What is the Doctorate in Data Science: Everything You Need to Know

Imagine a world where data-driven decisions are at the forefront of every industry, and organizations rely on skilled professionals to harness the power of big data. This is the world of data science, and the demand for experts is growing rapidly. If you’re passionate about data science and want to stand out among your peers, a doctorate in data science might be the perfect choice. In this blog post, we’ll explore the journey of obtaining a PhD in data science, the benefits, pathways, and opportunities it offers, and how it can lead to a fulfilling career.

Key Takeaways

  • A Doctorate in Data Science offers numerous benefits, including career advancement and research opportunities.

  • The process of achieving a data science doctorate typically takes 4-5 years for individuals with a master’s degree and involves completing relevant courses, selecting related positions, contemplating a master’s degree, applying to PhD programs and meeting prerequisites.

  • Achieving the doctorate can be funded through scholarships/grants/fellowships or assistantships. Graduates can pursue academic careers or industry roles with competitive salaries up to $120k annually.

The Allure of a Doctorate in Data Science

In the current era where data is king, organizations consistently strive to decode and utilize the copious amounts of information they gather. As a result, the demand for skilled data scientists has skyrocketed, making it a lucrative and exciting field to enter. Pursuing a PhD in data science offers several benefits:

While the cost of a doctorate degree in a data science graduate program may be around $114,300, and it typically takes four to eight years to complete, the benefits are well worth the investment. Some of the benefits include:

  • Acquiring professional experience before starting a PhD program is beneficial as it provides exposure to the field

  • Allows participation in conferences and research sharing

  • Networking opportunities

  • Continuous learning opportunities through involvement in professional data science associations

The Data Science Landscape

Data science is an interdisciplinary field encompassing disciplines such as statistics, computational sciences, and informatics, with the aim of equipping students with the skills and knowledge to tackle complex data science problems and contribute to the advancement of the field using data science methods. Applications of data science span across various industries, such as finance, healthcare, retail, and technology, making it a rapidly evolving field with endless possibilities.

High-level degrees in data science are increasingly accessible. 57 universities now offer doctorate-level programs in this field. These programs provide students with a solid foundation in data analysis, programming, and statistics, preparing them to conduct research in data science and contribute to the field. Earning a PhD in data science equips you to:

  • Step into the big data industry at a more advanced level

  • Shoulder greater responsibilities

  • Have more direct interactions with leadership

  • Exert more influence on crucial data-driven decisions

Why Pursue a Doctorate?

Obtaining a PhD in data science can lead to numerous advantages, such as career advancement, conducting research, and personal growth. For instance, having a PhD in data science makes you eligible for senior positions within companies, granting you greater responsibilities, more direct contact with leadership, and increased influence on essential data-driven decisions. Additionally, with a PhD, you can expect a higher remuneration package to match your rank.

However, finalizing a dissertation may demand substantial time investment and a strong passion for exhaustive research. Despite the potential challenges, obtaining a PhD in data science opens the door to a wide range of career paths, including academia, industry, and government.

Pathways to a Data Science Doctorate

The path to obtaining a PhD in data science largely depends on one’s prior education and experience. For those with a bachelor’s degree, the steps typically include:

  1. Taking relevant courses or a directly related degree

  2. Selecting a data science position and assessing current abilities

  3. Contemplating earning a master’s degree as part of the doctoral program

  4. Applying to a data science PhD program.

For individuals holding a master’s degree, the transition from a master’s to a PhD in data science typically takes around four to five years.

Bachelor’s to PhD

A bachelor’s degree in data science provides a strong foundation in data analysis, programming, and statistics, preparing you with the required skills and knowledge to conduct research in data science and contribute to the field. To pursue a PhD in data science after obtaining a bachelor’s degree, proficiency in subjects like statistics, calculus, machine learning, programming, data analysis, and big data processes is essential.

Foundational courses in data science, data mining, analysis of algorithms, calculus, and linear algebra are crucial for a successful transition to a data science PhD program. On average, students transitioning from a bachelor’s degree to a data science PhD program can expect a duration of approximately 4-5 years.

