Data Science

Best Schools for Data Analytics Master’s Degrees

Best Schools for Data Analytics Master’s Degrees
Students graduate from the strongest data analytics programs with the technical knowledge and specialized skills necessary to help companies and organizations leverage vast quantities of data. Image from Unsplash
Christa Terry profile
Christa Terry September 10, 2020

The best data analytics programs do more than just confer degrees. Students graduate from these colleges and universities with technical skills, professional connections, and the tools they'll need to advance in analytics—or data science.

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Headlines have been predicting the demise of data analytics and the discipline’s inevitable replacement by machine learning systems for some time. Given that, you could be forgiven for assuming that a data analytics master’s is less valuable than a more technical data science degree.

But while it’s true that automation is playing an ever-bigger role in analytics, data analytics continues to thrive. A Master of Science in Data Analytics—or any of the many closely related analytics degrees—can still lead to lucrative careers in data science, business intelligence, data engineering, or management.

In many ways, the degree actually looks like a safe bet. Experts predict that humans will create 175 zettabytes of digital data by 2025 (a zettabyte is one trillion gigabytes). That’s a lot of data to sift through.

The key is choosing the right data analytics master’s program. Programs at the top schools with data analytics master’s degrees tend to prioritize what might be called “advanced analytics” and Big Data analytics over business intelligence. The online Master’s in Data Analytics and Visualization at Yeshiva University, for example, includes topics like predictive analytics, Artificial Intelligence, and machine learning. Students graduate from the strongest data analytics programs with the technical knowledge and specialized skills necessary to help companies and organizations leverage vast quantities of data.

In this article about the best schools for data analytics master’s programs, we cover:

  • What is a data analytics master’s degree, and what career paths does it support?
  • Is a master’s degree necessary to work in analytics?
  • Which schools are known for having the best data analytics master’s programs?
  • Are the top colleges and universities for data analytics master’s expensive?
  • What sets the top data analytics master’s programs apart?
  • Are the programs above more selective than other programs?
  • Do the top data analytics master’s programs include data science in their curricula?
  • How can I determine which master’s in data analytics program is right for me?

What is a data analytics master’s degree, and what career paths does it support?

Because colleges and universities use different naming conventions for similar degrees, the Master of Science (MS) in Data Analytics is just one of many data analytics master’s degrees. This is important to keep in mind when searching for the best data analytics master’s programs. Some schools confer the MS or the similarly named Master of Data Analytics. Others offer degrees like the:

  • Master of Computer Science with a concentration in Data Analytics
  • Master of Professional Studies in Data Analytics
  • Master of Science in Applied Data Analytics
  • Master of Science in Computer Science with a concentration in Data Analytics
  • Master of Science in Data Analytics and Business Intelligence
  • Master of Science in Data Science and Data Analytics
  • Master of Science in Statistics and Data Analytics

Core courses in all of these technically advanced analytics master’s programs typically cover topics like:

  • Applied Statistics: In data science, applied statistics involves using statistical techniques to interpret, analyze, and draw conclusions from data. It covers methods like regression analysis, hypothesis testing, and time series analysis, which are essential for understanding data trends and patterns.
  • Artificial Intelligence (AI): AI in data science refers to the simulation of human intelligence processes by machines, especially computer systems. This includes the development of algorithms and models for tasks such as problem-solving, decision-making, and pattern recognition, often using data as input.
  • Data Management: This involves the practices of collecting, keeping, and using data securely, efficiently, and cost-effectively. In data science, it encompasses data storage, data governance, data warehousing, and ensuring high data quality for accurate analysis.
  • Data Mining: Data mining in data science is the process of discovering patterns and extracting insights from large datasets using machine learning, statistics, and database systems. It involves analyzing data from different perspectives and summarizing it into useful information.
  • Data Structures and Algorithms: In the context of data science, understanding data structures (like arrays, lists, trees, and graphs) and algorithms (methods for solving problems) is crucial for organizing, processing, and analyzing large datasets efficiently.
  • Data Visualization: This entails the representation of data in graphical format to help people understand the significance of data by placing it in a visual context. It is a key aspect of data science for presenting data findings in an accessible and compelling way.
  • Information Systems: In data science, information systems refer to the study of how data is collected, processed, and distributed within an organization. This includes the analysis and design of systems to solve problems using data.
  • Machine Learning: A subset of AI, machine learning in data science is about developing algorithms that enable computers to learn and make predictions or decisions based on data. This is central to predictive analytics in data science.
  • Operations Research: This involves using advanced analytical methods to help make better decisions. In data science, it includes optimizing products, processes, or operations and making decisions based on quantitative analysis.
  • Predictive Modeling: This is the practice of using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data, an essential part of data science for forecasting.
  • Programming Languages (Python, SQL, R): These are tools in data science for data manipulation, analysis, and visualization. Python is known for its versatility, R for statistical analysis, and SQL for database management.
  • Quantitative Analysis: This refers to the use of mathematical and statistical techniques in data science for analyzing large amounts of data, crucial for making data-driven decisions.
  • Reporting: In data science, reporting involves generating summaries of data findings, presenting them in a readable, understandable format, often using visualizations, to communicate insights to stakeholders.
  • Statistical Analysis: This is the process of collecting, exploring, and presenting large amounts of data to discover underlying patterns and trends, fundamental in data science for making predictions.
  • Systems Architecture: This refers to the structured design and implementation of data systems, including hardware and software that support data processing and analysis in data science.
  • Tools like Tableau and Hadoop: Tableau is used for data visualization, making complex data understandable, while Hadoop is used for storage and large-scale processing of data sets, essential in managing big data in data science.
  • Visual Analytics: This combines data analysis and visualization techniques to understand complex data, identify trends, and make decisions, often involving interactive visual tools.
  • Working with Structured and Unstructured Data: This involves dealing with data in various forms. Structured data is organized and easily searchable, while unstructured data (text, images, video) is less organized and requires more complex methods to analyze and use effectively in data science.

