The choice between earning a Master of Science in Computer Science (MSCS) or a Master of Science in Data Science (MSDS) isn’t always easy. Recent graduates coming out of technical bachelor’s degree programs and early-career professionals already working in technology may be unsure where their aptitudes and interests lie. Career advancement in tech is often fluid. Programming skills are as valuable for computer science as they are in data science careers. Software developers become data scientists, and data scientists become software engineers. Disciplines like machine learning fall somewhere in between computer science and data science. And some prospective students just find the computer science curriculum and data science curriculum equally fascinating.
Further complicating matters is that both the MSCS and the MSDS correlate with high salaries, booming demand, and rapid advancement in software development and data analysis. Students researching master’s degree programs because they want to earn more money or transition into managerial positions quickly find that either degree can propel a career in technology forward.
The key to choosing the right academic pathway is letting go of generalities. Accept that both degrees are valuable, and you can stop thinking about your future in terms of computer science vs. data science and refocus on the specific benefits. Below, you’ll find more information about the different career paths associated with these degrees, the job outlook in both fields, and how Tufts University School of Engineering approaches each pathway in its online programs. Armed with this information, you should have no trouble choosing between a computer science master’s degree and a data science master’s degree.
Career Paths in Computer Science and Data Science
More professional pathways are open to Master of Science in Computer Science graduates than technology professionals who study data science. Computer science is a broad interdisciplinary field made up of an ever-growing number of practical and theoretical specializations. A list of every possible computer science job would be hundreds of entries long and include positions in a variety of fields, including:
- Artificial intelligence
- Cloud services
- Database administration
- Hardware engineering
- Information systems management
- Information technology (IT) management
- Information theory
- Machine learning
- Network architecture
- Security engineering
- Systems administration
Perhaps you noticed that ‘computer science’ isn’t on the list. Some MSCS graduates do go on to become computer and information research scientists. They spend their days working in laboratories or for think tanks, tackling thorny problems in niche fields like quantum computation or program semantics. However, most people who study computer science in graduate school go on to work in applied computer science roles like:
- Applications architect
- Cloud engineer
- Cybersecurity engineer
- Development operations engineer
- Information security manager
- Hardware design engineer
- Product manager
- Site reliability engineer
- Solutions architect
- Software developer
- Software engineer
- Software architect
- Systems engineer
Some of the above positions are open to professionals with bachelor’s degrees and the right experience, but that changes when you add the words ‘senior,’ ‘principal,’ ‘vice president,’ or ‘director’ to those titles. To step into senior-level roles in computer science, you will almost certainly need a graduate degree.
“Both the MS in Computer Science and the MS in Data Science are salary boosters.”
Data science—which is itself a branch of computer science—is unusual in that the master’s is the entry-level degree in the discipline. Surveys find that nearly 90 percent of data scientists have advanced degrees, and about half of all job listings for data science positions state outright that candidates must have a master’s degree or doctorate. The competition in this field is fierce, but the pay is excellent for those with titles like:
- Business analytics developer
- Business intelligence engineer
- Data analytics manager
- Data architect
- Data engineer
- Data mining specialist
- Data visualization developer
- Data warehousing engineer
- Decision scientist
- Deep learning engineer
- Director of data science
- Domain expert analyst
- Machine learning engineer
- Machine learning scientist
Be careful that you don’t put too much stock in job titles when exploring possible career pathways. Employers create job titles, and one firm’s developer might be another firm’s software engineer. Titles in data science can be incredibly confusing because the discipline is so new (and possibly because machine learning roles are muddying the waters). Some companies don’t differentiate between data scientists and data analysts, for example. Confusion around titles may not be an issue for graduate students who have computer science or data science experience. For recent bachelor’s degree graduates or career-changers looking at computer science vs. data science, more research into both fields’ opportunities can be helpful.
Earning Potential and Job Outlook in Computer Science vs. Data Science
Fortunately, there’s a wealth of opportunity for both computer scientists and data scientists. Computer science jobs and data science jobs exist across industries. Tufts University School of Engineering graduates work for tech companies like Amazon and Facebook, financial firms like Goldman Sachs, and media companies like Bloomberg.
Demand may be the more important metric to consider when looking at computer science vs. data science degrees.
Consider the computer science job outlook. The U.S. Bureau of Labor Statistics (BLS) predicts employment in computer science will grow by 15 percent over the next 10 years, but that projection only applies to researchers and only represents about 5,000 new jobs. Meanwhile, jobs for software developers will grow by 22 percent, resulting in the creation of more than 300,000 new positions. And employers are creating new jobs for information security analysts at an even faster rate, but that still adds up to fewer open positions for analysts than for developers.
Put simply, the demand for computer science degree holders will grow over time, but some subdisciplines will see more or faster job growth than others. That may not be important to you if you’re intensely interested in cybersecurity, network architecture, or another computer science subdiscipline. If, however, you’re intensely interested in job security and maximizing your earning potential, you may be better off setting your sights on a career in a field like software engineering, where open positions are plentiful and pay well.
