Data science degrees prepare students to meet the needs and challenges of what is presently one of the fastest-growing career fields in the nation. Data scientists rely upon a broad range of interdisciplinary skills and knowledge, some of which can be highly technical. The vast majority of professionals begin their training with formal education: one major survey indicates more than 90 percent of data scientists hold advanced degrees. Learning options in data science have increased dramatically in recent years as colleges have added new programs to meet rising demand for trained data analysts. North Carolina State University’s Institute of Advanced Analytics (IAA) reports that the number of campus-based and online master’s in data science programs increased five-fold between 2013 and 2015 alone.
Data scientists use advanced mathematics, statistical modeling, and computer programming to solve data-intensive problems. By analyzing large data sets, data scientists create predictive models, test hypotheses, and identify actionable insights that can be used to fine-tune business strategies, evaluate financial markets, improve learning outcomes, and more. Data science can also be used to tailor public health initiative and save lives.
Consider data scientist and research fellow Aaron Gowins, who works in the Laboratory of Biological Modeling at the National Institute of Diabetes and Digestive and Kidney Diseases. Mr. Gowins and his team are developing a mathematical simulation of the changes in body composition and energy use in growing children. While the National Institutes of Health is using the model to study childhood obesity, the Bill and Melinda Gates Foundation intends to use the same simulation to research and combat under-nutrition in infants. Mr. Gowins discussed his work during a recent interview with OnlineEducation.com.
“In developing parts of the world, poor nutrition and sanitation put children at risk of experiencing stunted growth,” said Mr. Gowins. “Researchers have associated this early childhood growth faltering with an increased risk of cognitive deficiencies later in life, (which) puts an additional burden on populations already facing many challenges… We hope to provide insights into the optimal timing and nature of interventions, as well as identify specific gaps in data that can be addressed by future studies.”
A master’s degree in data science can equip a student with the tools necessary to perform similar analyses, not only in healthcare, but also in banking, technology, marketing, government, economics, and many more industries.
The field of data science existed long before dedicated data science degree programs, though it was sometimes known by other names. Previously, data science professionals studied and earned degrees in fields like mathematics, applied statistics, and computer science. Today’s students can pursue data science master’s degrees that are tailored to the demands of the field. These include the following degrees, among others:
Master’s in data science programs combine advanced coursework in math, statistics, and computer science with field-specific coursework like R statistical programming, machine learning, and data visualization. Many of these subjects—particularly programming, statistical modeling, and graphics work—are particularly well suited to computer-based and distance-learning programs. Colleges often offer online master’s degrees in data science to make classwork remotely accessible to students who are unable to attend regular campus-based courses.
Data science is a relatively new and fast-growing interdisciplinary field, which might explain why many colleges do not yet have dedicated data science departments. Online data science programs are often offered through computer science, engineering, and statistics departments. Because there is no formal naming convention for data science programs, some schools use general terms like analytics, while others offer data science concentrations as part of computer science and engineering programs. Currently, there is not a specific accrediting body that approves data science programs, so there are no set curricular standards that programs must follow. Therefore, not all curricula are the same. Variations in coursework may influence the focus of different programs and even the career options available to graduates. These variations can make it challenging in some cases to determine which online programs are most suitable for particular professional goals.
OnlineEducation.com researched the field of data science carefully and established clear criteria for categorizing online data science programs. This is meant to assist prospective students in identifying relevant degree programs regardless of their titles or departmental affiliations. All online data science programs featured on this website must meet at least one of the following requirements:
As of November 2017, we have identified 21 universities offering online Master’s in Data Science programs that meet our criteria for inclusion in our directory. On OnlineEducation.com, we only include programs offered by non-profit universities.
While OnlineEducation.com does its best to ensure online degree listings are accurate and up-to-date, programs can and do change. Students considering master’s degrees in data science should visit prospective schools’ websites to confirm the most current curriculum for the programs included below. Readers can visit OnlineEducation.com’s Data Analytics and Business Intelligence program pages to learn more about other online analytics degrees.
Note: At this time, OnlineEducation.com does not include online MBA programs with a specialization in data science in the listings below, but may do so in the future.
