for whether you might enjoy working with them!

? Make sure you are comfortable with being a student for at least another three years. That means less disposable income than some of your peers, and potentially delaying other life events a bit longer too. Since a PhD is research-based, be prepared for the possibility of everything going wrong, having to change direction part way through, and the degree taking an extra two years on top of what you originally planned (even if funding has ran out)!

? Be wary of unfunded PhDs. Given all the work pressure, doing it without getting paid just adds another layer of stress that may be better avoided. If it’s something you are incredibly passionate about it may be worth it, but really think carefully before committing.

? If you are looking for a funded option but don’t exactly know what to do, the Centres for Doctoral Training (usually called CDTs) can be a great place to start. They offer fully funded MSc + PhDs, so you get a year to figure out what you want to do during your MSc while getting paid to do it. The CDT helps you be near potential supervisors, have talks with each of them, and know the departments of the university, before you make a decision. A CDT is particularly useful if you don’t know the university from the inside. The downside to a CDT is that they usually have set intakes, so you will need to delay the PhD by six to 12 months, but it may be worth it.

⚔️ PhDs are often very flexible around work hours and remote working. This can be a double-edged sword. On the upside, it is much easier to set your own work schedule, and work at times and in a place that suits you. On the downside, holidays aren’t normally tracked at all, and some parts of academia have something of a culture of overwork (including taking too few holidays!) This often means research students don’t take enough time off either.

❤️ If you are choosing to be somewhere for a few years with very little money, make sure it is a beautiful place, with people you like, doing something you love.

Choosing to do a PhD in a relevant subject area can be an excellent choice for people who want to learn independently. Over the course of your PhD, you will likely teach yourself a lot of skills that will stand you in good stead for the commercial world, including how to code, how to research, how to read and write technical documents, and how to solve problems. A PhD is also a chance to travel, meet interesting people, and to ultimately complete a technically demanding research project.

On the other hand, PhDs can be very stressful, especially near the end. Self-learning can be challenging, and the whole degree can feel very isolating (especially if there are difficulties with your supervisor). PhDs generally have a very narrow technical focus, which can lead to a feeling that your work has no impact. Finally, it will delay you from starting your data science career, which might frustrate you if you aren’t completely sold on the PhD itself. 

Compared to a PhD, a Master’s degree is much quicker to complete, meaning you can typically get a job quicker. Master’s degrees expose you to a broad range of data science techniques, with learning in a much more structured environment, so you are less likely to ‘miss’ an important lesson or concept than you might be in a PhD. You will also get a good working knowledge of all areas of data science.

The problem with Master’s programs is that they often don’t have time to explore the application of techniques to real problems and real data, or to build highly complex systems. These are critical skills for any data scientist (but can be gained in other ways too, such as by building up a portfolio of data science projects.)

So, should you do a PhD if you want to be a data scientist? In short, the answer is…it depends. But, hopefully this blog has helped you decide which path best suits you!

Stuart is Head of Data Science at Peak, and Tom is a Data Science Team Leader. Got a question for them about landing a job in the industry? You can connect with Stuart on LinkedIn here , and find Tom here.

? We're currently looking for budding data scientists to join our Peak Data Science Graduate Scheme! For more information on this scheme, data science roles at Peak, or to learn about our Data Science Mentoring programme, get in touch with our People team.

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Home / Data Science Programs / PhD in Data Science

Data Science PhD Programs

If you’re passionate about big data and interested in an advanced degree, you may be wondering which degree is right for you. Should you go with a Master of Science (M.S.) or a PhD in data science?

Our guide to getting a PhD in data science is here to help. Here, we’ll break down potential pros and cons of choosing either option, related job opportunities, dissertation topics, courses, costs and more.

SPONSORED SCHOOLS

Syracuse university, master of science in applied data science.

Syracuse University’s online Master of Science in Data Science can be completed in as few as 18 months.

• Complete in as little as 18 months
• No GRE scores required to apply

Southern Methodist University

Master of science in data science.

Earn your MS in Data Science at SMU, where you can specialize in Machine Learning or Business Analytics, and complete in as few as 20 months.

• No GRE required.
• Complete in as little as 20 months.

University of California, Berkeley

Master of information and data science.

Earn your Master’s in Data Science online from UC Berkeley in as few as 12 months.

• Complete in as few as 12 months
• No GRE required

info SPONSORED

Just want the schools? Skip ahead to our  complete list of data-related PhD programs .

Why Earn a PhD in Data Science?

A PhD in Data Science is a research degree designed to equip you with knowledge of statistics, programming, data analysis and subjects relevant to your area of interest (e.g. machine learning, artificial intelligence, etc.).

The keyword here is  research . Throughout the course of your studies, you’ll likely:

• Conduct your own experiments in a specific field.
• Focus on theory—both pure and applied—to discover why certain methodologies are used.
• Examine tools and technologies to determine how they’re built.

PhD Benefits vs. Downsides

There are a number of benefits and downsides to earning a PhD in data science. Let’s explore some of them below.

Benefits of a PhD in Data Science

In a PhD in data science program, you may have the opportunity to:

• Research an area in data science that may potentially change the industry, have unexpected applications or help solve a long-standing problem.
• Collaborate with academic advisors in data science institutes and centers.
• Become a critical thinker—knowing when, where and why to apply theoretical concepts.
• Specialize in an upcoming field (e.g.  biomedical informatics ).
• Gain access to real-world data sets through university partnerships.
• Work with cutting-edge technologies and systems.
• Automatically earn a master’s degree on your way to completing a PhD.
• Qualify for high-level executive or leadership positions.

Downsides of a PhD in Data Science

On the other hand, some PhDs in data science programs may:

• Take four to five years on a full-time schedule to complete. These are years you could be earning money and learning real-world skills.
• Be expensive if you don’t find or have a way to fund it.
• Entail many solitary hours spent reading and writing
• Not give you “on-the-job” knowledge of corporate problems and demands.

Is a PhD in Data Science Worth It?

A PhD in data science may open the door to a number of career opportunities which align with your personal interests. These include, but aren’t limited to:

• Data scientist.   Data scientists  leverage large amounts of technical information to observe repeatable patterns which organizations can strategically leverage.
• Applications architect.  When you work as an applications architect, your main goal is to design key business applications.
• Infrastructure architect.  Unlike an applications architect, infrastructure architects monitor the functionality of business systems to support new technological developments.
• Data engineer.   Data engineers  perform operations on large amounts of data at once for business purposes, while also building pipelines for data connectivity at the organizational level.
• Statisticians :  Statisticians  analyze and interpret data to identify recurring trends and data relationships which can be used to help inform key business decisions.

At the end of a day, whether a data science PhD is worth it will be entirely dependent upon your personal interests and career goals.

Do You Need a PhD to Land a Job?

In most cases, you don’t need a PhD in data science to land a job. Most  computer and information research-related careers  require a master’s degree, such as an  online master’s in data science .

As you begin your search, pay attention to prospective employers and qualifications for your desired position:

• Companies and labs that specialize in data science—and tech players like  Amazon  and  Facebook  — may have a reason for specifying a PhD in the education requirements.
• Other industries may be happy with a B.S. or M.S. degree and relevant work experience.

Careers for Data Science PhD Holders

People who hold a PhD in data science typically find careers in academia, industry and university research labs,  government  and tech companies. These places are most likely seeking job candidates who can:

• Research and develop new methodologies.
• Build core products, tools and technologies that are based on data science (e.g.  machine learning  or  artificial intelligence  algorithms for Google or the next generation of  big data management systems ).
• Reinvent existing methods and tools for specific purposes.
• Translate research findings and adopt theory to practice (e.g. evaluating the latest discoveries and finding ways to implement them in the corporate world).
• Design research projects for teams of statisticians and data scientists.

