Why Should Someone Consider Getting a Master’s in Data Science Degree?
It’s no secret that our digital world is rapidly expanding and becoming more complex. Naturally, as technology advances, hundreds of thousands of new tech positions are inundating the job market. Unfortunately, the United States has a deficit of professionals qualified to fill these positions. In 2015, U.S. employers posted as many as 600,000 unfilled high-paying tech jobs.
Many employers seeking data science experts have a comprehensive list of qualifications they require a candidate to meet before they even consider inviting that candidate in for an interview. Quite often, a master’s degree is included on this list of requirements. In fact, according to a 2017 report from Burning Glass Technologies, 39% of data science and analyst roles require that candidates either have a master’s degree or doctorate. That’s a significant slice of the data-scientist-position pie you’ll be missing out on if you only have a bachelor’s degree.
There are a few reasons for this. Data scientists often need to draw on a wide variety of skills in order to meet all the requirements of their positions. These skills include statistics, programming, engineering, artificial intelligence, and even communications. A master’s program equips students with the systematic training they need to apply theoretical concepts to real-world dilemmas. A good data science master’s program will also teach students how to use all the concepts and methods they’ve learned with the concepts and methods they already know—in essence, a good master’s program should teach students how to unite their diverse skillsets in order to best serve the requirements of their positions.
Top 5 Full-Time Master’s in Data Science Programs
The data science program at Carnegie Mellon University combines interdisciplinary coursework with cutting-edge research in order to equip students with the tools they need to pioneer new developments in the data science field, whether they go on to lead in academia, the public sector, or various industries. Incoming students at Carnegie Mellon generally have a technical degree (such as engineering and computer science) and 1-3 years of work experience. The program is typically completed in three semesters, and graduates generally end up working in financial service firms, consulting companies, technology companies, and start-up organizations.
Carnegie Mellon stands out thanks to the breadth of data science master’s programs it offers in one place. These programs vary based on incoming students’ backgrounds, focuses of study, intended outcomes, and detailed logistics, and they include: Information System Management; Information System Management with Business Intelligence and Data Analytics Concentration; Public Policy and Management, Policy Analytics Track; Educational Technology and Applied Learning Science; Computational Data Science; Intelligent Information Systems; Language Technologies; Machine Learning; Statistical Practice; and Business Analytics Track.
Columbia’s data science master’s program is one of the most highly-rated and selective advanced data science programs in the country. Students are provided with opportunities to conduct original research, produce capstone projects, and interact with industry partners and renowned faculty. Generally, students choose an elective track focused on entrepreneurship or a subject area covered by another center at Columbia University. Columbia boasts an extraordinarily powerful and vast alumni network, along with connections across most major industries. Faculty at the Data Science Institute pride themselves on being profoundly invested in their students, and they are passionate about sharing their knowledge of research and problem-solving with those they mentor.
At Columbia, students also have access to the Professional Development and Leadership program, which enhances the Ivy League education by providing students with exclusive courses and connections to help launch a data science career in the most optimal, expedient way. 98% of graduates obtain a job or internship placement three months or fewer after graduating.
The University of Chicago
The Master of Science in Data Analytics at the University of Chicago is at the forefront of novel developments in the analytics field. Professors educate students on rarely-seen-before analytical models, and they also teach students how to become lifelong learners and continue advancing their knowledge after they graduate. Most students in the program majored in economics, finance, mechanical engineering, computer science, or business administration.
Students pursuing a master’s in data analytics at the University of Chicago (53%) are typically between the ages of 25 and 29 years old, with 18% from 20-24 years old, 14% from 30-34 years old, 7% from 35-39 years old, 5% from 40-44 years old, and 3% over the age of 45. Not only are students in the program diverse in age, but they also have professional work experience in a diverse set of fields—38% of students have 4-10 years of work experience, and 19% boast over a decade of work experience in such fields as tech, business consulting, government, marketing, finance, and healthcare.
Georgia Institute of Technology
The Master of Science in Analytics at Georgia Tech is an interdisciplinary analytics and data science program that expands learning opportunities in statistics, operations research, computing, and business by leveraging the expertise of the Scheller College of Business, the College of Computing, and the College of Engineering. Giving students access to these world-renowned colleges through one program equips students with the ability to yield deep insights into analytics problems.
The Master of Science in Analytics On-Campus program can be completed in one year. It boasts dedicated job placement assistance, on-campus analytics job fairs, and a conference travel budget for each student.