Some programs require students to complete an internship, practicum, capstone project, or thesis. Non-thesis graduate programs may devote more credit hours to in-class coursework than to research.

After graduation, data analytics master’s degree holders become data analysts, data architects, and analytics managers, as well as data scientists and data engineers. They work in almost every field, though one report put together by IBM and Burning Glass Technologies found that more data analytics professionals work in finance, insurance, information management, manufacturing, scientific services, and technology than in any other industries.

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Is a master’s degree necessary to work in analytics?

It’s possible to launch a successful analytics career without a graduate degree—for now—but you may advance more slowly without a master’s and hit a wall when it comes to advancement and earning potential. While only twenty five percent of hiring managers in this space are looking for candidates with graduate degrees, nearly 40 percent of job listings for advanced analytics positions require applicants to have a master’s degree or PhD in analytics, data science, or a related discipline.

You may also need a data analytics master’s if your goal is to move into senior-level or management and leadership positions. It’s also worth noting that a master’s degree can maximize your earning potential in this field. While the average salary associated with a Master of Science in Data Analytics is about $77,000, senior data analytics managers can earn well over $100,000.

Which schools are known for having the best data analytics master’s programs?

The colleges and universities below boast well-regarded data analytics programs:

  • Carnegie Mellon University, which offers a Master of Science in Information Technology in Business Intelligence & Data Analytics (MSIT-BIDA)
  • Columbia University, which offers a Master of Science in Applied Analytics
  • Georgia Institute of Technology (Main Campus), which offers a Master of Science in Analytics
  • Massachusetts Institute of Technology (MIT), which offers a master’s in business analytics through the Sloan School of Management
  • North Carolina State University at Raleigh, which offers a 10-month Master of Science in Analytics
  • Purdue University (Main Campus), which offers a Master of Science in Business Analytics and Information Management through the Krannert School of Management
  • Texas A & M University – College Station, which offers a Master of Science in Analytics through Mays Business School
  • University of Chicago, which offers a Master of Science in Analytics
  • University of Oklahoma – Norman Campus, which offers a Master of Science in Data Science and Analytics through the Data Science and Analytics Institute

There are also strong online data analytics master’s programs at the following schools:

  • Arizona State University – Tempe, which offers a Master of Science in Program Evaluation and Data Analytics online
  • Boston University, which offers a Master of Science in Applied Data Analytics online
  • Colorado State University – Global Campus, which offers a Master of Science in Data Analytics online
  • Columbia University, which offers its part-time Master of Science in Applied Analytics online
  • Georgia Institute of Technology, which offers a Master of Science in Analytics online
  • Northeastern University, which offers a Master of Professional Studies in Analytics online
  • Northwestern University, which offers a Master of Science in Analytics online
  • Pennsylvania State University – World Campus, which offers a Master of Professional Studies in Data Analytics online

Are the top colleges and universities for data analytics master’s expensive?