Sample job titles include:

• Director of Research
• Senior Data Scientist/Analyst
• Data/Analytics Manager
• Data Science Consultant
• Laboratory Researcher
• Strategic Innovation Manager
• Tenured Professor of Data Science
• Chief Data Officer (CDO)

PhD in Data Science Curriculum

Typical Program Structure Data science PhDs are similar to most doctoral programs. That means you’ll typically have to:

• Complete at least two years of full-time coursework.
• Pass a comprehensive exam—comprising oral and written portions—that shows you have mastered the subject matter.
• Submit a dissertation proposal and have it approved.
• Devote 2-3 years to conducting independent research and writing your dissertation. You may be teaching undergraduate classes at the same time.
• Defend your work in a “dissertation defense”—usually an oral presentation to academics and the public.

During these years, you’ll likely engage in professional activities that may help improve your career prospects. Such opportunities include attending and speaking at conferences, applying for summer fellowships, consulting, paid part-time research and more.

Dissertation

PhD students are expected to make a creative contribution to the field of data science—that means you’re encouraged not to go over old ground or rehash what’s already out there. Your contribution will be summed up in your dissertation, which is a written record of your original research.

Some students go into a PhD program already knowing what they want to research. Others use the first couple of years to explore the field and settle on a dissertation topic. Your advisor may be your closest ally in this process.

Data Science vs. Business Analytics vs. Specialties

Doctoral programs in data science may also fall under the related disciplines such as statistics,  computational sciences  and informatics. It is important to evaluate each program’s curriculum. Will the foundation courses and electives prepare you for the research area that you want to explore?

A related degree you may consider is a PhD in Business Analytics (or Decision/Management Sciences). These degree programs are typically administered through a university’s School of Business, which means the curriculum includes corporate topics like management science,  marketing , customer analytics, supply chains, etc.

Interested in a particular subset of data science? Some universities offer specialty PhD programs. Biostatistics and biomedical/health informatics are two examples, but you’ll also find a number of doctoral programs in machine learning (usually run by the Department of Computer Science) and sub-specialties in fields like artificial intelligence and data mining.

Considerations When Choosing a PhD Program

Typical Admissions Requirements PhD candidates typically submit an application form and pay a fee. Universities often look for applicants who have:

• A  Bachelor of Science (BS) in computer science , statistics or a relevant discipline (e.g. engineering) and a similar master’s degree with an official transcript from an accredited institution
• A GPA of 3.0 or higher on a 4.0 scale
• GRE test scores
• TOEFL or IELTS for applicants whose native language is not English
• Letters of recommendation
• Statement of purpose/intent
• Résumé or CV

If you don’t already have certain skills (e.g. stats, calculus, computer programming, etc.), the university may ask you to complete prerequisite courses.

Programs for PhD in Data Science – Online vs. On-Campus Online programs may require you to attend a few campus events (e.g. symposiums), but allow you to complete coursework and conduct research in your own hometown.

While online learning can be a convenient way of obtaining your PhD from the comfort of home, there are a few important factors to consider.

• Are you  extremely  passionate about an area of research?
• Do you mind committing to 4-5 years of study?
• Does your university have funding sources (private and government) for data science research?
• Will you have access to exciting data resources, labs and industry partners?
• Do you know how you’re going to pay for the program?

How Much Does a PhD Cost?

As you research PhD in data science programs, you’ll probably find information on relevant fellowships on some university websites, as well as advice on financial matters. Here are a few ways that you may be able to fund your education:

• PhD Fellowships:  You’ll find a number of fellowships sponsored by the university, by companies and by the government (e.g. National Science Foundation). Be aware that some external fellowships will only cover the years of your dissertation research.
• Teaching/Research Assistantships:  Assistantships are a common way for universities to support PhD students. In return for teaching undergraduates or working as a researcher, you’ll often receive a break on tuition costs and a living stipend.
• In-State Tuition : Public universities may offer in-state students a much lower cost per credit.
• Regional Discounts:  Many state universities have agreements to offer reduced tuition costs to students from neighboring states (e.g.  New England Board of Higher Education Regional Student Program (RSP) . Check to see if this applies to your PhD.
• Travel Grants:  Doctoral students may have the opportunity to attend research conferences and network with future collaborators. Some grants are designed with this purpose in mind.
• Student Loans:  In addition to grants, you can consider applying for student loans to finance your PhD studies. Remember, a doctorate is a long-term commitment—you may not see a financial return on your education for a number of years.

Some PhD students in data science are  fully funded . For example:

• U.S. citizens and permanent residents in  Stanford’s PhD in Biomedical Informatics  are funded by a National Library of Medicine (NLM) Training Grant and Big Data to Knowledge (BD2K) Training Grants

If you’re coming from overseas, try talking to your school about any differences between funding for citizens and international students.

How Long Does a PhD in Data Science Take?

The length of time it takes to obtain a PhD will likely vary depending on your chosen program. Programs for similar or identical degrees can have differing completion requirements at different schools, meaning how many years your PhD program takes will differ as well.

Of course, the amount of time you spend working toward a PhD in data science can also vary depending on whether you choose to take it part-time or full-time. Assuming you consistently pass your classes, a full-time commitment to your PhD program will expedite your way through it.

But a commitment like that won’t fit everyone’s lifestyles. For example, you might need to work to support yourself financially, or you might be raising a family. These sorts of important commitments are time-consuming and can take a lot of energy. So, in that case, a part-time commitment to your PhD program might make more sense for you.

Interested in STEM Careers? 

If you’re looking for information on  career paths that involve STEM , see our guides below:

Data Science and Analytics Careers:

• Data Scientist
• Data Analyst
• Business Analyst

Computer Science, Computer Engineering and Information Careers:

• Computer and Information Research Scientist

Marketing and User Research Careers:

• UX Designer  

Compare Careers and STEM Fields:

• Cybersecurity vs. Computer Science

Related Graduate STEM Degrees

• Master’s in Business Analytics
• Master’s in Information Systems
• Master’s in Computer Engineering
• Master’s in Computer Science  
• Master’s in Cybersecurity Programs
• Master’s Applied Statistics
• Master’s in Data Analytics for Public Policy
• Data Science MBA Programs
• Master’s in Geospatial Science and
• Geographic Information Systems
• Master’s in Health Informatics
• Master of Library and Information Science

Related Undergraduate STEM Degrees

• Online Bachelor’s in Data Science
• Sponsored:  Computer Science at Simmons

PhD in Data Science School Listings

We found 57 universities offering doctorate-level programs in data science. If you represent a university and would like to contact us about editing any of our listings or adding new programs, please send an email to [email protected].

Last updated August 2021. The program’s website is always best for most up to date program information.

PhD in Data Science/Analytics Online

Looking for on-campus programs? See the  full list of on-campus PhD in Data Science/Analytics programs .

Colorado Technical University

Doctor of computer science – big data analytics, colorado springs, colorado.

Name of Degree: Doctor of Computer Science – Big Data Analytics

Enrollment Type: Self-paced

Length of Program: 4 years

Credits: 100

Admission Requirements:

Carnegie Mellon University

School of computer science, ph.d. program in machine learning, pittsburgh, pennsylvania.

Name of Degree: Ph.D. Program in Machine Learning

Enrollment Type: N/A

Length of Program: 2 years

Credits: N/A

• Recent transcripts
• Statement of purpose
• Three letters of recommendation
• TOEFL scores if your native language is not English

Chapman University

Schmid college, ph.d. in computational and data sciences, orange, california.

