Graduate education provides a proven means to gaining and demonstrating skills employers seek. An overwhelming majority of data scientists hold either a master's or PhD, meaning that you likely need an graduate degree to advance the field.
Still, deciding to pursue a master's in data science is not as easy as absentmindedly throwing that checkout counter candy bar on top of your other groceries. A graduate degree requires considerable commitments of time, effort, and financial resources. The last can be mitigated through financial aid and graduate assistantships; even so, you'll still likely go into pocket to complete this degree.
The National Center for Education Statistics says that 56 percent of students who complete a Master of Science (MS) take out student loans. If you are one of the millions with undergraduate debt, you'll want to consider how much additional debt you're willing to take on as you plan your graduate education.
The good news is you do not need to sell your soul to a loan officer in exchange for education and networking opportunities. Many of the least expensive master's programs cost under $30,000. Some even cost as little as $10,000, which is less than room and board fees at some schools.
Learn about the benefits of the least expensive data science master's programs. This article covers:
The Master of Science in Data Science is a graduate-level degree that educates students in:
Not every data science degree program covers each of these topics, nor would you want them to. Degrees geared towards students looking to transition into a data science career from a different field likely focus more on basic data science techniques—for instance, programming languages, such as SQL, Java, and Python—that professionals with a few years of work experience are likely already knowledgeable about. Evaluating the coursework of programs you want to apply for is a great way to determine which degrees fit your goals.
Many graduate programs are designed to attract students with existing data science backgrounds, such as someone who is currently working in big data analytics or business analytics. These programs generally rely on students with some proficiency in programming languages and statistical modeling techniques.
For example, the University of California - Berkeley program only accepts students who can demonstrate "an understanding of—or, a proven aptitude for and commitment to learning—data structures and discrete mathematics." Like the UC Berkeley program, the CUNY School of Professional Studies requires all applicants to demonstrate proficiency in:
Though the CUNY School of Professional Studies offers a bridge program for students rusty in these subjects, it is specifically designed for students with a quantitative background. Why is this important? Curricula geared towards professionals can differ significantly from those geared towards novices.
The School of Professional Studies includes core courses with titles like like:
In contrast, Northeastern University, which does not have as strict admissions requirements, has core courses with titles like:
Though not a hard rule, students with existing experience in the field usually benefit the most from completing a data science program. Having existing knowledge allows students to use their elective credit hours to develop a specialty or maybe create a capstone project that wows potential employers. For instance, you may choose to take several classes with a cybersecurity focus and alter your career path upon graduation.
Data science encompasses many different careers and degrees. A Master of Science in Data Science is not the only way to prepare yourself for the field.
According to a report from Burtch Works, an executive recruiting agency that specializes in data jobs, 40 percent of data science professionals have a master's degree, and 48 percent have a PhD. These degrees may not all be in data science, though they all deal with quantitative subjects. Specifically, professionals have degrees in:
Master of Science designations that apply to data science include:
Some Master of Science in Analytics programs are very close to a data science master's, though these, strictly speaking, are two distinct degrees. Even a Master's in Business Administration (MBA) can even lead you to a data science career—if you decide to complete a data science track. Finally, you can get a job with a relevant bachelor's degree, although you will likely need to supplement it with bootcamps and certificate programs.
How is this relevant to earning a master's in data science? Because graduate-level education is nearly essential to securing a job, even though there are many ways to slice the apple.
If you decide to earn a master's in data science, the program should include coursework in topics relevant to your career and have the right price tag.
According to US News & World Report, the total cost of a two-year graduate degree can be over $100,000. However, you don't need to pay that much for quality education.
Some of the more affordable programs cost between $20,000 and $40,000. Outliers may cost as little as $10,000. Many of the least expensive data science master's programs are offered online, though some in-person programs that fit the criteria as well.
Here, affordability is defined as under $1,000 per credit. Keep in mind that you will likely need to pay extra fees, which may include room and board, technology fees (especially for online programs), and even university health insurance. Additionally, programs at state universities frequently offer a discount for residents or regional students. In this case, tuition is calculated for in-state students (who typically make up most of the student body in these programs).
Merely having a master's degree improves your chances to land a job. It is becoming a baseline qualification, even when its not absolutely required There is a high premium on technical skills in data science. Gaining the right skillset can be more important than school rankings.
This doesn't mean you should jump for the least expensive program you can find. You want your education to come from an accredited institution. Organizations like the Higher Learning Commission and regional bodies evaluate schools based on thorough criteria and confer accreditation on those that meet their standards. It's possible for an unaccredited school to offer a worthwhile program, but there's no guarantee, and there's a greater chance that the program is substandard. Accreditation confers a predefined level of quality.
Distance learning programs frequently target professionals who need to work or meet other pressing obligations during their studies. These are more likely to be part-time programs with a flexible schedule. They typically offer curricula identical, or nearly identical, to their on-campus counterparts.
Neither of these degrees is better or worse than the other; it depends on your situation. For instance, you may look to attend an in-person program because you want to take full advantage of networking opportunities to meet professors and interact with other students. A working data science professional whose employer is helping to pay for the cost of a degree may opt for an online Master of Science with some flexibility to meet work obligations. It is essential to consider your priorities during the decision-making process.
According to US News & World Report, expected salary is an important determinant in how much you should borrow for a master's program. "There's a standard rule of thumb that you should not be borrowing more than what your expected first-year wages are," says Mark Schneider, who is the vice president of American Institutes for Research in the article.
While nothing is guaranteed, a master's in data does lead to quite a few high-paying jobs, which means you may have more leeway in taking out loans. The average salary for a master's in data science-holder is $92,500, according to PayScale, but the earning potential can be far higher. Students who attend top programs usually have access to better networking opportunities and career services, potentially leading to better, higher-paying jobs after graduation.
The Georgia Tech online master's degree in data science is affordable and well-regarded. Many other top degree programs are far more expensive. It can cost a full-time NYU student who lives on campus nearly $40,000 for a single semester of data science education. Attending a highly-visible, recognizable school may be better for your long-term career goals, but at a much greater cost. If you are receiving funding from an employer to complete a degree program, you might be asked to choose the most affordable one that you can find.
There is no blanket statement for which master's program best meets your needs, especially in a skill-based field like data science. Deciding on one requires an honest look at your situation and expectations. If the circumstances are right, completing an affordable master's in data science can pay big dividends.
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