What is a master’s in data science and what will you learn in an online program?
Pursuing a master’s degree in the fast-growing field of data science can help you to advance your career in a wide variety of tech-related roles. Expect to learn a broad set of skills, including how to use computer programming languages and about applied statistics, database systems, and machine learning. The skills and concepts you learn in a master’s degree program will prepare you for a career in data science to help organizations make strategic decisions based on the data they collect. There’s no significant difference between online and on-campus data science programs—schools typically offer the same courses that are taught by the same professors, regardless of the format.
General curriculum and skills taught
You can expect a comprehensive curriculum in an online master’s degree program in data science that draws on both statistical and computational methods. Programs will emphasize the real-world application of these knowledge and skills, while offering a multidisciplinary approach to the field that also draws on statistics, computer science, and law. Data science is about more than numbers, however; you will also learn “soft skills” about how to effectively communicate the lessons learned and collaborate with others to learn how to best utilize information in an ethical way. Core coursework at many data science programs covers the following topics:
- Machine learning
- Data mining
- Data visualization
- Cloud computing
- Research design
- Information ethics
- Statistical analysis
- Data engineering
Beyond the core and advanced-level coursework that are common among all data science programs, some schools also offer mandatory or optional project-based learning opportunities. These projects focus on the real-world application of the skills learned in the program, and can be an opportunity for students to display the skills learned during a program to potential employers. The master’s degree programs at both the University of California-Berkeley and Bay Path University, for example, both include a culminating capstone project that draws upon the skills learned throughout the course of the program. Such projects may extend the length of a master’s degree program, however.
Specializations and concentrations
While the core coursework required for completing a master’s degree in data science is intentionally comprehensive, many programs offer specializations or concentrations so students can carve out a niche within this field. The University of Illinois at Urbana-Champaign offers advanced coursework in cloud computing and scientific visualization, while Texas Tech University has advanced coursework in multivariate analysis and project management. Concentration options may include:
- Machine learning
- Business analytics
- Artificial intelligence
- Data engineering
- Data visualization
While admissions requirements can vary by school, graduate degree programs require the following of aspiring data scientists:
- Successful completion of a bachelor’s degree, as demonstrated by an official transcript from a college or university
- If you don’t have an undergraduate degree in a data-related field (like computer science), you may need to demonstrate that you have sufficient work and educational experience in fundamental concepts on your résumé
- You may also use your personal statement or essay to highlight your unique characteristics and goal for the program
- Letters of recommendation from supervisors, professors or alumni of the program
- Many top-ranked data science programs no longer require you to submit GMAT or GRE scores, though you may need to if you don’t meet minimum undergraduate GPA requirements
- Some master’s degree programs in data science, like the University of Illinois Urbana-Champaign, may require applicants to complete a data proficiency exam
GMAT, GRE & GPA
A majority of online master’s degree programs in data science have waived GRE or GMAT score requirements and, in fact, only two schools on Fortune’s ranking still require applicants to submit scores as part of that application process. That said, you may submit this information particularly if you want to provide additional supporting information that’s helpful in the admissions process. Moreover, GPA requirements also vary by school and may be waived with sufficient work experience.
Which factors drive acceptance?
While admissions officers strive to take a holistic approach when evaluating candidates, they will be particularly interested in your educational background and work experience in a data-related field. Applicants to some data science programs, like the University of Wisconsin-Madison and the University of Connecticut, must show they’ve completed particular quantitative college-level coursework, while other programs like Syracuse University place a greater emphasis on the personal essay and what applicants emphasize they’re looking for in the program, why they chose it, and what their goals are.
The online master’s in data science experience: What is it like to study online?
Online learning has been growing in popularity in recent years, and students considering a master’s degree program in data science can often choose between an in-person or online option within the same school. Data science programs may offer a mix of both synchronous and asynchronous learning, meaning courses that either need to be attended live at a particular time or at the student’s convenience, and could include some limited in-person elements.