Name of Degree: Ph.D. in Computational and Data Sciences

Enrollment Type: Full-Time and Part-Time

Credits: 70

• GRE required
• Statement of intent 
• Resume or curriculum CV.                                       
• TOEFL score for international students

Indiana University – Indianapolis

School of informatics and computing, ph.d. in data science, indianapolis, indiana.

Name of Degree: Ph.D. in Data Science

Credits: 90

• Bachelor’s degree; master’s preferred
• Transcripts
• TOEFL or IELTS

Kennesaw State University

School of data science analytics, doctoral degree in analytics and data science, kennesaw, georgia.

Name of Degree: Doctoral Degree in Analytics and Data Science

Enrollment Type: Full-Time

Credits: 78

• Statement of how this degree facilitates your career goals

PhD in Data Science/Analytics On-Campus

Looking for online programs? See the  full list of online PhD in Data Science/Analytics programs .

New York University

Center for data science, new york , new york.

Credits: 72

• Resume or curriculum CV
• TOEFL or IELTS (TOEFL Preferred)
• Statement of Academic purpose

Institute for Computational and Data Sciences

Phd computational and data enabled science and engineering, buffalo, new york.

Name of Degree: PhD Computational and Data Enabled Science and Engineering

Computational Data Sciences  

• Master’s degree
• Resume or CV
• GRE scores (Temporarily suspended)

University of Maryland

College of information studies, doctor of philosophy in information studies, college park, maryland.

Name of Degree: Doctor of Philosophy in Information Studies

Credits: 60

• Transcripts 
• Resume or CV or CV
• academic writing sample
• TOEFL/IELTS/PTE (required for most international applicants)

University of Massachusetts in Boston

College of management, doctor of philosophy in information systemaster of science for data science and management, boston, massachusetts.

Name of Degree: Doctor of Philosophy in Information SysteMaster of Science for Data Science and Management

Credits: 42

• Official transcripts official
• GMAT or GRE scores scores
• Official TOEFL or IELTS score.

University of Nevada – Reno

College of science, ph.d. in statistics and data science, reno, nevada.

Name of Degree: Ph.D. in Statistics and Data Science

Length of Program: 4+ years

• Undergraduate/Graduate Transcripts
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University of Southern California

School of business, ph.d. in data sciences & operations, los angeles, california.

Name of Degree: Ph.D. in Data Sciences & Operations

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University of Washington

Mechanical engineering, doctor of philosophy in mechanical engineering: data science, seattle, washington.

Name of Degree: Doctor of Philosophy in Mechanical Engineering: Data Science

Worcester Polytechnic Institute

Worcester, massachusetts.

• Doing a PhD in Data Science

What Is a PhD in Data Science?

If you have always been fascinated by science, especially if you are interested in statistics and the scientific method, then a PhD in Data Science might be for you.

Data science is a field of study dedicated to applying the science of statistics to the problem areas of data visualisation, data science and machine learning. In this field, the challenge is to use data analysis and mathematical formulas to predict data patterns and draw conclusions from them.

Data science has become popular because it covers a wide range of topics, including the use of statistical methods for analysing and interpreting data. The primary goal of the discipline is to explain the way data enters the scientific community and influences decisions. Data is analysed to find patterns and connections, and then possible solutions are explored. With big data and new statistical computing methods, patterns can be uncovered, and relationships can be tested.

As more and more industries rely on information generated by computers, data science will be one of the key players in the future.

Browse PhDs in Data Science

Application of artificial intelligence to multiphysics problems in materials design, study of the human-vehicle interactions by a high-end dynamic driving simulator, physical layer algorithm design in 6g non-terrestrial communications, machine learning for autonomous robot exploration, detecting subtle but clinically significant cognitive change in an ageing population, what does a phd in data science focus on.

The primary focus for a PhD in Data Science is statistical methods. This means that you would study statistics in all its forms at the macroscopic and microscopic level, including statistical computer science, theory and applied mathematics. The advantage is that you get an insight into how large-scale data works. Thus, a position in a company where you are analysing large amounts of project data can be made available through a PhD.

PhD programs in data science provide university students with a thorough grounding in the theoretical aspects , but they are also taught the practical aspects of the discipline. PhD students are taught how to conduct proper experiments and interpret the results of scientific studies.

The importance of data and its interpretation is of paramount importance in all fields, and a PhD programme in data science addresses this topic, with some institutions also offering taught modules that doctoral students can use to deepen their knowledge.

Within a data science field, there are several areas of focus. One of them is the analysis of large databases and their effective interpretation. With this doctoral qualification, you could conduct statistical analysis, research studies and even exploratory data analysis. You could see what kinds of relationships exist between variables. You can explore areas such as Databases, Human Resource Management Machine Learning, or Information Technology during your studies.

Entry Requirements for A PhD in Data Science

A PhD in Data Science involves conducting original research in this area; therefore, applicants must have a good knowledge of statistical methods, computing, probability calculation, statistics and other related topics.

Basic requirements are typically a strong Master’s degree in mathematics, computer science or statistics from an accredited university. International students will also need to meet several minimum English language requirements set by the university, usually as part of a TOEFL or IELTS exam.

Although there are many advantages to obtaining a PhD in Data Science, it requires hard work and perseverance to master the techniques of analysis; to become an effective researcher, you will need strong mathematical and logical skills.

If you are interested in a PhD in Data Science but are unsure whether you have the background or resources available, consider taking a Master’s degree in this subject, or if you are a prospective student, contact the department you are interested in to see if they have any advice for you.

Duration and Programme Types

You can earn a PhD in data science in as little as 3 years full-time or 6 years part-time at a leading university. There are also online courses; many universities offer online PhD programmes which allow you to complete your entire doctoral programme from home. You still need to meet your course requirements by attending lectures and doing laboratory work, but your work can be completed at your own pace and off-campus.

Costs and Funding

The cost of a PhD in Data Science will depend on the university you study with, but average tuition fee is £4000-£6000 per academic year for UK/EU students and £16,000-£19,000 per academic year for international students.

Due to the popularity of Data Science PhD projects and the increasing demand for individuals who can elaborately analyse large data sets , it is not difficult to obtain PhD funding in this area. In many cases, funding for full-time research can be obtained from the university’s Centre for Doctoral Training (CDT), covering tuition fees and living costs.

Available Career Paths

A PhD in Data Science will enhance your data analysis skills and allow you to specialise in areas not available to others. A PhD offers many opportunities for those interested in statistics; you could become an engineer, statistician, consultant or academic lecturer. There are even PhDs in Data Science that offer internships in financial institutions or government agencies. Upon completing your doctorate, you can enter the workforce in many areas depending on your aptitude and experience.

A PhD in Data Science can lead to a wide range of jobs in many fields. If you are interested in working for a company that uses data one way or another, a PhD would be the perfect choice for you. If you are interested in independent research and studying various scientific methods and data, you will do well with a PhD. You could also spend your time teaching or doing your own research.

A person who has a PhD in data science can work in many industry-related positions. For example, you may work in the financial industry as an analyst for mergers and acquisitions, in healthcare, as a statistician, or as an information systems administrator. You can even get a job as an IT analyst, project manager, and software designer.

You can use your knowledge in the workplace to start up your own small business. Many small businesses today are founded on the back of a PhD. In fact, many Fortune 500 companies started as a result of a doctor trying to solve a problem or answer a long-standing question plaguing their industry.

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• Last updated January 16, 2022
• In Innovation in AI

Is a doctoral degree in data science worth it? Know from PhD holders

• Published on January 12, 2022
• by Avi Gopani

Doctorate degrees have always been a mirage far too superior to achieve. But the recent trends are demystifying pursuing a Ph D. In the US, the Bureau of Labor Statistics projects a 15.3% jump in the number of jobs in CS requiring a doctorate by 2022. This trend is finding its way in India, with the top engineering institutes like IITs and BITS Pilani now offering several Ph D programs. Students are pursuing their doctoral degrees right after their master’s, but it is not reserved to them only. Many recent programs today are catered towards industry professionals that are being sponsored by their employers in collaboration with universities to pursue their Ph D after having gained work experience. 

