If you're interested in earning a data science master’s, you’re already aware that this is a rapidly expanding field with numerous job opportunities and a median annual salary of $ 111,490. Yet, even though you know that obtaining this degree is an excellent way to advance your data science career, you may be hesitant about the cost, time, and effort involved—and are considering a faster and cheaper certificate program.
This article on data science master's vs. certificate programs examines the pros and cons of each option and covers subjects like:
A recent survey by Burtch Works found that 50% of the data science professionals had earned their master’s, and a study conducted by Stitch several years ago revealed that around 40% of data science professionals in both senior and chief leadership roles held master’s degrees. While earning a data science master’s may not be the sole path to a successful career in this field, these studies strongly indicate that possessing this advanced degree is definitely advantageous—and expected and/or required in many upper-level data science positions.
If you’re applying for an entry-level data science job, earning a certificate can be a good way to demonstrate to a prospective employer that you have some data science skills. Likewise, if you’re in a lower-level data science role and want to acquire a specific new skill that will help you become better at your job and be seen as a more valuable asset to your employer, a data science professional certificate is a good move. However, these are short-term career strategies (with short-term payoffs).
Below, you’ll find the pros and cons of both data science master's and certificate programs:
Simply put, data science is a computer science discipline that focuses on data. Data science professionals utilize advanced data analysis tools, such as machine learning, data visualization, artificial intelligence, and high-level statistical analysis to collect and interpret data sets.
If you're applying to a data science master’s program, it’s beneficial to have at least two years of relevant work experience in data management, business analytics, or business intelligence. While there are programs that accept inexperienced applicants—those with a bachelor's degree in an unrelated subject looking for a career change—it’s likely you’ll be required to complete bridge coursework, in addition to your regular course of study.
A basic understanding of one or more of the top computer programming languages (Python programming, R programming, SQL, or Java) will bolster your application, and knowing how to manage data structures is helpful. In addition to specific programming skills, many data science program applicants have worked as data analysts and possess the skills and experience necessary for that role.
Overall, applying to a data science program is similar to other master’s programs, including submitting personal statements, letters of recommendation, and undergraduate transcripts. You'll also likely send in GRE scores, though many programs no longer require them, especially those offered online. Even highly regarded schools like Tufts University are loosening their standardized test requirements in favor of a more holistic approach to admissions, favoring related work experience over test scores.
There's no universal curriculum for a data science master’s. The University of Virginia "draws from multiple disciplines to give students a comprehensive and holistic approach to data science." Common elective and required courses include:
Not all relevant data science programs offer each of these courses, and not all of them are titled Master of Science in Data Science. Other related degree titles include:
Many, but not all, programs allow for specialization. Common specializations include:
Data scientists utilize AI, which is capable of processing enormous amounts of data, to improve efficiency, spot trends, and decipher data.
It's hard to be a data scientist and not specialize in big data or data analytics. Big data courses teach data wrangling techniques to handle the massive and ever-increasing volume of data.
Bioinformatics is the intersection of healthcare and data. According to the National Human Genome Research Institute, it "is a subdiscipline of biology and computer science concerned with the acquisition, storage, analysis, and dissemination of biological data, most often DNA and amino acid sequences."
Computational finance "uses the tools of mathematics, statistics, and computing to solve problems in finance," according to Carnegie Mellon.
A large and growing discipline in its own right, cyber security involves applying computer science principles to keep governments, companies, and individuals safe. You may be able to specialize in this field through a data science program, but there also are several excellent cyber security master's programs to choose from.
Data engineering is the movement, storage, exploration, and transformation of data. Data engineers need excellent warehousing skills and work to provide data scientists with good, viable information.
Data scientists who specialize in machine learning are in high demand, as this is one of the most technically demanding roles in data science. Southern Methodist University students who complete the specialization "learn to utilize advanced computational algorithms needed to build platforms that expand the boundaries of machine cognitive function to provide solutions, advance automation and evolve processes."
LinkedIn consistently ranks data science as one of the fastest-growing fields. Hiring in this profession has increased by 46 percent since 2019. Data scientists are needed by tech giants like Microsoft and IBM to government agencies to healthcare organizations. Wherever there is data—and data is everywhere—data scientists are required.
The top jobs that you can find with a master's in data science include:
The top data science master's programs include:
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