For the most part, students can expect to participate in class discussions via video conferencing or using other technology. And because of the online format, many students who pursue a master’s degree in data science are working while attending school with a goal of either switching careers or advancing their current career in data science.
How to choose the best online master’s degree program in data science for you: Factors to consider beyond rankings
Fortune’s ranking of online master’s degree programs in data science is a good starting place when comparing various programs. We emphasize selectivity (schools with top-notch faculty that attract some of the brightest students) and demand (based on the size of the student body), since the people you meet in graduate school could be transformative to your future career.
That said, prospective students should also consider how a particular program will help you achieve your goals and advance in the field of data science. Other factors that may be important include cost, a school’s prestige, its curriculum, and the years of work experience schools may require of applicants.
Start times, schedule, and program length
As data science programs have grown in popularity, schools have beefed up the number of start dates they offer. The University of Illinois and UC Berkeley, the No. 1 and No. 2 ranked programs, both offer three start dates throughout the year. Students may have some flexibility in choosing their schedule and how long it takes to complete the program of their choice, though two years is common.
As indicated, some data science programs include project-based learning opportunities that focus on the real-world application of skills taught in the program. Because these projects can be useful to show potential employers, career switchers may want to consider prioritizing schools with project-based learning opportunities—even if they could extend the program’s length.
As you think about your career goals post-graduation, you should also consider the concentrations offered by various data science programs. By carving out a specialty within data science, that may make you a more attractive job candidate for some employers—and it could increase your earning potential. People with the title of “data scientist” can earn up to $170,000, while manager-level professionals in the field could fetch salaries of as much as $250,000.
The cost of a data science program is undoubtedly a factor to consider when applying to school—and tuition varies widely. Students may be able to pay one-year tuition of about $20,000 (or less) at schools like the University of Illinois Urbana-Champaign, Loyola University Maryland, the University of Missouri-Columbia, and CUNY School of Professional Studies. That said, the cost of tuition exceeds $50,000 at UC Berkeley, Syracuse University, and the University of Denver.
Network and access to alumni
The more students a data science program has, the larger its alumni network. This is important to consider during your selection process, not only because your cohort can be a defining characteristic of your grad school experience even if you’re attending classes online. What’s more, the network and a school’s ability to connect you with alumni may help you when looking for jobs—and particularly if you’re not already working in the field.
Years of work experience
Because many data science programs are seeking out applicants who already have relevant work experience, it may be useful to see how your experience compares. What’s more, the amount of work experience will inherently influence how advanced your fellow students are in their careers. Worcester Polytechnic Institute reports that students have an average of 8 years of work experience, while roughly half of the master’s degree students in New York University’s program enroll straight out of undergrad.
Careers for master’s in data science graduates
There’s a hot job market for data scientists thanks to robust demand—and that means many graduates of master’s degree programs are fielding multiple, six-digit salary offers. Big tech companies are a likely career path for many data scientists. A survey of more than 11,000 data scientists found that the companies with the largest teams of data scientists are Microsoft, Facebook, and IBM. And Apple, for example, pays as much as $182,000 for data scientists.
Financing and scholarships
If your goal of obtaining a master’s degree in data science is to advance within your current company, then your employer may help pay for the cost of the program. New York University grants tuition scholarships to some master’s degree students, while UC Berkeley offers several fellowships of varying amounts.
You may also want to seek out a growing number of scholarship or fellowship opportunities from private organizations. Some examples that are available to master’s degree students include:
- The Association of Computing Machinery (ACM) awards computational and data science fellowships to diverse candidates with a $15,000 annual stipend.
- Acxiom awards $5,000 scholarships to U.S.-based students from diverse backgrounds who are enrolled full-time in various programs that include data science.
- Although it doesn’t specify the amount, the American Statistical Association (ASA) offers a pride scholarship to students enrolled in a data science graduate program and identify as LGBTQ+ or an ally.
Finally, current members of the military or veterans may want to consider covering the cost of your data science program with Post-9/11 GI Bill benefits or the Yellow Ribbon Program, which can cover any tuition and fees not covered by those benefits.