This trend is still developing. So, Analytics India Magazine got in touch with AI experts and Ph D holders to understand the nuances of a Ph D in Computer Science and identify if it is worth it. 

Why go beyond a master’s

“Pursuing a doctorate is a four-to-five-year-long process, and a couple of years are spent only in finding the correct problem”, said Dr Pushpak Bhattacharyya . A professor at IIT Bombay, Dr Bhattacharyya, has mentored more than 300 research students, published hundreds of papers and runs a research lab at IIT-B. 

Despite the process, “Ph D is very much in demand”, Dr Bhattacharyya noted, citing the various attributes of pursuing a doctoral degree. “Ph D is important because it gives the student a long association with the disciple. They go into the foundations of the subject to establish a hypothesis. (And to do so), a huge amount of reading and research has to be done, which allows you to see the gaps in the knowledge that exists. This depth of awareness building is not done when one pursues a bachelor’s or a master’s.” 

Further discussing why a Ph D can be preferred over a master’s degree, Dr Anup Kalia , a Research Staff Member at IBM who earned his Ph D in 2016 and worked with the US Army Research Laboratory, observed, “Ph D holders are eligible for research positions in industry or academia that solely need a Ph D degree and experience in publishing papers in top-tier conferences. Bachelor’s and master’s may not be eligible for these positions.”

Why should one pursue a Ph D in Computer Science?

Be one with the discipline

A doctoral degree requires students to read through hundreds of research papers written in the field, thereby associating completely with the subject matter. It also strengthens them with skills they may not have developed during the usual theoretical education. “You learn about the fundamentals of state of the art. Ph D is important because it makes you better at reading, writing and presenting, and it helps you find a gap in knowledge and address it,” illustrated Dr Bhattacharyya. 

Recent-Ph D graduates we spoke to were all in agreement with this claim. Discussing the advantage of a Ph D over a master’s, Dr Narendra N P , who pursued his Ph D from IIT in 2016, said, “In Ph D, we would have worked on a problem independently. We know how to formulate, analyse and solve the problem. Also, we will be good at writing and presenting the work. It would be easy for us to communicate with managers and other teammates.”

Develop practical skills

One of computer science’s current challenges is the lack of representation of data scientists in the board room, mainly given the lack of presentation and other soft skills. But given the tough few years pursuing a doctorate, Ph D graduates leave their institutions with a strong character in patience and presentation, believes Dr Bhattacharya.

Dr Maneet Singh , a senior AI specialist at MasterCard, expanded on this building of soft skills, “Working on challenging problems enables Ph D candidates to hone their problem-solving skills, while failures/rejections prepare them for manoeuvring through obstacles in an otherwise ideal project plan. Repeated cycles of paper writing often result in clearer thought flow and improved communication.” Dr Singh pursued her Ph D in computer science and has since worked at MasterCard. 

Know what works

“You learn a lot during your doctoral. Even everything that goes wrong is part of learning. You understand what works and what doesn’t; this helps build expertise when you get into the industry. You develop an intuition that will help you in the industry,” said Kali Krishna Kota , a Ph D candidate at IIT-H. Kali discussed how the challenges faced during the doctoral makes one open to new ideas and equips them to better deal with new challenges and opportunities. 

Identify ‘real’ claims

The disciplines of computer science and AI are evolving rapidly, with new developments and innovations being announced every day. “The length and breadth of Ph D will help professionals understand the real value of the claims and develop their perspective accordingly. It will help them recognise which of the several claims are real”, illustrates Dr Bhattacharyya. 

Experience or Ph D?

The conundrum of the importance of experience or education in computer science is lengthy, and our interviewees reflected the same difference in opinion we see in the industry. 

“Due to the accumulation of such soft skills, I believe Ph D holders might have an advantage for industry positions focusing on cutting edge research-driven product development or business-driven research breakthroughs,” said Dr Singh. But Dr Shah and Dr Narendra N P seemed to have different views. 

Dr Narendra N P said, “In companies, a master’s with five years of company experience is considered better than a Ph D. Also, in terms of salary, a master’s with five years of experience will get a better package than a fresh Ph D.” Dr Ritesh Shah , Senior Principal Data Scientist at Jio, who pursued his Ph D in 2017, had a similar idea. “Within the industry, work experience is valued more over academic qualifications. (Therefore), work experience coupled with a Ph D in CS could be more valuable in the senior and mid-senior roles .”

Intellectual satisfaction or financial gain—why should you opt for a Ph D?

 Pursuing a Ph D is generally confined to the space of academia, but the notion is changing. We asked our Ph D graduates if the common belief of Ph D being all about academics with less financial viability is true. 

“I believe that the Ph D program offers much larger intellectual benefits as compared to the financial ones,” expressed Dr Singh. “During their Ph D, one gains in-depth knowledge about the topic of their dissertation with various opportunities to explore and grasp concepts beyond their immediate area of research. Via media of conferences, talks, and different sessions, Ph D candidates often engage in insightful conversations with leading researchers of their area of specialisation, resulting in sharper thinking and richer thought processes. While financial betterment could be a long-term byproduct of holding a Ph D degree, that might not be the immediate outcome of pursuing one.”

According to Dr Bhattacharyya, a Ph D in computer science is surely financial viable. While abroad, the state of the market says ‘higher the qualification, more is the income’; this mindset is slowly growing upward in India. 

Dr Shah believes, “A Ph D program is for those who pursue intellectual satisfaction, but there are examples where people choose to self-learn and have the right mindset to innovate without a Ph D. It’s the individual’s inclination and passion that matter. Financial betterment for a Ph D graduate is a function of the organisation they belong to. Research and development are practised successfully in very few organisations or start-ups.”

Research for corporate companies: The best of both worlds?

 An interesting trend is the merge of both worlds that occur with doctoral degrees; academia and industry. Dr Kalia defined this new position, “These candidates will be working as an applied research scientist to conduct research, build prototypes and work with software engineers to scale their prototypes into a real-world service or product.”

“Today, people in the industry prefer Ph D candidates because of how they approach the discipline and the out-of-box innovations,” said Kali. She discussed how the struggles and challenges of working on the doctorate enable Ph D graduates to hold a unique understanding of the subject matter with a proactive approach. 

Dr Kalia’s experience is similar, pursuing the degree with the goal of research positions in top-tier companies, “I want to be reasonably compensated along with the pursuit of research.” Talking about the common perspective he has observed in his colleagues, Dr Kalia said, “I have a few colleagues who have left research positions in pursuit of software engineering positions (because of the) motivating compensation in top-tier companies and the ability to work on something that could impact billions of people.” But, on the contrary, he has come across students that have pursued research for the sole sake of intellectual satisfaction. “These are the people who have even left jobs in some top-tier companies to work in academia (because) in academia; you get more flexibility and resources in terms of pursuing your own topic and being well-known in that,” he said. 

So, is it worth it? 

When asked if their Ph D was worth it, all five of our interviewees certainly found it to be worth it for them. Yet, it is a subjective decision, as both expressed through their experience. 

“It’s a choice for someone who wants to explore”, expressed Dr Kalia. “When I joined my master’s program, I had two years of experience working for a software company. Hence, I wanted to explore further, which motivated me to pursue a Ph D. Undoubtedly, it was the best decision I have made, and my current job role is intellectually satisfying. What I observe is that a Ph D gives you that experience in turning a vague and not so well-defined problem into a more concrete problem.”