Master’s to PhD

A master’s degree in data science lays a firm foundation in the field, equipping students with advanced knowledge and skills in:

  • data analysis

  • statistical modeling

  • machine learning

  • programming

Many master’s programs require students to complete a research project or thesis, which helps hone research skills and prepares them for the independent research required in a PhD program.

The estimated time frame for transitioning from a master’s to a PhD in data science is one to two years. Prospective applicants for a master’s to PhD pathway in data science should possess an undergraduate or master’s degree in fields such as mathematics, statistics, computer science, engineering, or other scientific disciplines, as well as experience with calculus, probability, statistics, and computer programming.

Diving into Data Science Doctoral Programs

Data science doctoral programs provide a detailed study of their structure and components, enabling students to deepen their understanding of the field. A PhD in data science is a research-based degree that usually requires four to five years of study to complete, though the timeframe may vary depending on individual circumstances.

Students pursuing a PhD in data science can anticipate a comprehensive education encompassing a vast array of topics, including:

  • Data mining

  • Big data integration

  • Business intelligence

  • Data visualization

  • Critical analysis

  • Strategic data management

Additionally, the dissertation process is an integral part of obtaining a doctoral degree, as it provides a meaningful contribution to the development and advancement of data science.

Core Courses and Electives

Typically, data science PhD programs offer a core curriculum of courses related to statistics and statistical methods, such as:

  • probability

  • statistics

  • machine learning

  • big data

  • inference

  • research

These courses provide students with the knowledge and skills necessary to tackle complex data science problems and contribute to the advancement of the field.

In addition to the core curriculum, elective courses are also available in data science PhD programs, offering students the opportunity to explore specific areas of interest within the field. Students may be able to choose elective courses on topics such as deep learning, natural language processing, and computational cognitive modeling. These courses provide students with a deeper insight into the field of computer science. These electives allow students to customize their education and gain specialized expertise in their chosen area of study.

Research Opportunities

Numerous research opportunities are available to data science PhD students during their studies. These opportunities may include participation and presentation at conferences, submission of summer fellowship applications, consulting, and paid part-time research positions. Data science research topics are diverse and ever-changing, with current focuses on data analytics, artificial intelligence, big data, end-to-end AI solutions, real-time data and analytics, data governance and regulation, privacy and security in big data analytics, cloud data ecosystems, edge AI, and responsible AI.

Successful data science PhD research projects span various domains, such as:

  • Building chatbots

  • Credit card fraud detection

  • Fake news detection

  • Forest fire prediction

  • Road lane line detection

  • Sentiment analysis

  • Detecting Parkinson’s disease

  • Color analysis

Universities typically offer financial support for data science PhD research projects, further encouraging students to explore and contribute to the field.

Dissertation Process

The dissertation process is a vital part of any data science doctoral program, enabling students to showcase their ability to conduct original research and contribute to the progression of the field. A dissertation is a formal document that presents the results of original research, with the purpose of providing a meaningful contribution to the advancement of data science.

The dissertation process typically involves the following steps:

  1. Selecting a topic

  2. Demonstrating the significance of the topic

  3. Formulating the methodology

  4. Undertaking the research

  5. Composing the dissertation

  6. Presenting the dissertation orally

This process requires dedication, passion for research, and a deep understanding of the data science field.

Successfully completing a dissertation is a significant milestone in a data science PhD student’s journey and serves as a testament to their expertise and skill in the field.

Online vs. On-Campus Data Science Doctorates

Aspiring PhD in data science candidates have the choice between online and on-campus programs. Each type of program has its own advantages and disadvantages, depending on the individual’s needs and preferences. Online programs may require attendance at a few campus events, yet allow for coursework and research to be completed in the student’s hometown. On the other hand, on-campus programs can provide more personalized instruction, networking opportunities, and access to campus resources.

Indiana University Bloomington and North Central University now offer Ph.D. programs with a data science minor or specialization. These programs are available online. It is anticipated that more educational institutions will offer online PhD programs in data science in the near future. However, it is important to carefully consider the pros and cons of each type of program before making a decision.