The data science master’s program at Harvard University is jointly led by the university’s computer science and statistics faculties, and is administered through the Institute for Applied Computational Science (IACS). The program focuses on reproducible data analysis, collaborative problem-solving, visualization and communication, and security and ethical issues that arise in data science. Students are required to take at least three semesters (twelve courses in total) in order to complete the program, and some students choose to take a fourth semester in order to either take additional courses or complete a master’s thesis or research project.
Harvard’s data science master’s program is notable for its strong preparation in statistical modeling, machine learning, optimization, management and analysis of enormous data sets, and data acquisition.
Top 5 Part-Time Master’s in Data Science Programs
The Tufts School of Engineering offers an interdisciplinary online data science program. The program can be completed in as little as one year, or at a student’s own pace. In order to obtain a master’s in data science at Tufts, students must complete at least ten courses at the 100-level or above with grades of S (satisfactory) or a B- or above. Prerequisites include a bachelor’s degree in a STEM field like mathematics, science, engineering, or computer science. However, applicants with bachelor’s degrees in non-STEM fields have the option of beginning study with a Certificate in Data Science for their first term or year.
Tufts’ master’s in data science stands out thanks to its dual degree master’s program with Tufts Gordon Institute. This program intends to develop a student’s innovation, leadership and management skills alongside their technical knowledge, and will confer two degrees: an M.S offered jointly by the Tufts Department of Computer Science and the Department of Electrical and Computer Engineering, and an M.S. Innovation & Management. Students can earn both degrees at a reduced cost, and in as little as two years.
At Syracuse University’s iSchool, students gain the analytical, technical, and managerial expertise that employers so desperately seek. The program was developed in collaboration with the Whitman School of Managements, and equips students with the ability to draw insights in both information science and management. The program is intended to help students understand on a deep level the impact data science has on businesses. Students also have the ability to customize coursework based on their goals.
The program at Syracuse equips students with data techniques and tools like Python, SQL and R in order to apply natural processing languages to data mining, text mining, and machine learning tasks with unstructured big data. Students then use these data insights to explore the business impact of data science and earn a Certificate of Advanced Study in Information Security Management or Enterprise Technology Management without taking any extra classes.
University of California at Berkley
The Master of Information and Data Science program delivered from the UC Berkeley School of Information (100% online) prepares data science professionals to become leaders in their chosen field. The program’s multidisciplinary curriculum, renowned faculty recruited from world-class data-driven companies, and an extraordinary online network makes Berkeley a no-brainer for any professional looking for flexibility in their data science master’s degree pursuits.
Berkeley’s program draws on computer science, social sciences, statistics, management, and law in order to maintain one of the most multidisciplinary online programs on the market. The 27-unit online program is designed specifically for the working professional’s schedule and can be completed on one of three paths, chosen by the student: accelerated, standard, or decelerated.
The online Master of Science in Applied Data Analytics at Boston University’s Metropolitan College is a hands-on program that exposes students to a wide variety of database systems, data mining tools, data visualization tools and packages, Python packages, R packages, and cloud services such as Amazon AWS, Google Cloud, and Mass Open Cloud.
BU’s program prides itself on equipping students with deep knowledge of analytics tools and an understanding of data mining and machine learning approaches that will enhance students’ ability to critically analyze real-world problems and grasp the possibilities and limitations of analytics applications.
Georgetown’s Master of Science in Data Science and Analytics provides students with rigorous and comprehensive training in computational, mathematical, and statistical methods in order to prepare them to be leaders in their careers as data science and analytics experts. Students build a deep knowledge base and foundation in data science and analytics fundamentals, including big data and cloud computing, machine and deep learning, interactive and complex visualization methods, advanced databases, objects, algorithms, complexity, text mining, natural language processing, and advanced mathematical and statistical modeling.
Georgetown stands out thanks to its focus on critical skills like decision science, data communication, visual narrative development, teamwork, and complex problem-solving techniques.
What Are the Benefits of Graduating with a Master’s of Science in Data Science?
The benefits are many and varied. Quant Crunch report explains that “Demand for a new breed of professionals skilled in data, analytics, machine learning, and artificial intelligence requires a requisite response from both higher education and workforce development.” Graduating with a master’s in data science equips professionals with the data science skills needed to become a legendary professional.
A master’s in data science program builds on specific skills acquired through bachelor’s degrees in math, computer science, engineering, or other technology-related degrees, as well as professional experience, to impart a holistic set of skills upon its graduates. This gives graduates with a data science masters degree an edge in the job market—it shows employers that the candidate has systematic and well-defined training in both the theory and application in data analytics. A data science degree will also demonstrate to prospective employers that the candidate is willing to commit time, energy, and effort to methodical learning in the data science field.