Defining who should pursue a Ph D, Dr Singh said, “If someone is excited about research, new developments in a chosen computer science field and has the passion to learn, innovate, and experiment, pursuing a Ph D degree could be a wonderful decision for them.” She also shared the other side of the perspective, discussing, “if chosen for the wrong reasons, it might end up being an unpleasant experience for the candidate and the associated people. Since a typical Ph D in CS often requires dedicated commitment and rigour of 4-5 years, it must be pursued for the right reasons only.”

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Doctor of Philosophy in Data Science

Developing future pioneers in data science

The School of Data Science at the University of Virginia is committed to educating the next generation of data science leaders. The Ph.D. in Data Science is designed to impart the skills and knowledge necessary to enable research and discovery in data science methods. Because the end goal is to extract knowledge and enable discovery from complex data, the program also boasts robust applied training that is geared toward interdisciplinary collaboration. Doctoral candidates will master the computational and mathematical foundations of data science, and develop competencies in data engineering, software development, data policy and ethics. 

Doctoral students in our program apprentice with faculty and pursue advanced research in an interdisciplinary, collaborative environment that is often focused on scientific discovery via data science methods. By serving as teaching assistants for the School’s undergraduate and graduate programs, they learn to be adroit educators and hone their critical thinking and communication skills.

LEARNING OUTCOMES

Pursuing a Ph.D. in Data Science will prepare you to become an expert in the field and work at the cutting edge of a new discipline. According to LinkedIn’s most recent Emerging Jobs Report, data science is booming and data scientist is one of the top three fastest growing jobs. A Ph.D. in Data Science from the University of Virginia opens career paths in academia, industry or government. Graduates of our program will:

• Understand data as a generic concept, and how data encodes and captures information
• Be fluent in modern data engineering techniques, and work with complex and large data sets
• Recognize ethical and legal issues relevant to data analytics and their impact on society 
• Develop innovative computational algorithms and novel statistical methods that transform data into knowledge
• Collaborate with research teams from a wide array of scientific fields 
• Effectively communicate methods and results to a variety of audiences and stakeholders
• Recognize the broad applicability of data science methods and models 

Graduates of the Ph.D. in Data Science will have contributed novel methodological research to the field of data science, demonstrated their work has impactful interdisciplinary applications and defended their methods in an open forum.

A Week in the Life: First-Year Ph.D. Student

Ph.D. Student Profile: Jade Preston

Ph.D. Student Profile: Beau LeBlond

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Ph.D. Specialization in Data Science

The ph.d. specialization in data science is an option within the applied mathematics, computer science, electrical engineering, industrial engineering and operations research, and statistics departments..

Only students already enrolled in one of these doctoral programs at Columbia are eligible to participate in this specialization. Students should fulfill the requirements below in addition to those of their respective department's Ph.D. program. Students should discuss this specialization option with their Ph.D. advisor and their department's director for graduate studies.

Applied Mathematics Doctoral Program

Computer Science Doctoral Program

Decision, Risk, and Operations (DRO) Program

Electrical Engineering Doctoral Program

Industrial Engineering and Operations Research Doctoral Program

Statistics Doctoral Program

The specialization consists of either five (5) courses from the lists below, or four (4) courses plus one (1) additional course approved by the curriculum committee. All courses must be taken for a letter grade and students must pass with a B+ or above. At least three (3) of the courses should come from outside the student’s home department. At least one (1) course has to come from each of the three (3) thematic areas listed below.

Specialization Requirements

• COMS 4231 Analysis of Algorithms I
• COMS 6232 Analysis of Algorithms II
• COMS 4111 Introduction to Databases
• COMS 4113 Distributed Systems Fundamentals
• EECS 6720 Bayesian Models for Machine Learning
• COMS 4771 Machine Learning
• COMS 4772 Advanced Machine Learning
• IEOR E6613 Optimization I
• IEOR E6614 Optimization II
• IEOR E6711 Stochastic Modeling I
• EEOR E6616 Convex Optimization
• STAT 6301 Probability Theory I
• STAT 6201 Theoretical Statistics I
• STAT 6101 Applied Statistics I
• STAT 6104 Computational Statistics
• STAT 5224 Bayesian Statistics
• STCS 6701 Foundations of Graphical Models (joint with Computer Science) 

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Ph.d. specialization committee.

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

Richard A. Davis

• Faculty of Arts and Sciences Howard Levene Professor of Statistics

Vineet Goyal

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

Garud N. Iyengar

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

Gail Kaiser

Rocco a. servedio, clifford stein.

• The Fu Foundation School of Engineering and Applied Science Wai T. Chang Professor of Industrial Engineering and Operations Research and Professor of Computer Science

John Wright

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Electrical Engineering
• Data Science Institute Associate Director for Research

DiscoverDataScience.org

Guide to Applying for a Ph.D in Big Data

By Kat Campise, Data Scientist, Ph.D.

Ph.D. programs, in general, are a strenuous undertaking. You’ll spend between 4 to 7 years, on average, in deep and highly structured research on one topic with specific writing requirements. These won’t be blogs or superficial articles waxing poetic about the trials and tribulation of AI. You’ll be expected to publish and present your research to the highest levels of academia who will undoubtedly relish (at least some scholars will) in debating — if not outright challenging — every aspect of the research you conducted.

None of this is meant to scare you away from embarking on the Ph.D. journey. Rather, this is to prepare you for many years of sacrifice and, to be forthright, stress. Ph.D. completion rates hover around 50% . However, this statistic may be more promising depending on the graduate school you choose to attend and the program you intend to complete. For example, Duke University has Ph.D. completion rates as high as 95% .

By the conclusion of your Ph.D., however, you’ll be positioned as one of the leading experts in your chosen area of research. While this doesn’t make you omniscient or omnipotent (too many scholars conflate expertise with being downright arrogant), you will have more knowledge about a given subject than those at the bachelor’s or master’s degree levels. This knowledge is granular, meaning that through your applied research, you will have accrued greater understanding of the nuances involved in the problems you’ve studied at great length.

A Ph.D. is creational. The expectation is that you’ll create or discover something new in your research area. For example, if you’re in the midst of a Ph.D. in Data Science, deriving a brand new AI system, and then discussing how you arrived at this via your dissertation — which you will defend — is what a Ph.D. program will demand of you.

Should You Apply to a Ph.D. Program?

Most Ph.D. programs require full-time study. This will leave very little room for additional employment responsibilities, e.g., having a part-time job or attempting to work full-time. You won’t merely be reading others’ research and then repeating or summarizing it. You’ll critically analyze the strengths and weaknesses of their research, and then use it to inform your research design, development, and implementation. You’re building a brand new solution to a particular problem.

Many Ph.D. programs have a stipulation that you will be part of a teaching cadre, meaning you’ll be teaching either bachelor’s or master’s level students in your discipline. This is in addition to your research and writing. While these may be paid, the teaching assignments don’t tend to be as lucrative as jobs within private industry. For instance, the Bureau of Labor Statistics reports that the median salary for data scientists is $100,910 as of May 2021. It’s extremely unlikely that you’ll earn that type of salary within your Ph.D. in Data Science program.

The flip side of this is that you can reach a six-figure salary once you complete your Ph.D. if you’re willing to take on the opportunity cost during your Ph.D. program. In fact, BLS data says the highest-earning data scientists have salaries of $167,040 or more.

So, should you apply?

If you are certain of the program, which includes having an idea as to what you want to research, you enjoy focusing on a problem (almost endlessly) and creating new solutions, and you’re willing to spend around 6 years of your life constantly reading, analyzing, writing, publishing, and presenting, then start by reviewing the next steps of the application process.

