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 2022’s best online master’s degrees in data analytics.
Top 5 schools for master’s in data analytics
The best online master’s in data analytics 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. Champaign, IL $13,176 in-state $28,464 out-of-state Application fee: $70 Baltimore, MD Application fee: $75 Pensacola, FL Application fee: $30 Stillwater, OK $481.15 in-state $611.35 out-of-state Application fee: $50 Denton, TX Application fee: $50 Unless otherwise indicated, data is drawn from the Integrated Postsecondary Education Data System and College Scorecard.
Insights from a data analytics graduate
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. Accreditation: Southern Arkansas University is regionally accredited by the Higher Learning Commission (HLC). 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. Sure, learning how to use the latest machine learning frameworks are important, but you’ll have your whole career to improve on that. You won’t spend much time on the fundamentals once out of school. ZDNet: How did you make the most of your data science degree program so that it prepared you for post-grad jobs? TR: I had already spent a few years working after undergrad and chose to make a career pivot before entering grad school. At that point, networking and learning how to work in industry was less important than shoring up my technical weaknesses. My background was in math and statistics, so I tried to focus my electives on areas where I was weak — mainly computer science fundamentals. It may have been easier to take a more familiar elective, but I wanted to come out of the program as a more well-rounded data scientist.
What to expect in an online master’s in data analytics degree program
Core classes emphasize theories and principles of statistics, data analysis, and database systems. Learners study computational tools and techniques and complete hands-on learning activities and group projects. Students have options to complete electives in advanced or niche data analytics topics. Many online programs incorporate a cumulative capstone requirement. Synchronous programs present coursework to students sequentially, while asynchronous degrees provide more flexibility. Some online data analytics master’s degrees incorporate scheduled meeting times and face-to-face activities.
Data analytics courses
Common courses for data analytics degrees build a foundational understanding of the field. They also train students in essential tools and techniques of data acquisition, visualization, and analysis. Data visualization Data visualization coursework introduces students to theories, techniques, and tools of visualizing, investigating, and understanding data. Learners explore data representation, volume rendering techniques, and applied human perception alongside topics like advanced display devices and the future of data visualization. Applied machine learning By examining learning models and algorithms, students learn to apply strategies like decision trees and logistic regression to performance issues and signal models. Database systems Database systems courses introduce learners to concepts and theories of database management. Coursework explores relational and non-relational database management systems. Students study database system architecture, data models, database design, and information organization from user and system perspectives. Data mining A data mining course covers techniques for extracting and analyzing data to discover and assess patterns and clusters. Data mining coursework integrates theoretical content with practical application of methods to preprocess, visualize, and find coherence in data.
Data analytics degree levels
Students can earn an associate degree in data analytics in two years or less, while a bachelor’s degree typically includes four years of coursework. Earning an undergraduate degree sets the foundation for entry-level positions across economic sectors or for continued study. A master’s degree in data analytics spans two years or less, while a doctorate can last four years or more. These degrees allow for advancement to administrative and managerial roles. Outside of data analytics degrees, learners have options to complete data science bootcamps or earn a certificate in the discipline. These often take less time and money than earning a data analytics degree.
Associate in data analytics
Length: Two years Cost: $12,000-$30,000 Post-grad careers: Computer support specialist, data entry associate, junior data analyst An associate in data analytics generally includes 60 credits of coursework in data science fundamentals. Students learn data policy and decision-making as they develop an understanding of the role of data analysis in organizations. Data analytics associate degrees also introduce learners to tools and techniques of data analysis. Experiential learning opportunities accompany collaborative projects and possible opportunities for internships and practicums.
Bachelor’s in data analytics
Length: Four years Cost: $18,000-$50,000 Post-grad careers: Operations research analyst, market research analyst, data scientist Through roughly 120 credit hours of coursework, a bachelor’s degree unites business, information technology, and mathematics content. Students learn to use cutting-edge tools and technology to assess data and guide decisions based on their findings. Many business analyst and data science professions require at least a bachelor’s degree in data analytics or a comparable field. A data analytics bachelor’s program also prepares learners to enter a graduate degree.
Master’s in data analytics
Length: Two years Cost: $15,000-$55,000 Post-grad careers: Senior data scientist, data engineer, data management consultant A master’s degree in data analytics hones students’ existing analytical and critical-thinking skills. Through an advanced curriculum that incorporates theoretical and practical data analysis content, learners gain proficiency in using data to optimize decision-making practices. Master’s degrees in data analytic typically include about 30 credit hours of coursework. Students attend lectures and take part in independent and group projects, practical learning exercises, and capstone requirements.
Doctorate in data analytics
Length: Four or more years Cost: $30,000-$80,000 Post-grad careers: Business intelligence officer, chief data officer, data analytics professor A doctorate in data analytics emphasizes qualitative research in the field of data science. Learners also build programming, communication, and decision-making skills. With a doctorate in data analytics, individuals achieve leadership roles in academia and industry alike. After completing advanced coursework in topics such as statistical analysis, data mining, and analysis methodologies, students complete a qualifying exam and a dissertation.
In conclusion
A data analytics master’s degree incorporates interdisciplinary coursework designed to grow existing analytical and critical-thinking skills. Many industries value graduates’ advanced abilities to assess raw data using the latest tools, technologies, and techniques. This degree may open the door to many data science jobs. Use the information and resources above to help you find the data analytics program that’s right for you.