On the data science side of the computer science vs. data science question, demand is booming and shows no signs of slowing. Employers posted 34,000 jobs for data scientists on sites like LinkedIn and ZipRecruiter over the past 12 months. Sources predict the worldwide market for Big Data will grow to almost $230 billion over the next five years, and employment in the field will increase by 19 percent over the next decade. Companies of all sizes and across industries are desperate to leverage the quintillions of bytes of information human beings generate daily.
Despite differences in demand, both the MS in Computer Science and the MS in Data Science are salary boosters. Computer science bachelor’s degree holders’ median salary is $85,000 per year, while the median salary for computer science master’s graduates is about $102,000 per year. Given that most data scientists have master’s degrees or doctorates, determining how those degrees affect salary is challenging. According to the BLS, data science professionals, regardless of title, earn about $101,000, and the most recent Robert Half Technology Salary Guide reported that the median salary for data science roles in 2021 could increase to $129,000 per year.
Keep in mind that other factors also affect salary. Geography can be a strong predictor of earning potential, though the impact of location is more profound in computer science than in data science. A computer researcher in Texas might earn $87,000 while someone in the same role could earn $97,000 in Virginia. A data scientist working in Texas, on the other hand, will make just about as much as one employed in Virginia and nearly as much as one working in California (a state where the cost of living is also much higher).
MSCS vs. MSDS: What’s the difference?
You can’t look at employment numbers alone when answering the computer science vs. data science question. Program content is an equally important consideration. Many data scientists have computer science degrees, and a computer science specialist with a master’s degree can leap into data science. Figuring out whether you belong in an online MSCS program or an online MSDS program may be as easy as looking at the admission prerequisites, cost, delivery, and course work in each.
“Regardless of which degree you choose, you’re committing to a lifetime of learning.”
At Tufts’ School of Engineering, the admissions committee takes a holistic approach when reviewing applications to both programs. They look at academic and professional accomplishments, and neither program requires GRE scores. The online MSCS admissions standards and online MSDS admissions standards differ, however, and the committee judges applicants hoping to get into the two programs using different criteria. The 33-credit online Master of Science in Computer Science program looks for applicants who have demonstrable knowledge of computer architecture, data structures, functional programming, and object-oriented programming languages. Master of Science in Data Science candidates must show strong analytical skills to get into the 32-credit program.
If you meet the admissions standards for both programs, cost can help guide your decision at some schools but may not be much of a differentiator at others. Tufts’ online MSCS costs approximately $56,000 while the online MSDS costs roughly $54,300. Both computer science master’s and data science master’s candidates are eligible for the same financial aid packages and other assistance like the alumni “Double Jumbo” scholarship, scholarships earmarked for students from bridge universities, Yellow Ribbon scholarships, and other scholarships offered by the School of Engineering.
Program format is another important consideration—especially if you’re looking at schools that don’t offer both degrees online. This won’t be a deciding factor at Tufts because both degrees for distance learners are delivered entirely via online courses. Students in each program can complete all required coursework—including the two-course, hands-on, culminating computer science or data science project—without ever stepping foot on campus.
Looking at the curriculum and the student experience in online MSCS and online MSDS programs can help you choose the right degree pathway. Both paths include machine learning in Tufts’ curriculum, but otherwise, computer science and data science students do very different work. MS in Computer Science students study:
- Computational theory
- Database systems
- Operating systems
- Programming languages
- Software engineering
Students in the MS in Data Science program study:
- Artificial intelligence
- Big Data
- Mathematical data science
- Probabilistic systems analysis
- Probability theory
- Python programming
- Reinforcement learning
- Statistics and statistical analysis methods
Consider your interests and your long-term career goals in the context of those lists. Students exit the online MSCS programs knowing how to develop realistic programs in various programming languages and predict algorithms’ complexity, including algorithms for trees and graphs and as part of dynamic programming. They can also provide mathematical proofs of algorithms’ properties and have the soft skills necessary to effectively take constructive criticism, present to a group and make a compelling argument in and out of tech settings. MSDS candidates learn to formulate real-world problems using data science tools and practices. They can collect and clean the data needed to solve various problems (even when data is raw and unstructured), apply advanced analytics techniques to find answers, and then evaluate answers for errors and risks.
Should you get a master’s in computer science or data science?
There’s no one correct answer to this question. Your choice of degree will ultimately be an exceedingly personal one because it’s not a question of computer science vs. data science. An online Master of Science in Computer Science can take your career in many different directions, but so can an online Master of Science in Data Science. Those most interested in computational design, architecture, and programming theory may naturally gravitate toward computer science. Some people choose the MSCS because they see it as the more versatile degree, but that might not matter if you’re a dedicated data fanatic. If you’re passionate about analytics, machine learning, AI, and the insights buried in data, you may be better off in data science.
Graduates from both programs go on to work at high-profile companies in and out of tech. With a strong portfolio and any master’s degree, you should be able to take your career anywhere you want it to go. Regardless of which degree you choose, you will be committing to a lifetime of learning.
Technology changes rapidly, and staying relevant requires not just self-study but a solid educational foundation. The skills you learn and the relationships you develop in an MSCS or MSDS program equip you to evolve with technology—for years to come. Applying to Tufts is a smart first step.