Step 1: Finalize School and Program Choice

Although there are a growing number of online programs, Ph.D. programs are still primarily an onsite experience for the sciences, technology, engineering, and math (STEM) disciplines. So, most Ph.D. applicants will need to take the university location into consideration along with the availability of the specific Ph.D. program.

Regarding program choice, ideally, you should have either a bachelor’s or master’s degree in a related discipline. In many cases, one of the application requirements is for you to have completed specific courses (or a directly relevant degree). Using data science as an example, all Ph.D. programs in data science currently require the completion of Calculus (at the very least, Calculus I), Linear Algebra, and advanced statistics.

Some programs go further and have programming requirements (Python, Java, R, etc.) along with coursework in data structures and algorithms. It’s rare to jump from a B.A. in English to a Ph.D. in Computer Science (or Data Science); not because someone isn’t capable of doing so, but due to the major “catch up” required in terms of extensive practicum in the subject. A Ph.D. is already rigorous without you needing to take a series of prerequisite courses.

Review the current professors’ research interests and publications. One of them is likely to be your advisor and you’ll need to invite others to be a part of your dissertation committee (if the Ph.D. is structured in that way). This will also help you to generate research ideas of your own while also helping your application to “connect” with the department’s goals and objectives.

Additionally, peruse the required courses. If you can find the syllabi for those courses, read through them thoroughly. Note the journals and journal articles they reference. If you can find them (many are locked away in pay for view gateways such as JSTOR, but Google Scholar may have them available for free via PDF), then start reading! Doing so will clue you in on both the professor’s research area — especially if they are an author for one or more of the articles — and the focus of both the particular course and the Ph.D. program.

Remember, the department and its constituents want a high Ph.D. completion rate (which also holds true for master’s and bachelor’s degrees). The prestige factor attracts more students and more students translate into more funding. It’s not all about the money, of course. But, they do strongly prefer candidates who will successfully complete the program and earn their Ph.D.

While you should read the program requirements carefully. Don’t hesitate to gather questions that you can’t find answers to (specifically about the program itself rather than “how do I apply”) and send an email to the Department Chair. Keep in mind that if this is in the middle of a semester, it may take them time to respond to you as they also have teaching, research, and other bureaucratic duties.

Step 2: Review the Application Process

Depending on the department’s website layout, usually, it’s pretty easy to find their “How to Apply” section. Wherever that is located, make sure you find it and review the materials you’ll need to send along with your application. Thus far, just about every U.S. university has an option for applying online (we’ve yet to find one who doesn’t accept online applications). An overwhelming majority of Ph.D. programs require the documents discussed in the steps below. As such, you’ll need to set aside additional time, and money, so that you’ll have all of the requisite materials.

Step 3: Gather Your Transcripts

All U.S. universities are going to ask for official transcripts. During the online application process, you may be asked to upload unofficial transcripts for review by their admission committee. Subsequently, the Graduate Department will request your official transcripts upon admission acceptance. If you have any gaps in education or there was a semester or two where you weren’t performing very well academically, this can be briefly (and professionally) addressed in your Statement of Interest or Letter of Intent; more will be included on this topic below.

Step 4: Test Scores

Some Ph.D. programs are moving away from the GRE testing requirement. Others will accept GMAT test results in lieu of GRE scores. But, STEM programs aren’t likely to abandon the GRE as part of the application process. You’ll need to pay close attention to any cutoff scores listed by the department and whether you should take the General GRE or its Subject Tests .

Depending on where you are located in the world, GRE fees range from $205 to $230 . Subject Tests are $150 per subject. That aside, you’ll also need to spend time in test preparation mode which can be as little as 50 hours and as high as 120 hours. Your test preparation needs are unique and depend on many different factors. Most students perform better on one section over the other, e.g., if you have a Bachelor’s Degree in Math, the Quant section may be a breeze but your performance on the Verbal section may not be as stellar.

Also, keep the application due date in mind when scheduling your GRE test. Give yourself time to retake the test if need be while also ensuring that your test scores are received by the university before the application due date.

Step 5:  Writing Samples, Resumes/CVs, and Letters of Intent

It cannot be overstated that scholarly work at the Ph.D. level requires a mind-numbing amount of writing (and research!). The department admission committee wants to determine if you can write at an academic level and if you have begun to form research interests. Essentially, they want to understand why you want to enter the Ph.D. program and how your studies will align with your career goals. All of this is part of determining not only your commitment but also your readiness.

Having industry experience is a bonus which is one of the reasons they ask for a resume or CV. As much as a Ph.D. seems to be “ivory tower” pontificating — admittedly, it can be —  students who have some hands-on experience in the particular research area tend to have more successful outcomes — as do students who have a set of clear goals and objectives.

If you don’t have an academic writing sample, then this is the time to reach out to the Department Chair to determine what you should write about for application purposes. If you’ve completed a master’s degree, you should have your thesis to send. Some departments will explicitly state what the writing sample should contain. Summarily, if for some reason you don’t have a sample readily available, be prepared to create one.

What the department committee is likely not seeking is for you to have an already formed dissertation topic. If they’re seasoned academics, as they should be, they’re keenly aware that research interests evolve over time. But, as long as you have some direction, e.g., “I’m interested in researching how AI facial recognition can be accurately and equitably deployed in determining the likelihood of criminal activity”, then you’ll have a higher probability of making it to the acceptance pile.

Step 6: Letters of Recommendation

Sometimes referred to as “Letters of Reference” department requirements vary on the number and type of recommendation letters to include with your application. Usually, you’re required to send 3. Since you’re applying for admission into academia, recommendations from prior professors are the prevailing preference. However, an increasing number of universities also accept references from employers if they can include how your employment experience has prepared you for your intended academic studies.

The “how” of routing the reference or recommendation letters differs between universities. Some will still require that the letters are sent via postal mail directly to the department. But, there’s a shift towards simply uploading the letters as a PDF directly to your online graduate application.

Remember Self Care

Your application is viewed from a holistic perspective. Although GRE scores can be part of the admission consideration equation, most universities don’t view you as merely a test score number (which is one reason some are foregoing that requirement). As mentioned elsewhere, the department does want a high graduation rate along with generating scholars who are well-regarded in their expertise. The department admission committees are aware of the blood, sweat, and tears that committing to a Ph.D. program requires.

There is a high probability that you’ll experience disorienting moments including imposter syndrome. Life doesn’t always flow smoothly and definitely doesn’t stop just because you’re in the middle of your Ph.D. in Statistics (or whichever discipline you’ve chosen). It’s perfectly feasible to speak with your advisor about taking a short break from your studies so you can enact self-care. Only you can know and determine if that’s an action (or inaction) you need to take so you can return to your program revived and ready for the next set of challenges.

2021 US Bureau of Labor Statistics salary and employment figures for data scientists reflect national data, not school-specific information. Conditions in your area may vary. Data accessed January 2023.

• Related Programs

10 Best Online PhD in Data Science Programs [2024 Guide]

If you have a passion for mining information from large amounts of data, you should consider exploring PhD Data Science online programs.

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Furthering your education in this field can help take your career to the next level. By earning your PhD, you may increase not only your knowledge but also your salary.

Universities Offering Online Data Science Doctorate 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. In addition, the schools offer online data science programs .

1. Capella University

Founded in 1993, private Capella University offers online doctorate, master’s, and bachelor’s degrees. The Minneapolis-based school’s 38,000 enrolled students represent 50 states and 61 countries. Doctoral students account for more than 27 percent of Capella University’s student body.

• DBA in Business Intelligence – Data Analytics

Capella University is accredited by the Higher Learning Commission.

2. Capitol Technology University

Capitol Technology University is a private university located near the nation’s capital in South Laurel, Maryland. Established in 1927, the university now offers undergraduate and master’s programs in business, computer science, cybrsecurity, and engineering.