Online master’s in data science programs provide in-depth instruction in the foundational skills and concepts of analytics, as well as in the tools and technologies used to aggregate, organize, and interpret vast streams of data. Students typically study algorithmic programming, statistical modeling, computer coding, IT systems design, and machine learning. Some online data science programs also focus on specific real-world applications of analytics, in areas like business and healthcare. This may include coursework in health informatics, government and public policy, and/or econometrics.
The following courses are sampled from real online master’s in data science programs across the country.
|Foundations of Computer Science||An essential introduction to the field of computer science and its mathematic foundations. Additional topics include regular and context-free computing languages, computational complexity, Turing machines, and finite-state automata.|
|Applied Machine Learning||A foundational course that introduces the technology and fundamental ideas used in machine learning. Emphasizes practical applications of machine learning, such as speech recognition and artificial intelligence. Students apply probability, linear algebra, programming, and statistics skills.|
|Database Management and Design||Students attending this course are introduced to practical aspects of database management systems (DBMS). They will learn how to design, use, and implement database systems, and how to retrieve information from them using languages like SQL. Additional topics include the principles and programs that emerge when designing, operating, and maintaining relational DBMS.|
|Data Mining||An introduction to the processes data scientists use to manage, explore, and visualize large datasets. Familiarizes students with classification, regression, and other data mining techniques. Programming languages used: Python and R.|
|Data Visualization||Students learn how to use the processing programming language and coding to visualize data with 2- and 3-D graphics. This course introduces key informational design principles, object-oriented programming, and APIs.|
|Big Data Management||Presents the latest scientific and engineering methods that allow data scientists to collect data quickly and efficiently. Students will become familiar with the techniques and infrastructures that support big data management across systems, including how to query, optimize, and access stored data.|
|Cloud Computing||Provides an overview of the technologies, principles, and methods of cloud-based computing. Students will learn about Hadoop and MapReduce; resource management; and migration to the cloud over the course of designing their own cloud applications.|
|Applied Regression and Times Series Analysis||Students learn time series data visualization, probability, statistics, and the linear regression models that power modern statistics and support virtually all data science concepts. Students will learn how to formulate, choose, and apply statistical techniques while familiarizing themselves with R code.|
Prospective data science students must typically meet certain criteria to be eligible for admissions. Programs may require applicants to hold undergraduate degrees in mathematics, engineering, statistics, computer science, and other relevant disciplines. Some data science programs also request applicants’ official scores on aptitude tests like the GMAT or the GRE. Academic prerequisites can include the successful completion of specific undergraduate courses, such as Calculus I and II, Linear Algebra, and Intro to Computer Science. A minimum threshold for undergraduate grade point average is another common requirement, as are admissions essays, personal statements, and letters of recommendation.
Online graduate degrees in data science may require applicants to have additional, non-academic experience in analytics. For example, colleges may admit students with a minimum level of professional data science experience and/or certifications in lieu of a relevant undergraduate degree. Highly competitive programs, on the other hand, may require or prefer that applicants have verifiable professional data science experience in addition to meeting academic criteria.
Convenience and course scheduling flexibility are two of the more obvious benefits of online education, but some data science programs may suit one’s circumstances better than others. Mandatory course loads, average time-to-graduation, and the rigidity of course schedules can significantly improve or hinder one’s chances of enjoying or completing their studies. This may be especially true for students balancing school with military, career, and/or family obligations.
Course Load and Length of Study: Like campus-based programs, online data science programs may allow students to attend on a part-time, full-time, or accelerated basis. This will determine how many courses one takes each semester and, ultimately, how long it will take to complete the degree. The average time-to-completion for data science master’s degrees is from one to two years, but schools may allow students to take more time so long as they meet minimum course enrollment requirements or file an official leave of absence. The following table presents real enrollment options and credit requirements from three different online data science schools. All estimates reflect university-cited program data.
