It’s official: 10 years after it first won the title, data scientist has retained its claim to being the “sexiest job of the 21st century,” at least according to Harvard Business Review.
Looking at the job market, it’s hard to argue: the US Bureau of Labor Statistics foresees 36% growth in data scientist headcount over the next decade, over 7x the average projected growth rate for all jobs during this period. In exchange for their skill in applied mathematics, machine learning, business analytics, data management, and data visualization, these new data science professionals will be entitled to substantial compensation: $138,780 on average in the US, more than twice the median household income of $67,521.
A booming job market and hefty salaries are encouraging new undergrads to declare data science majors at universities across the country and analytically minded professionals from all walks of life to transition into data science from their old fields.
Given the advanced training and the boost to compensation that a graduate degree in data science can provide — by some accounts up to $60,000 with commensurate experience — it’s no surprise that many are choosing to avail themselves of the many new data science master’s programs that institutions of higher education are offering both on campus, and, increasingly, online.
If you’ve done any research into graduate study in data science, you know how saturated the market is. But with so much choice, how do you know where to start? That’s where we come in: our guide to data science masters programs will walk you through exactly what you can expect to learn in graduate school and what should go into your decision. We’ll also present our picks for master’s programs that will set you down a new data science career path or help you move further along one you’ve already begun.
What is data science?
Before we begin, as we always do, a refresher on what data science is, exactly. According to IBM, data science is:
“a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. [It] encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.”
The responsibilities of a data scientist include:
Identifying research interests and questions
Preparing unstructured data (or “raw data”) for analysis
Analyzing data with machine learning algorithms and models
Communicating findings through data visualization tools
Why should you pursue a data science master’s program?
There are certainly paths into data science that don’t involve graduate studies. In many cases, students are able to land a job after completing a bachelor’s degree or data science bootcamp.
But if you want to be a serious candidate for the most competitive jobs or move into more senior roles, there’s a good chance that you’ll need a data science MS.
A master’s in data science degree is also a good option for someone with background (and a bachelor’s degree) in a different STEM field (computer science, applied mathematics, etc.) or even in the social sciences or humanities who needs the endorsement and training that a data science master’s can provide. In the end, the candidate with the best skills and fit will get the job, but having a master’s degree in data science from a prestigious institution can often help “non-traditional” candidates land a first-round interview.
While the curriculum of a master’s in data science degree will resemble that of a data science bachelor’s degree to an extent, generally courses will be more advanced, with fundamentals either not offered or required to be completed in advance of beginning the degree proper. DS master’s students can expect to learn more about the following during their course of study:
Computer science and information technology, including advanced courses in artificial intelligence, machine learning, data science algorithms, and software design
Statistics, including advanced courses in exploratory data analysis and visualization, statistical modeling, and statistical analysis with SQL and R
Data science principles, including research design, data management, and data science ethics
Industry-specific electives such as Marketing Analytics, Sports Performance Analytics, Big Data in Finance, etc.
For most master’s programs, a data science course of study is supplemented by career-readiness initiatives, including capstone projects, internships, and even job placement programs.
In addition to data scientist roles, graduates of DS master’s programs are qualified to apply to data analyst, business intelligence analyst, data engineer, and data architect roles.
Data analyst is an extremely broad title — so the day-to-day of two data analysts at different companies might look very different. Typically, however, a data analyst gathers data and readies it for analysis, and then analyzes it to yield business insights that the analyst will then communicate to relevant stakeholders, often through data visualization. Though there is some overlap between the responsibilities of a data scientist and a data analyst, data scientists are usually more concerned with creating new techniques, tools, and processes for data analysis, while data analysts more often use existing techniques, tools, and processes.
According to Salary.com, in the US the average data analyst salary is $81,719. If you’re especially interested in this career path, dive deeper with our guide to data analytics certifications.