Capitol Technology University is a military-friendly school founded by a Navy veteran. It holds the prestigious SC Media Award for Best Cybersecurity Higher Education Program. The school’s annual enrollment is approximately 850 students.

• PhD in Business Analytics and Data Science

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

3. Colorado Technical University

Colorado Technical University was founded in 1965. This private university offers undergraduate, graduate, and doctoral degrees in business management and technology.

The school has earned the U.S. News & World Report “Best for Veterans” designation, the Council of College and Military Educators (CCME) Institution Award, and recognition as a center of Academic Excellence in Information Assurance and Cyber Defense from the NSA and Department of Homeland Security.

Annual enrollment stands at around 26,000 students.

• Doctor of Computer Science – Big Data Analytics

Colorado Technical University  is accredited by the Higher Learning Commission.

4. Columbia University

New York City’s Columbia University is a private Ivy League research university founded in 1754. It stands today as the oldest university in New York City. Columbia operates four undergraduate schools and 15 graduate/professional schools.

Bachelor’s, master’s, and PhD programs covering business, medicine, liberal arts, technology, and political science are available. Student enrollment at Columbia stands at roughly 33,413.

• PhD in Data Science

Columbia  is accredited by the Middle States Commission on Higher Education.

5. Grand Canyon University

Grand Canyon University is a private Christian college based in Phoenix, Arizona. With a student enrollment of 70,000 students, it is considered to be the world’s largest Christian university.

Grand Canyon University offers bachelor’s, master’s, and doctoral degrees in business, education, health sciences, liberal arts, and nursing. The school offers a total of 200 academic programs throughout its nine colleges.

• DBA in Data Analytics

Grand Canyon University is accredited by the Higher Learning Commission.

6. Harrisburg University of Science and Technology

Founded in 2001, Harrisburg University of Science and Technology is a STEM-focused institution with campuses in Harrisburg and Philadelphia.

This private university offers bachelor’s degrees, master’s degrees, doctoral degrees, and certificate programs. The nearly 6,000 students enrolled study degree paths related to applied science and technology.

• PhD in Data Sciences

Harrisburg University of Science and Technology is accredited by the Middle States Commission on Higher Education.

7. Indiana University-Purdue University Indianapolis

Indiana University-Purdue University Indianapolis is a public research university offering more than 225 options for bachelor’s, master’s, and doctoral degrees across 18 different schools. The university’s campus is based in Indianapolis, Indiana.

The more than 30,000 students enrolled pursue degrees in majors like art and design, business, education, engineering, law, liberal arts, medicine, nursing, and social work.

• PhD in Data Science (on-campus)

Indiana University – Purdue University Indianapolis  is accredited by the Higher Learning Commission.

8. National University

National University is a network of nonprofit educational institutions that is headquartered in San Diego, California. It offers a range of bachelor’s degrees, master’s degrees, doctoral degrees, and certificates in business, education, marriage and family therapy, psychology, and technology.

NU has over 30,000 students enrolled and more than 220,000 alumni from around the world.

National University is accredited by the Western Association of Schools and Colleges.

9. Stevens Institute of Technology

Located in Hoboken, New Jersey, Stevens Institute of Technology is a private research institution with an enrollment of approximately 6,125 students. Founded in 1870, the school has been named among the “Best Value Colleges” by the Princeton Review.

Additionally, the Princeton Review ranks Stevens Institute of Technology among its “Top 15 for Internships.” The school’s undergraduate and graduate students represent 47 states and 60 countries. Students can pursue bachelor’s, master’s, doctoral, and certificate programs.

Stevens Institute of Technology is accredited by the Middle States Commission on Higher Education.

10. University of Central Florida

Located along Orlando’s Space Coast, the University of Central Florida is a public research university with a student enrollment of approximately 69,525. It offers bachelor’s, master’s, and doctoral programs.

Students can pursue degrees in arts and humanities, business, engineering, computer science, health science, medicine, and nursing. The University of Central Florida has been ranked as a “Best Southeastern College” by the Princeton Review.

• PhD in Big Data Analytics

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

Online PhD in Data Science Programs

Data science is exactly what it sounds like – the study of data. Data scientists look at sets of data and notice patterns that emerge. They identify key information that data presents which may not seem readily apparent at first.

If you are someone that notices the small details while also keeping an eye on the bigger picture, a career in data science may be right for you. If you find trends and patterns in large amounts of data, you may be well-suited for this field.

What kind of job can you expect to have as a data scientist? In the last few years, Glassdoor has continuously ranked data scientist as one of the best jobs to have in the United States. The options for specific jobs are numerous and varied.

For example, one data scientist may work as a statistician and interpret statistical information for the U.S. Department of Agriculture. Another data scientist may be a business intelligence developer for Discover, creating strategies for businesses to make more informed decisions about their company.

Data Science Pros and Cons

As with any financial and length time investment, you should consider both the pros and the cons of earning your PhD in an online data science program.

All salary data in this table was provided by the Bureau of Labor Statistics.

Choosing to pursue an online PhD in a data science program is decision that must be taken into careful consideration, but there are many benefits to completing a program.

Data Science Curriculum & Courses

Curriculum for data science programs is heavily focused on analysis and research. Examples of courses offered by universities like Dakota State University and the University of North Texas are listed below.

• Information Systems – This course is designed to help students learn about the role information systems have for businesses and other organizations.
• Applied Statistics – This class teaches how to use statistical software to study data samples and make inferences based on the data presented.
• Project and Change Management – This class is designed to help students learn the underlying principles for managing information systems and how to utilize software for project management.
• Technology for Mobile Devices – Students in this course study the process of developing apps for mobile devices like smartphones and tablets.
• Advanced Network Technology and Management – This class helps students learn how to work with a model network environment, including how to find solutions for problems with the network.
• Seminar in Research and Research Methodology – Students in this seminar are asked to develop a research proposal and participate in a research study.
• Knowledge Management Tools and Technologies – This course introduces students to a variety of technologies including those associated with knowledge management and IT infrastructure.
• Seminar in Communication and Use of Information – This class explores the roles communication plays at various levels in society.
• Readings in Information Science – Students in this class study texts which emphasize methodological and theoretical issues.
• Medical Geography – In this course, students study the correlation between location and health care and work on their own projects.

Exploring the curriculum offered by different universities can help you determine which online PhD program is best suited for your interests and your needs.

Data Science PhD Admissions

Before applying for a PhD program, you will want to ensure that you have all the application materials on hand, including the commonly required materials listed below.

• Reference letters – You should request these documents well before your application deadline as mentors may not be able to honor a last-minute request due to time constraints.
• All transcripts – These grades will include both undergraduate and graduate level courses.
• Letter of intent – Be prepared to explain in writing why you want to enroll in the program and what you plan to do after its completion.
• Application fee – Fees to cover administrative costs of reviewing your application can add up, so make sure to budget for the costs of each one.
• Resume – Schools want to know your background in not just education but in the job market as well.
• Specific program application – Your prospective school will most likely have its own unique application on its official website.

Save yourself the stress of anxiously waiting to receive documents from an institution or mentor in time and compile them well ahead of the due date.

Data Science PhD Careers & Salaries

According to the U.S. Bureau of Labor Statistics , computer and information research scientists earned a median of $131,490 a year. Data scientists as a group earn increasingly high salaries in various industries including research laboratories, government departments, and a variety of companies focused on technology.

Some of the top companies that utilize data scientists are IBM, Amazon, Microsoft, Facebook, Oracle, Google, and Apple. These multi-billion dollar companies are consistently hiring data scientists to interpret the large amounts of data, or “big data,” that is collected via their services.

Data scientists can expect to work in roles where job duties include designing data models, organizing data from multiple sources, and identifying trends in data.

