Guide to a Ph.D. in Data Science in 2020

Written by ComputerScience.org Staff Writers


What Is a Ph.D. in Data Science?

A Ph.D. in data science gives students deep knowledge of programming, statistics, data analysis, machine learning, and artificial intelligence. The degree combines course requirements in data science and related fields with practical experience and a dissertation. Like other doctoral programs, this degree emphasizes original research, particularly in pure theory, applied theory, and the techniques and tools of data science.

Graduates from online doctoral degree programs in data science can work as researchers, college professors, and senior data scientists. They may build new products, lead research teams, reinvent tools, or craft new applications for existing knowledge.

The Bureau of Labor Statistics projects computer and information technology occupations to grow by 12% between 2018 and 2028, amounting to more than 546,000 jobs. These professionals earn a median annual salary of $88,240.

Should I Get a Ph.D. in Data Science?

Is a Ph.D. in data science worth it? Each person must answer that question for themselves based on their own expected return on investment. Nevertheless, many professional and personal benefits come with pursuing a terminal degree in data science. The following list outlines several of these benefits.

  • Qualify for Academic Positions: Earning an online Ph.D. in data science qualifies professional data scientists to take on academic positions, including postsecondary teaching jobs and university administration positions.
  • Transition from Computer Engineering to Data Science: Most machine learning engineers and data scientists transition into the field from computer engineering. A Ph.D. can help smooth that transition.
  • Demonstrate Intelligence and Dedication: The road to a Ph.D. is long and requires dedication. Completing a terminal degree demonstrates to employers that an applicant possesses both the dedication and the intelligence to succeed in a challenging field.
  • Competitive Resume: Data science is a competitive field, and a Ph.D. can help applicants' resumes stand out. A Ph.D. indicates strong leadership and research skills along with deep technical knowledge.
  • Research Skills: Ph.D. work primarily consists of conducting original research. After completing a Ph.D. in data science, graduates can apply these skills to stay ahead of industry trends and create innovative solutions to problems.

Admission Requirements for a Ph.D. in Data Science

The admission requirements for an online Ph.D. in data science program vary from school to school. However, most schools have common expectations regarding previous education and experience. In general, prospective students should hold at least a bachelor's degree, and some schools expect applicants to hold a master's degree.

While a major in computer science, math, or business analytics may increase an applicant's chances of acceptance, most schools do not list a specific major as a requirement. Some universities do, however, expect students to have completed courses in linear algebra, multivariate calculus, computer programming, and statistics or probability theory.

Typically, applicants need a minimum overall GPA in all previous coursework, often 3.5 on a 4.0 scale. Schools may also require a minimum GRE score, usually in the 70th percentile, as a prerequisite for acceptance into the program.

What Can I Do With a Ph.D. in Data Science?

After completing an online doctoral degree program in data science, graduates can work as data scientists, business analysts, information science researchers, or college professors. Some people may also wish to gain certification in a new data research language, which can open additional career opportunities.

Career and Salary Outlook for Data Science Graduates

An online Ph.D. in data science can lead to careers in analytics, business leadership, and machine learning. The Bureau of Labor Statistics projects the demand for computer and information technology professionals to grow by 12% between 2018-2028. Many computer science careers pay lucrative salaries and offer opportunities to advance with more education.

Some of the highest-paying careers in the field include database administrator, software developer, information security analyst, computer and information research scientist, and computer network architect. Graduates can also work in high-paying jobs in data science. Data scientists who work for federal agencies in Washington, D.C., or in technology firms in Silicon Valley or New York City do particularly well.

