Data science powers more intelligent and better-informed decision-making in virtually every industry. As the field has grown, so has the demand for professionals who can mine and interpret data. Many employers have a rigid list of qualifications that data science candidates must meet to be considered. Is a master's degree among them?
The story of how data science became a career path with staying power originates in the combination of statistics and computer science, a phenomenon that dates back farther than you might think. As long ago as the early 1960s, statisticians like John W. Tukey theorized that computers would eventually revolutionize the field. Their actual impact at the time was minimal only because they were too slow and too expensive.
By the 1980s, the rise of personal computers had broadened access to computer power. Suddenly, you didn't need to be in a cutting-edge lab to explore how a model, test, or system could aggregate and organize large data sets. People began to experiment and develop new methodologies. Companies realized the data they generated could provide answers to fundamental questions.
By the 1990s, some were successfully using that data to support decision-making processes. As one 1994 BusinessWeek cover story put it, "Companies are collecting mountains of information about you, crunching it to predict how likely you are to buy a product, and using that knowledge to craft a marketing message precisely calibrated to get you to do so."
Three decades later, data science has grown to a field that powers intelligent and better-informed decision-making in virtually every industry. The demand for professionals who can mine and interpret data has grown with it. Finding candidates who are well-versed in data analytics is challenging, and competition over hiring them is fierce. Many employers have a rigid list of qualifications that candidates must meet to be considered for a role. But is a master's degree one of them?
In this article, we'll answer the question "do I need a master's for data science?" by covering:
Data science is centered around collecting, organizing, analyzing, and using data to drive strategic decisions. Through a blend of practical statistical analysis and a variety of computational techniques, the field deals with data from the collection stage to organization and analysis. In then follows through to the reporting and communication of what the data means.
In the business world, data science is used to transform data into value by facilitating improved revenue, reduced costs, business agility, improved customer experience, and new products. Data science applications aren't confined to the business world. Its insights also benefit government, nonprofit institutions, academic institutions, healthcare operations, scientific inquiry, and any other enterprise that collects large amounts of useful data.
Data scientists are analytical data experts who have the technical skills to solve complex problems—and the curiosity to explore what problems need to be addressed. They primarily act as problem-solvers for businesses and organizations that can learn and benefit from data.
They're typically tasked with creating insight from missing data, unstructured data, or data that lacks regular structure. Their work involves ordering data to make it more useful, mining it, making relevant assumptions, building correlation models, proving causality, and searching for signs of anything that can drive better business results.
There is also a range of other roles that involve data science work—many of which are found on an organization's data science team. In this sense, you can think of data science as a team sport. Roles are complementary. Each individual possesses a different vocabulary and skill set to support different steps in the data science lifecycle, from building data pipelines to embedding machine learning models into applications.
Common job titles among data science teams include data engineers, who use their software engineering experience to process large datasets. They typically focus on coding, cleaning up data sets, and implementing requests that come from data scientists. From here, data analysts look through the data and provide reports and data visualizations to illuminate insights the data hides.
Data science job listings vary in the amount of education and experience they require. Skilled graduates with an associate or a bachelor's degree in a computer-related field may qualify for entry-level positions in computer programming or systems analytics. With additional work experience, they may advance into computer and IT management or network architect positions.
However, data scientists who conduct advanced organizational problem-solving and leadership skills usually hold graduate degrees. A 2017 report from Burning Glass Technologies reports 39 percent of data scientists and advanced analyst roles require candidates to have a master's degree or higher. An earlier survey from Burtch Works Executive Recruiting suggests as many as 88 percent of data scientists have at least a master's degree.
Some of the most common advanced degree titles in the field are held in quantitative disciplines such as applied mathematics, statistics, computer science, engineering, operations research, and economics. Most straightforward of all is a Master of Data Science (sometimes called a Master of Science in Data Science), which is designed to help students develop the specific skills that recruiters seek to place among their data science teams.
Many graduate-level data science programs include "master of science"—or "MS" or MSc"—in their titles due to their focus on the technical aspects of practice. They typically require students to take core courses in topics like data analysis, data mining, data preparation, data visualization, and decision analytics, IT ethics, and project management. Students are expected to enter with knowledge of data management systems (e.g., Hadoop) and programming languages (e.g., Python, Java, SQL), but the programs provide additional training in these as well. Students must complete substantial field or laboratory work to graduate.
Programs may also involve a capstone project, which helps students round out their training through a culminating academic and intellectual experience. The MS in Data Science degree program at the University of Virginia Main Campus, for example, pairs teams of its graduate students with industry, academic, and community sponsors to apply data science tools and techniques to real-world issues. In some programs, a final thesis is required, either in addition to or in lieu of the capstone project.
Some data science programs may offer students a route to pursuing a specialization in the field by tailoring their degree to meet their interests, such as through electives related to big data, data management, artificial intelligence, among other subfields.
Master's programs in data science typically require students to complete 30 to 40 credits. These programs can be completed in one to three years, depending on the program's format, the number of courses students take per term or semester, and whether the program offers courses year-round.
Programs are available for both full-time and part-time enrollment. The latter accommodate students who intend to work while earning their degree or have other outside obligations that make full-time study inaccessible. The number of quality online programs available in the field has also grown exponentially alongside on-campus programs. Some online programs have flexible enrollment policies that allow students to choose the number of courses they take per term, extending or reducing the time to completion.
While an advanced degree in data science or a related discipline can boost graduates' chances of landing top-paying leadership and management positions, it's not always the most crucial factor when starting a career.
Many entry-level roles, such as data analysts, usually require an undergraduate degree in STEM with experience in areas like programming, computer modeling, and predictive analytics. In some cases, candidates who lack a relevant bachelor's degree may be able to reskill or upskill their technical knowledge through boot camps or massive open online courses (MOOCs). They can use these to earn certification, complete continuing education courses, or show employers that they're committed to growing professionally.
As with many professions, work experience is invaluable in data science. Fortunately, because of the high demand for candidates capable of solving complex data science challenges, many internship opportunities are available. Some of these may lead to a full-time job offer.
Lastly, a strong portfolio will help you catch the attention of hiring managers. Yours should highlight three to five practical projects relevant to the jobs for which you've applied. For example, if you're applying to positions that require machine learning expertise, building end-to-end projects that use automated analytical model building may be useful. On the other hand, if you're applying for analyst positions, data cleaning and storytelling projects may be worthwhile.
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