Data scientists use a comprehensive process for gathering and analyzing information including asking questions, acquiring data, storing data, using models to interpret it, and presenting their findings to stakeholders in the community.

According to the Bureau of Labor Statistics, some careers in the data science field include:

Whatever the job title, data scientists continually earn a significant amount more than employees in other fields.

Data Science Accreditation

Before clicking the “submit” button on your application to a PhD program, you will want to ensure that the university you are applying to is accredited, meaning it is recognized as a legitimate program that offers quality coursework and research opportunities.

If you decide to apply to a program related to computer technology or engineering, the Accreditation Board for Engineering and Technology (ABET) determine which schools offer suitable coursework and requirements for these fields. Also be sure that your prospective university is regionally accredited, the gold-standard for accreditation in the United States.

Search on your prospective schools’ website for information regarding their accreditation status. You will want to ensure that the schools you apply to are regionally accredited so you can get the most out of your PhD experience and your credits will be more likely to transfer should you switch schools while studying.

Data Science Professional Organizations

Joining a professional organization can help to advance your career by connecting you with other individuals who work in the same field.

Professional organizations offer a multitude of benefits, including networking opportunities (which may help to connect you with future employers), and they can also provide inspiration for completing your PhD program, decreasing feelings of isolation that can accompany students.

• Association for Information Science and Technology – This organization states its role “advances the information sciences and similar applications of information technology by helping members build their skills and [develop] their careers” via several different ways, including training and education.
• Association of Information Technology Professionals – This agency gives members advice on how to pursue certain career paths while also providing discounts on certifications and resources for professional development.
• International Association for Social Science Information Services and Technology – IASSIST has 300 members from countries around the world. They offer resources for professionals from sectors such as non-profits, academia, and government.

While some organizations may have a yearly membership fee, the potential gains for job opportunities and professional development through these groups can easily offset those costs.

Financial Aid

Across the nation, the average cost of obtaining a PhD online is between $4,000 and $20,000.  As a student in a PhD program, you can expect to have costs from tuition, books, personal supplies, transportation, etc. Without the time or energy for a full-time or often, even part-time job, you should explore all financial aid options available.

Financial aid for PhD students can come in the form of loans, scholarships, and grants. Grants and scholarships typically do not have to be paid back, but loans are borrowed money which may accrue interest and should be a last resort for students.

Some specific scholarships and grants are designed with scientists, including data scientists, in mind. For example, the National Science Foundation Graduate Research Fellowship is designed to support students who are pursuing research-based doctoral degrees.

Previous recipients include Nobel Prize winners, a U.S. Secretary of Energy, and the founder of Google.

Another common source of money comes from taking on teaching assistant positions within your university or becoming an assistant lecturer. Both positions are great for gaining experience teaching in your academic department while generating income to offset the costs incurred from your years of study.

How long does it take to get a PhD in data science?

It takes an average of 71 credits to complete a PhD in data science. On top of this, students may also have responsibilities to research and/or teach, which can make the process take even longer.

It is not unusual for some PhD programs to take anywhere from four to five years to complete.

Is a PhD in data science worth it?

Whether or not a PhD in data science is “worth it” depends on a number of factors. Do you have the time available for next few years (possibly longer) to invest in this opportunity? Are you motivated enough to complete coursework while also on a shoestring budget?

Search for employment positions you are interested in and take a look at the education requirements employers are requesting. These factors may effect your decision in potentially pursuing an online masters in data science instead.

Can I do a PhD in data science?

Whether or not you complete a PhD in data science depends on your ability to stay focused and motivated. PhD programs are notoriously intensive, and they are not for everyone.

You should have a better reason for applying to a program than simply not knowing what to do in today’s job market.

Getting Your PhD in Data Science Online

Obtaining your doctoral degree in data science is not an easy task, but it is also not an impossible one. If you are serious about pursuing your PhD, talk to experts in the field. The admissions departments at prospective universities can help put you in touch with recruiters who can give you more information about the program.

Joining a professional organization can help you connect with individuals who are working in the field, many of whom will have obtained their higher education degree. With careful planning and the right information to make informed career choices, you can further your education and your sense of accomplishment.

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A space for data science professionals to engage in discussions and debates on the subject of data science.

PhD or not to PhD

I’m really on the fence. The DS market was oversaturated before the layoffs but now it’s even worse. I’ve been working at a FAANG for about a year and been testing the waters because I’m doing more Data Analytics than DS in my current role. I’ve been turned down for everything. I’m generally qualified for most roles I applied for through yoe and skills and even had extremely niche experience for others yet I can’t get past an initial screening.

So I’ve been considering going back to school for a PhD. I’ve got about 10 years aggregate experience in analytics and Data Science and an MS and I’m concerned that I’m too old to start this at 36.

I digress but do you have thoughts on continuing education in a slower market? Should I try riding it out for now? Is going back to school to get that PhD worth it or is it a waste of time just to be on the struggle bus again for 3 or more years?

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Exploring Memorable Messages and Resilience in Graduate Mentoring Relationships

Mentorship between faculty members and graduate students is critical for students’ success, especially when it includes career and psychosocial support (Byars-Winston & Lund Dahlberg, 2019; Crisp & Cruz, 2009; Paglis et al., 2006). When mentors offer both types of support, mentees benefit in professional and personal ways (Gardiner et al., 2007; Johnson, 2007). Mentoring occurs through the communication messages that mentors transmit to mentees, but research on mentoring has failed to explore faculty mentor messages (Buell, 2004).

This study aimed to analyze mentor messages from the perspective of Latino graduate alumni from Science, Technology, Engineering, Mathematics, and Medicine (STEMM) programs, a continuously growing group in graduate programs (Solinas-Saunders et al., 2023). To study these messages, this study utilized the theory of memorable messages (ToMM) (Cooke-Jackson & Rubinsky, 2022; Knapp et al., 1981). This study aimed to not only identify what memorable messages mentors communicate to students but also the potential impacts of these messages. This study was also concerned with identifying whether mentor memorable messages also support students’ development of resilience. According to the communication theory of resilience (CTR), resilience may be supported through five communication processes (Buzzanell, 2010).

Thematic analysis of 40 semi-structured interviews with Latino alumni with degrees from various STEMM disciplines revealed four types of memorable messages, positive and negative, from primary faculty mentors. These messages ranged from short verbal messages to longer conversations and included non-verbal communication such as memorable behaviors. Participants recalled messages of invalidation and validation of their academic, interpersonal, and cultural identities. Messages of career and life advice were also recalled. Lastly, messages of mentor red flags were remembered and focused on three specific red flags: manipulative behaviors based on power, emotional manipulation, and unprofessional behaviors.

This study showed that negative messages were more easily recalled, nearly word for word, than positive messages, and the impact of these messages had a deep lasting effect on students’ sense of self-worth. Positive messages, however few, had the ability to counter negative messages even in mentoring relationships that were nearly completely negative. Regarding resilience, all five communication processes of resilience from CTR were exemplified in the data. The data showed that there were very few examples of negative messages that spurred resilience and that many participants learned from memorable messages to develop emotional resilience, particularly those in mostly negative mentoring relationships.

These findings expand existing mentoring literature by illustrating how faculty mentor messages can either facilitate or impede graduate students’ development in STEMM. They also support ToMM’s suggestion that memorable messages can be nonverbal and include behaviors. This data also extends how certain memorable messages may serve as a catalyst for developing emotional resilience. Finally, recommendations are offered for faculty for more intentional communication with graduate students that may result in supportive memorable messages.

Degree Type

• Doctor of Philosophy
• Communication

Campus location

• West Lafayette

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, additional committee member 4, usage metrics.

• Communication studies