Professor
College professors prepare and deliver lectures, oversee research assistants, and grade tests and projects. These educators may also serve on committees, counsel students, and conduct research as part of their jobs. Most professorships in data science go to applicants with a Ph.D. in data science or a closely related field.
Data Scientist
These experts use their skills in math, computer science, and trend analysis to solve complex problems. Many data scientists start out as statisticians or data analysts, then develop expertise in machine learning, data visualization, deep learning, and pattern recognition. They often work in academia or for large companies in finance, government, or the pharmaceutical industry.
Senior Data Scientist
Senior data scientists support businesses, government agencies, and universities by synthesizing and leveraging data and datasets to enhance overall outcomes. They lead teams and oversee junior data scientists. Senior data scientists often need high-level education in the field and leadership skills to manage the data scientists on their team.
Computer and Information Scientist, Research
Computer and information research scientists identify new uses for current technology or develop new technology from the ground up. Typically, they hold government jobs or work for computer design firms. These professionals need the original research skills and deep technical knowledge that come with a Ph.D in data science.
Business Intelligence Analyst
Business intelligence analysts form the bridge between pure data science and its application to business activities. These professionals excel in data visualization, data analytics, and data modeling technologies and techniques. They work to improve bottom lines by determining areas where businesses can minimize losses or maximize profits.

Ph.D. in Data Science Careers: Median Salaries by Experience
Job Title Entry Level (0-12 Months) Early Career (1-4 Years) Mid-career (5-9 Years) Experienced (10-19 Years)
Professor $61,000 $61,000 $69,000 $86,000
Senior Data Scientist $108,000 $121,000 $130,000 $137,000
Computer and Information Scientist, Research $105,000 $103,000 $120,000 $149,000
Business Intelligence Analyst $59,000 $66,000 $77,000 $85,000
Data Scientist $86,000 $94,000 $108,000 $120,000
Source: PayScale

Continuing Education in Data Science

Data science is a fast-moving field that requires ongoing study to stay abreast of the latest changes. Pursuing continuing education opportunities can provide benefits such as salary increases and new career opportunities.

Earning Your Doctorate in Data Science

Earning an online Ph.D. in data science typically takes about five years. This timeline includes coursework, apprenticeships, research, and dissertation writing and defense.

Most graduate data science programs require 10 courses to complete the master's component of the program, which usually takes two years. These courses often draw from data science, ethics, applied math, computer science, and statistics. Students may learn to create simulations, run sklearn regressors, optimize data sets, and perform hyperparameter optimization on an artificial neural network.

Once they complete the coursework component of the Ph.D., graduate students can move on to teaching, internships, research, and writing. An online degree may take longer to complete than an on-campus program, since these students often work while going to school. Online students may also spend more time in courses or on writing, since they are less likely to conduct teaching or guided research than their on-campus peers. That said, each online Ph.D. in data science is different and prospective students should consider the individual merits of prospective programs.

Comparing Ph.D. Options

Students can pursue several different types of Ph.D. programs that explore data science, including degrees in business analytics, computer science, and data science itself. While all of these degrees include coursework and research opportunities in the same major fields, their varying emphases differentiate them from each other.

Ph.D. in Data Science
Ph.D. in data science programs focus on defining and creating inventive solutions to data research problems. Coursework emphasizes informatics, design, and knowledge acquisition and management. Learners typically take courses in bioinformatics, library science, medical informatics, and human-computer interaction.
Ph.D. in Business Analytics
Usually offered through a school of business, a Ph.D. in business analytics consists of coursework in business analytics, operations, supply chain management, and project management. Students often direct their research toward statistics, econometrics, data mining, operations research, or probabilistic modeling. The program may also include apprenticeships.
Ph.D. in Computer Science with a Data Science Concentration
This program focuses on the larger discipline of computer science while providing focused coursework in data science. Students may also direct their research and dissertation toward data science as a subdiscipline of computer science. Typically, students learn about human-computer interaction, distributed systems, artificial intelligence, and theoretical computer science.

Popular Ph.D. in Data Science Courses

Online doctoral degree programs in data science offer a variety of course types and topics depending upon faculty members' expertise and the school's focus. Due to the cutting-edge nature of data science, the curriculum examines critical topics aligned with current industry needs. Most courses also include an opportunity for students to gain hands-on experience.