Online Programs

Online PhD programs in data science provide enhanced flexibility, enabling students to learn at their own pace and in the comfort of their homes. This can be particularly beneficial for working professionals or those with family commitments, who may not be able to attend a traditional on-campus program. Additionally, data science online programs can be more cost-effective than on-campus programs, as students may not need to relocate or pay for housing and other expenses associated with living on campus.

However, online PhD programs in data science might not offer the same networking opportunities or personalized instruction as their on-campus counterparts. Students pursuing an online degree may miss out on the chance to connect with peers and faculty members in person, which can be an important aspect of building a professional network and learning from others in the field.

On-Campus Programs

On-campus data science PhD programs come with several benefits, including:

  • A small, collaborative cohort experience

  • A data science-focused program

  • Collaboration opportunities with advisors

  • A comprehensive understanding of various areas in data science

  • Access to campus resources and facilities

These data science program also provide students with ample opportunities to network with their peers and faculty members, which can be beneficial for their future careers in data science.

However, pursuing a data science doctorate through an on-campus program can be more costly and less flexible than online programs. Students may need to relocate or pay for housing and other expenses associated with living on campus, and may have less control over their study schedule than they would in an online program.

Navigating the Admissions Process

While the admissions process for data science doctoral programs can be intricate and time-consuming, it becomes manageable with appropriate preparation and guidance. Prospective students must consider the typical admissions requirements, such as:

  • Completion of prerequisite courses

  • Skills in statistics, calculus, and computer programming

  • Submission of an application form and fee

  • Statement of purpose

  • Letters of recommendation

  • Transcripts

  • Resume or CV

  • GRE scores

By fulfilling these requirements, prospective students can increase their chances of being accepted into a data science doctoral program.


Eligibility for a data science PhD program generally requires:

  • A bachelor’s and master’s degree from an accredited institution

  • Coursework or equivalent experience in calculus, probability, statistics, and programming

  • Some programs may also require written and oral qualifying exams.

In terms of coursework, admissions committees typically look for subjects like:

  • Data science programming

  • Data management for data science

  • Numerical linear algebra

  • Introduction to optimization

  • Probability and statistics

Application Materials

In addition to meeting the prerequisites, applicants to data science doctoral programs must also submit a variety of application materials. These may include:

  • A completed application form and fee

  • A statement of purpose

  • Letters of recommendation

  • Transcripts

  • A resume or CV

  • GRE scores

The statement of purpose is a particularly important aspect of the application, as it allows applicants to showcase their research interests, academic background, and career ambitions. Letters of recommendation should be from academic or professional sources who can provide an honest evaluation of the applicant’s aptitude and potential for further study.

Deadlines and Timelines

When applying to data science PhD programs, one should be mindful of the typical application deadlines, which are usually between December and February. Additionally, the timeline for PhD program applications can vary, but a general timeline includes:

  • Researching programs

  • Preparing application materials

  • Submitting applications

  • Undergoing the application review and decision-making process

  • Participating in interviews (if applicable)

  • Receiving notification of acceptance or rejection.

To ensure a smooth application process, it is recommended to initiate the application process at least six months prior to the desired start date of the program.

Financing Your Data Science Doctorate

While pursuing a PhD in data science may constitute a substantial financial commitment, a variety of funding options exist to help students offset their educational expenses. Scholarships and grants, fellowships, and assistantships are just a few of the many funding opportunities available to data science PhD students.

Scholarships and Grants

Numerous scholarships and grants are available to data science doctoral students, including:

  • ACM SIGHPC Computational and Data Science Fellowship

  • Acxiom Diversity Scholarship

  • AIGC Science Post-Graduate Scholarship

  • ASA Pride Scholarship

  • Milliman Opportunity Scholarship Fund

  • MinneAnalytics Data Science Scholarship

  • Ontario Graduate Scholarship program

  • International Merit Postgraduate Scholarship

  • GREAT Scholarships

  • Latin American Academic Achievement Scholarship

These scholarships and grants can help alleviate the financial burden of pursuing a PhD in data science and allow students to focus on their academic and research pursuits.