Cost is almost always a factor when choosing a master’s degree program, which is why it’s somewhat frustrating that tuition for the above programs is all over the map. Tuition for most data analytics master’s programs falls somewhere between $35,000 and $45,000, but the MIT tuition is significantly higher at between $66,000 and $88,000 (depending on the analytics capstone course sponsor offset), while tuition for the Georgia Tech program is just $9,900 total. No hard-and-fast rules dictate how graduate schools price their programs, so never assume that shorter programs or online programs are less expensive. Some colleges and universities actually charge slightly more when you factor in the additional fees distance learners pay.

What sets the top data analytics master’s programs apart?

The schools with the best graduate-level data analytics master’s programs provide the tools to hone problem-solving skills, think analytically, use the latest analytics and data science technologies, and confidently work with various stakeholders. They also offer:

  • Faculty members who bring industry experience to the classroom: Data analytics is a rapidly changing discipline; the most effective professors have current industry knowledge.
  • A curriculum that includes both statistics and comp sci: Well-rounded data analytics master’s programs teach programming techniques and concepts related to data science along with statistical problem-solving and analytical decision-making.
  • Soft skills as part of the curriculum: Schools with strong data analytics master’s programs make sure students graduate with robust communications, presentation, project management, and leadership skills.
  • Numerous opportunities for curriculum customization: Not all data analytics master’s programs have concentration or specialization options. Still, many allow students to customize their educational experience by choosing electives.
  • Hands-on experiential learning projects: Students in the above programs work with real-world data-sets and even real-world companies, so graduates emerge with refined analytics skills.
  • Built-in, comprehensive career support: The best data analytics master’s programs are offered by colleges and universities that have relationships with employers and researchers and can provide students with professional connections that lead to future opportunities.

Are the programs above more selective than other programs?

It should come as no surprise that data analytics master’s programs offered by schools like MIT and Columbia are more selective when it comes to admitting applicants.

The programs we identify in this article are, for the most part, geared toward experienced professionals who already have analytics skills. The online Master of Science in Applied Data Analytics program at Boston University, for instance, looks for “mid-career IT professionals or students with a computer science background who seek to train their focus on analytics.” Admissions officers will look as closely at your work experience as at your college transcripts.

Surprisingly, the best schools for data analytics master’s usually don’t require applicants to hold a bachelor’s degree in a specific field. Some don’t even require applicants to submit GRE scores. Instead, they require applicants to have an academic or professional background in information systems, cloud computing, computer science, analytics, or statistics. Some data analytics schools expect applicants to know the most common programming languages analytics (e.g., Python, SQL, and R).

Do the top data analytics master’s programs include data science in their curricula?

Modern on-campus and online data analytics degree programs have a lot in common with data science master’s degree programs; the gulf between these two disciplines is narrowing rapidly. Some schools have even transformed their data analytics programs into data science programs instead of maintaining two distinct programs.

When colleges and universities offer both a Master of Science in Data Analytics and a Master of Science in Data Science, one is typically administered by a college of business while the latter is administered by a school of engineering. The curricula in these programs may be largely the same, which may be why students who graduate from analytics programs frequently go on to work in data science careers. At North Carolina State University’s Institute for Advanced Analytics, about 30 percent of students who graduate with a Master of Science in Analytics become data scientists. Only 20 percent become data analysts.

How can I determine which master’s in data analytics program is right for me?

Any of the programs we identify will give you a solid education in advanced analytics, but choosing a degree program isn’t as easy as enrolling in the highest-ranking one. You’ll also need to think about your finances, personal and professional commitments, and location. Keep in mind, too, that each program we’ve listed here has unique curriculum and features, which you should review carefully. What may be most important of all is whether you’ll enjoy the work you’ll do over the one or two years you’re enrolled in a data analytics master’s program.

If you want to spend your career doing more technical work or eventually transition into data science, look for STEM-designated programs or programs that touch on high-tech topics like predictive analytics. On the other hand, if you’re dreaming of becoming a business analytics consultant or you’re fascinated by business intelligence, a business-focused program may be the better fit. In both cases, make sure the program you choose includes plenty of project work and lots of internship opportunities. The more practical experience you have upon graduating, the better your career prospects will be—regardless of which school you attend.

(Updated January 3, 2024)

Questions or feedback? Email editor@noodle.com

About the Editor

Tom Meltzer spent over 20 years writing and teaching for The Princeton Review, where he was lead author of the company's popular guide to colleges, before joining Noodle.

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