Doctoral programs are oriented toward research and therefore include project-based courses, capstones, and writing-intensive classes. These courses explore many critical topics, including databases, data mining, business intelligence, data visualization, and big data integration. Like most other doctoral programs, Ph.D. in data science programs typically conclude with a dissertation.

  • Algorithm Design, Analysis, and Implementation

    Students in this course learn the fundamental techniques required to design and analyze algorithms. Topics include balancing, dynamic programming, and divide and conquer. Learners also examine lower and upper bounds on space and time costs, worst case, and expected cost measures. In addition, students consider a selection of applications, including pattern matching, search trees, disjoint set union/find, and graph algorithms.

  • Computational Methods in Analysis

    This course considers ways to use numerical algorithms to solve classical problems in real analysis. Students focus on optimization problems, along with both nonlinear and linear systems of equations. The course also includes a study of the writing, testing, and comparison of numerical software in problem-solving. Students consider the software characteristics needed to implement these algorithms.

  • Informatics Research Design

    In this class, Ph.D. students learn about the philosophical underpinnings and practical applications of informatics research. Topics include deterministic hypothesis-driven experimental designs, data mining, posteriori research, and quantitative and qualitative research.

  • Visualization Design, Analysis, and Evaluation

    This graduate-level course introduces students to topics such as visualization design, interaction techniques, human visual perception, and evaluation methods. Students learn how to use modern web-based frameworks to create visualizations, critically evaluate visualizations, and conduct independent research in visualization and visual analytics.

  • Applied Cloud Computing for Data-Intensive Sciences

    Students in this course learn the techniques, tools, and concepts of data science, including parallel algorithms, cloud computing, high-level language support, and nonrelational databases. Learners discover how to apply virtual-machine utility computing environments and the MapReduce programming model to scalable data processing and data-driven discovery for scientific applications.


The Doctoral Dissertation

One of the final steps in the Ph.D. journey is the dissertation, which is an original research project in data science. Typically, academics consider the dissertation to be the most important component of their doctorate, since it shapes their knowledge and specialty in the field. Data science students might research topics such as data cleaning, trend identification, or compositional machine learning. Alternatively, they may research a cross-disciplinary topic such as bioinformatics or the role of data science in renewable energy policy.

The dissertation phase typically lasts 1-3 years and includes research, writing, and defense components.

Selecting Your Data Science Doctoral Program

Students should consider many factors when researching prospective programs. The list below describes a few that may be particularly important to consider.

Staff Credentials

Ph.D. students should consider the expertise, professional reputation, and diversity of the faculty members who teach classes, oversee research projects, and direct dissertations.

Cost/Financial Aid

Cost and financial aid are major factors in selecting a school for many students. Selecting a less expensive program or one that provides a substantial aid package can save students a lot of money.

Concentrations/Specializations

Data science links with other disciplines such as genetics, biomedical science, engineering, computer science, technology management, information studies, and business administration. Consequently, many schools offer concentrations in these areas, as well as other fields.

Program Length

Does the program accept transfer coursework from a master's degree or other graduate-level study? Or must students complete all required credits at the university? The answers to those questions can determine the difference between a three-year completion time versus a five-year completion time for many learners.

Alumni Network

A robust alumni network can lead to significant career opportunities by allowing learners to connect with top professionals in the data science field.

Online vs. In Person

Online programs are more flexible and often less expensive than on-campus programs, but they may not include as many networking and research opportunities.

Accreditation

At minimum, students should select a school with regional accreditation. The best programs often hold programmatic accreditation, as well.

Should You Get Your Ph.D. in Data Science Online?

Data science is particularly suited to the online learning experience because of its inherent curricular flexibility, emphasis on technology, and focus on analytics. Students considering an online Ph.D. in data science should consider whether their top choices include limited residency requirements.

Prospective students should also look at the cost of an online degree, which is typically less than that of a traditional program. They may also research graduate career outcomes to determine the return on investment when considering the best path forward.