In order to apply for scholarships and grants, students should:

  1. Research available opportunities

  2. Examine the eligibility criteria and requirements

  3. Assemble their application materials

  4. Submit their application by the deadline

If chosen, they must adhere to any supplementary instructions provided by the scholarship or grant provider.

Fellowships and Assistantships

In addition to scholarships and grants, fellowships and assistantships are also available to data science PhD students. Fellowships typically do not require any service requirements in terms of hours or work performed, although certain deliverables may be requested by the sponsor. Assistantships, on the other hand, have specified hours and work requirements within 13-week periods, as outlined in the position description.

Notable fellowship opportunities for data science PhD students include the Bloomberg Data Science Ph.D. Fellowship, the IDA Data Science Fellowship, the Google PhD Fellowship Program, and the NVIDIA Graduate Fellowships. To apply for fellowships and assistantships, students should research available opportunities, complete an application form, and submit it for review. Some programs may require letters of recommendation.

Career Prospects for Data Science PhD Graduates

A PhD in data science paves the way for an array of career opportunities, with potential paths spanning academia, industry, and government. By obtaining a doctoral degree in data science, graduates are equipped with advanced knowledge and skills in the field, enabling them to take on research and leadership roles in various sectors.

Employment in the data science field is projected to witness a substantial growth of 36% in the forthcoming decade. This rate of increase is far above the average for all occupations. According to the Bureau of Labor Statistics (BLS), data scientists had a median annual wage of $100,910 in May 2021. This is significantly higher than the median annual wage across all occupations. With a PhD in data science, graduates can expect to enjoy a lucrative and fulfilling career in a rapidly growing field.

Academic Careers

Graduates with a PhD in data science can seek research and teaching positions in universities, serving as professors or researchers in the field. These positions offer the opportunity to collaborate with other experts, develop innovative solutions to complex problems, and contribute to the advancement of data science through research and education.

Furthermore, academic careers often come with enhanced job security, competitive remuneration, and prospects for career advancement.

Industry Careers

Data science PhD graduates can also venture into the private sector, taking up roles such as a data scientist, or:

  • Data scientists

  • Data analysts

  • Business analysts

  • Database administrators

  • Analytics managers

These roles can be found across diverse industries, and in them, graduates can apply their advanced knowledge and skills to help organizations make data-driven decisions, optimize processes, and gain a competitive edge.

The average salary for a data science PhD graduate in the private sector is approximately $100,000 to $120,000 annually.

Government Careers

Alongside academia and industry, data science PhD graduates can secure positions in government agencies, where their expertise is crucial for making data-driven decisions with widespread societal impact. Working in government agencies allows data scientists to contribute to meaningful research and policy-making initiatives, while enjoying the stability, competitive salaries, and career advancement prospects that often accompany government positions.


Pursuing a PhD in data science offers numerous benefits, from increased career opportunities to personal growth and the chance to contribute to the advancement of the field. With various pathways to obtain a doctorate, as well as online and on-campus program options, there is a suitable path for everyone. By understanding the admissions process, financing options, and potential career paths, you can make an informed decision about pursuing a data science doctorate and embark on a rewarding journey in this rapidly growing field.

Frequently Asked Questions

Is a doctorate in data science worth it?

A PhD in Data Science is the highest qualification for an aspiring data scientist and demonstrates to employers and fellow professionals that you are an expert. For those who want to make a deep dive into their studies and gain insight from their research work, a doctorate in data science is worth it.

What is the difference between a PhD in data science and a PhD in statistics?

The main difference between a PhD in data science and a PhD in statistics is that the former requires knowledge of graduate-level statistics, computer science, and programming, whereas statisticians need strong math skills but not necessarily computer science knowledge.

How many credits is a PhD in data science?

A PhD in Data Science requires a minimum of 72 graduate credits with 24 dissertation credits, including 30 credits of 700-level courses.

How long does it typically take to complete a data science PhD program?

It typically takes four to five years to complete a data science PhD program.

What are the typical prerequisites for a data science PhD program?

To pursue a data science PhD, you typically need a bachelor’s and master’s degree along with coursework or experience in calculus, probability, statistics, and programming.

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