Avg. Courses Per Semester
Time to Completion*
|University A (27 credits)||Part-time|
Up to 3 courses
|No more than 32 months
As little as 12 months
|University B (32 credits)||Student-determined||~ 2 to 3 courses||18 to 24 months, on average|
|University C (23 credits)||Part-time only||2 courses||24 months|
|* Schools’ estimated time-to-completion for students who maintain suggested course load throughout their programs.|
Synchronous versus Asynchronous Instruction: How and when students attend online courses is another consideration that impacts one’s learning experience. Online master’s programs in data science that use synchronous instruction require students to attend scheduled lectures in real-time. While this approach offers less flexibility, it mirrors the spontaneous, interactive class experience one would find on campus. Online data science programs that use asynchronous instruction allow students to access prepared lectures and materials whenever convenient, so long as they meet all specified due dates. For more information on the different types of instruction methods, see our guide to Instructional Methods for Online Learning.
Required Internships or Campus Visits: Colleges offering online master’s degrees in data science often design programs with parents and working or remote students in mind, so they aim to provide a certain degree of flexibility and convenience. In many cases, this means one can complete 100 percent of the degree online without required internships or campus visits. In some cases, however, online data science students may be required to participate in internship programs—often within their home communities—or travel to campus for on-site courses or seminars. Students considering such programs may want to factor travel costs into their budget as schools typically do not include these additional expenses in the program’s tuition and fees.
OnlineEducation.com classifies online data science programs as those that require no more than two campus visits per year. Programs that require more than two visits per year are classified as hybrid programs and are not included on the site.
|School||Program||Fully Online Instruction*||Program Options||Website|
|Elmhurst College||Online Master of Science in Data Science program||Yes||PT||www.elmhurst.edu|
|George Mason University||Online Master of Science in Data Analytics Engineering (Data Science)||Yes||FT & PT||masononline.gmu.edu|
|Georgia Southern University||Online Master of Science in Computer Science (MSCS) with a Concentration in Data Mining and Data Warehousing||No||PT||ceit.georgiasouthern.edu|
|Illinois Institute of Technology||Online Master of Data Science program||Yes||PT||science.iit.edu|
|Indiana University Bloomington||Online Master of Science in Data Science program||Yes||FT & PT||www.soic.indiana.edu|
|Johns Hopkins University Engineering for Professionals||Online Master of Science in Data Science||Yes||PT||online.ep.jhu.edu|
|Kent State University||Online Master of Digital Sciences with a Concentration in Data Science||Yes||FT & PT||www.kent.edu/dsci/|
|Lewis University Online||Online Master of Science in Data Science program||Yes||PT||online.lewisu.edu|
|Oklahoma State University||Online Master of Science in Management Information Systems with a Data Science Option||Yes||PT||spears.okstate.edu|
|Regis University||Online Master of Science in Data Science program||Yes||PT||www.regis.edu|
|Saint Mary's College||Online Master of Science in Data Science program||No||PT||grad.saintmarys.edu|
|Southern Methodist University||DataScience@SMU Online Master of Science in Data Science program||No||FT & PT||datascience.smu.edu|
|Stevens Institute of Technology||Online Master of Science in Business Intelligence & Analytics program - Data Science Concentration||Yes||FT & PT||www.stevens.edu|
|University of California, Berkeley||datascience@berkeley Online Master of Information and Data Science (MIDS) program||No||FT & PT||datascience.berkeley.edu|
|University of California, Riverside||Online Master of Science in Engineering with a focus in Data Science||Yes||FT & PT||engineeringonline.ucr.edu|
|University of Colorado Boulder||Online Master of Science in Computer Science with an Emphasis in Data Science and Engineering||Yes||FT & PT||www.colorado.edu|
|University of Illinois at Urbana-Champaign||Online Master of Computer Science in Data Science (MCS-DS) track||Yes||FT & PT||cs.illinois.edu|
|The University of Oklahoma||Online Master of Science in Data Science and Analytics program||Yes||FT & PT||www.ou.edu|
|University of Southern California||Online Master of Science in Computer Science (Data Science)||Yes||FT & PT||online.usc.edu|
|University of Wisconsin - Extension||Online Master of Science in Data Science (MS-DS) program||Yes||FT & PT||datasciencedegree.wisconsin.edu|
|Utica College||Online Master of Science in Data Science program||Yes||PT||programs.online.utica.edu|
|*Programs with fully online instruction do not require students to visits the campus during the program. Programs marked No require students to visit the campus two or fewer times per year for on-campus orientations or intensive sessions.|
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