Accreditation for Data Science Schools and Programs

In the U.S., schools can hold regional or national accreditation, with regional accreditation generally considered the more prestigious of the two. Regional accreditation indicates that a degree-granting institution meets high academic standards with respect to factors like faculty qualifications, academic rigor, and student learning outcomes. There are six regional accrediting bodies approved by the U.S. Department of Education and the Council for Higher Education Accreditation.

Data science programs may also receive accreditation, typically through the Accreditation Board for Engineering and Technology. Programmatic accreditation indicates a school's willingness to invest in high-quality educational infrastructure for its graduate programs in data science.

Students who attend an accredited school enjoy more financial aid opportunities and greater access to careers after graduation. Learners pursuing a doctorate should take an especially keen look at their school's accreditation status, since it will affect their ability to pursue opportunities in postsecondary teaching.

Resources

Professional Organizations for Data Science

Professional organizations are a great way to network, access information and resources, and stay up to date in the field. Members can connect with peers, learn from established experts, pursue valuable designations, and receive recognition for their work. Members may also assume leadership roles in an association.

  • International Data Engineering And Science Association IDEAS offers members access to chapters and conferences around the world, along with up-to-date information about market trends and job opportunities in data science.
  • The Association of Data Scientists Data scientists and machine learning professionals in this association can access professional development, networking, and recognition opportunities. ADaSI also manages the chartered data scientist credential.
  • Data Science Association Open to data scientists, academics, students, and others interested in the field, the DSA provides members with conference discounts, access to networking opportunities, and a full library of resources.

Scholarships for Ph.D. Programs in Data Science

Scholarships can help students pursuing online doctoral degree programs in data science to meet their financial obligations. Some scholarships and aid programs provide support for living expenses beyond tuition costs. Application criteria vary, and some scholarships require students to work for a particular organization after graduation.

NSF Graduate Research Fellowship Program

Who Can Apply: The National Science Foundation funds this program to support graduate students in STEM. The ultimate goal of the program is to advance the country's national security and technological infrastructure, as well as contribute to the economic well-being of society.

Amount: Varies

Apply for Scholarship

Women Techmakers Scholars Program

Who Can Apply: Formerly known as the Google Anita Borg Memorial Scholarship Program, this scholarship supports the advancement of women in technology. Applicants must show a strong academic record and a commitment to leading initiatives that increase the involvement of women in technology.

Amount: $10,000

Apply for Scholarship

GAANN Ph.D. Fellowship

Who Can Apply: Applicants must attend schools that have received this federal grant. Recipient schools accept applications, determine eligibility, and award funding at their discretion under the guidelines the program has established.

Amount: Varies

Apply for Scholarship

Mary G. and Joseph Natrella Scholarship

Who Can Apply: The American Statistical Association's Quality and Productivity Section funds this scholarship for full-time graduate students who demonstrate their skills through coursework, research, and prior work experience. Recipients give a presentation at the association's conference.

Amount $3,500

Apply for Scholarship

SMART Scholarship Program

Who Can Apply: Funded by the U.S. Department of Defense (DoD), this scholarship supports applicants in qualifying fields who hold a GPA of 3.5 or better. Candidates must be willing to accept post-graduate employment with the DoD.

Amount: Full tuition, monthly stipends, health insurance, and book allowances

Apply for Scholarship

Frequently Asked Questions

How long does it take to get a Ph.D. in data science?
A Ph.D. in data science typically takes 4-5 years to complete, including coursework, research, and a dissertation. Some part-time programs may require more time.
Is a Ph.D. in data science worth it?
The terminal degree in the field, many professionals find a Ph.D. in data science essential to their success as it prepares them for top-level positions in the field.
What can I do with a Ph.D. in data science?
Since the field essentially consists of applied research, an online Ph.D. in data science leads to flexible career options in government, business, academic research, and postsecondary teaching.
How much does a data scientist make?
According to Payscale, the average data scientist earns $96,000 per year. This figure goes up for professionals with skills in machine learning, algorithm development, and Apache Spark.

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