A whopping 88 percent of data scientists hold either a master’s degree or a PhD. Clearly, most data scientists have considered the facts—the data, if you will—and concluded that a graduate degree is an essential credential.
Multiple indicators suggest that it is indeed worth the effort and expense to complete a data science graduate program. Data scientist consistently ranks among the top jobs in the United States. And there’s plenty of demand within the profession: in 2020, Quanthub reported a shortage of qualified data scientists, noting three times as many data science online job listings as job searches.
Completing a Master of Science in Data Science will improve both your data skillset and competitiveness in the job market. According to Patrick Lewis, a graduate of Columbia University‘s MSDS program, “The skills I learned in the master’s program were essential in getting through the interview process for each company. Without those skills, I would not have had the competitive edge to secure a data scientist job at a top tech company.”
Your options won’t be limited to jobs that include the term ‘data scientist’ in the title, though. This degree also prepares you for roles like:
Provided you have the experience and background necessary to complete this degree, a master’s in data science can qualify you for a broad range of challenging, remunerative careers. This article addresses what you can do with a masters in data science by covering these questions:
The Master of Science (MS) in Data Science typically targets students with a STEM background—often in analytics, information technology, computer science, or mathematics.
Programs typically focus on developing hard skills, although they may also encompass subjects like IT ethics or project management. University of California – Berkeley claims to produce graduates who are well-versed in:
Not all schools offer this degree as a Master of Science in Data Science. As in many fields, different universities utilize varied nomenclatures to indicate similar—sometimes identical—degrees. Depending on where you pursue your master’s in data science, you might earn a:
Title variations may indicate a program’s focus. For example, an MS in Computer Science with a data science concentration may include coursework focused on computer systems and software systems as well as skills like machine learning. However, title variations may also simply indicate which department or school of the university confers the degree.
For business-minded students, a Master of Business Administration (MBA) with a data science focus may provide better opportunities than an MS. That’s because MBAs prepare students for upper-level tech management roles rather than advanced technical positions.
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Before addressing why someone should consider a master’s in data science, it’s essential to understand when they shouldn’t. Knowing the pitfalls of pursuing a master’s can help determine whether you are ready for one. According to Forbes contributor Meta S. Brown, the top four reasons not to get a master’s in data science are:
While it’s true that nearly half of all data science professionals hold master’s degrees, you do not unequivocally need one to start your career. What you need is transferable work experience, additional education (which could be self-study), and a relevant bachelor’s degree (think STEM).
If you decide to go the graduate study route, make sure you are ready for the accelerated pace of advanced study. Some programs offer foundation courses for inexperienced students, but entering a master’s program without the requisite background unquestionably heightens the challenge. Students with relevant educational or work backgrounds often graduate in a better position than those who use a master’s to begin their careers. Fluency in a programming language like Python or R, plus appropriate work experience in data science, is a good starting point for any student.
Many data science master’s candidates currently work in fields that use math, statistics, and data. One of the best reasons to earn a master’s is to advance a stagnating career—especially if you’re working a data analytics job.
Knowing that people go into master’s in data science programs with different goals, it should not be surprising that they leave with various career opportunities.
Columbia University published a list of the top data scientist jobs, which includes positions like:
According to Glassdoor, the top 50 jobs in America are littered with data science positions, including data scientist (3) and data engineer (6). While you may qualify for these roles with the right undergraduate degree and work experience, a master’s education will certainly improve your prospects.
Yes, attending a top-ranked school can increase your prospects. Big-name schools attract big-name faculty and partners resulting in excellent networking opportunities. This could mean getting on the shortlist of hiring managers who frequently struggle to sort through (digital) stacks of resumes.
Some of the top master’s in data science programs can be found at:
Where will you work after you’ve earned your MSDS? NYU provides a detailed breakdown of its graduates’ placements. They include:
The average annual salary for a Master of Science in Data Science is $92,500, according to Forbes. That said, many qualified professionals command significantly more. Three-figure salaries, further augmented with incentives, are not uncommon in job postings.
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