Business Intelligence Analyst
There’s certainly lots of overlap between a data analyst and a business intelligence analyst. Indeed, sometimes it just comes down to what title a company gives a position. But, in theory at least, what sets a BI analyst apart? According to Indeed, a business intelligence analyst — or, sometimes just “business analyst” — is more narrowly focused than a data analyst on the metrics, sometimes called “key performance indicators” (KPIs), that can be used to evaluate a company’s performance. By definition, business intelligence and business analytics focus on the past, while the umbrella field of data analytics can include areas like prescriptive and predictive analytics that focus on future activity.
According to Salary.com, in the US the average business intelligence analyst salary is $84,803.
A data architect designs efficient pathways, protocols, and systems for data collection, preparation, and storage according to a business’ specific needs, capacity and resources.
According to Salary.com, in the US the average data architect salary is $124,771.
Data engineers and data architects are commonly confused, but the distinction is actually quite simple. While a data architect designs efficient pathways, protocols, and systems — plans them out — a data engineer focuses on creating them.
According to Salary.com, in the US the average data engineer salary is $112,555.
What should you look for in a master’s in data science program?
Before you fall in love with a master’s in data science program and start assembling your application, it’s crucial to ascertain whether your application will be welcome. When schools list requirements and prerequisites for prospective students, they mean it. Their top priority is turning out stellar data science professionals who will make an impact. Accordingly, they will only accept students who show an aptitude to thrive in their program.
This might mean that a program will give preference to computer science or applied mathematics majors or require a certain GPA or certain scores on the Graduate Record Examination (GRE) or other standardized tests. If you’re a non-traditional applicant or right on the edge qualification-wise, there’s no harm in shooting off an email to the admissions director before you spend the time crafting an application.
While the core curriculum of most data science programs will be fairly consistent, different schools will have different strengths and emphases. As you are researching, try to identify whether topics like big data, machine learning, data mining, or business analytics are important to you, and, if so, make sure that you narrow down your choices to the programs that will offer courses in these areas.
These days, many data science master’s programs can be completed online in addition to on campus. Some schools only offer an online program, often with the option to attend part-time or full-time.
Learning data science online gives much-needed flexibility to those who don’t wish to relocate, want to continue working, or need to care for a loved one. But online study isn’t for everyone. Some see social benefit in meeting their teachers and fellow students in person, not to mention the potential for in-person networking. Others simply like the campus experience and the motivation and inspiration that comes with it.
And all online programs aren’t created equal: some will offer asynchronous content so that you can self-pace your study, others will offer live instruction, while still others will provide some hybrid of the two.
In the end, you’ll have to decide which option will work best for your particular situation and desires and adjust your research accordingly. There are loads of options to learn data science online and in person, so whichever way you go you can be confident in finding a program that will work for you.
Before even applying to a program, you want to be sure it will get you where you want to go — there’s no use spending time and money on education if you won’t recoup this investment with a great career in data science. Oftentimes, departments will proudly list graduate placement on their websites. You might also hit up LinkedIn to see where alumni have ended up.
Let’s face it: higher education is expensive. Spurred by breakneck growth in university administrations and college campuses, tuitions are higher than they’ve ever been and only growing.
Only structural changes will provide relief for those seeking undergraduate and graduate degrees, but online study can offer a substantial improvement when it comes to the total cost of education. As we’ve mentioned above, not having to relocate, take time off from your job, or hire a caretaker for your loved ones — not to mention transportation costs — already puts a significant amount of money back in your pocket.
It gets even better. As U.S. News & World Report notes, “the average per credit price for online programs at the 168 private colleges that reported this information is $488 – lower than the average tuition price for on-campus programs at ranked private colleges, which is $1,240 among the 113 colleges that reported this information.”
It’s worth keeping in mind that some disagree that online education offers a cost benefit compared to in-person study — your best bet will always be to crunch the numbers yourself — but if you’re sitting on your couch trying to decide between studying data science online and heading to campus, keep in mind the potential financial upshot of staying right where you are. To help assess the return on education, check out our guide on salary outcomes for data science master's graduates.