By building statistical, analytical, and decision-making skills, a data analytics master’s degree can prep you for in-demand positions.
A data analytics master’s degree blends mathematics and statistics content with information technology training. Today, data analytics professionals help businesses identify ways to reduce costs and increase efficiency. In healthcare, data analysts determine ways to streamline care. Workers in municipal and government roles apply data analytics to predict and prevent everything from crime to traffic jams. According to the Bureau of Labor Statistics, roles for operations research analysts are projected to grow 25% by 2030. Other positions available to data analytics degree-holders, such as management analysts, are estimated to increase by 14% by 2030. As a high-demand, versatile option, data analytics master’s degrees have a lot to offer. Here’s a look at 2021’s best online data analytics master’s degrees. To provide the most relevant rankings for readers, we pull publicly available data from the most reputable sources. Read ZDnet’s ranking methodology to find out what information we used to create the below ranking of the best online bachelor’s in finance degrees. Unless otherwise indicated, data is drawn from the Integrated Postsecondary Education Data System and College Scorecard. Tim Roy is a data science manager at SparkPost. Tim has an M.S. in mathematical statistics from Virginia Commonwealth University and is a Fellow of the Society of Actuaries. He’s been in data science and analytics for 12 years and leads the data science department for the SparkPost division of Message Bird (which acquired SparkPost in May 2021). ZDNet: Was there anything about your data science master’s degree program that you didn’t expect or anticipate? Tim Roy: A wide variety of backgrounds from peers. Some students had strength in computer science, pure math, applied math, physics, etc., but also students who previously studied in unrelated disciplines are now making a pivot. ZDNet: What was the most challenging, rigorous course you took in your data science degree program? What advice would you give to students who are about to start this course? TR: My first exposure to machine learning, other than toying with the knobs in scikit-learn, was from a theoretical perspective. I took a couple courses that were mostly pencil + paper proofs, reading academic papers focused on theory, and math jargon like Hessian matrices, Lagrange multipliers, and duality. I benefited more from this course than any other as it allowed me to think about solutions that were not already neatly programmed in a python library and allowed me to be more creative by beginning with the fundamentals, then seeing what libraries already existed that I could tailor to my needs. My advice is to try to take some courses like theory but also others in areas like ethics, where you are getting exposure to things you typically would not when employed as a data scientist. Ethics is becoming an increasingly important part of the data and AI conversation so having a starting point that predates doing the actual work is helpful.
