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What Is a Master’s in Data Analytics?

There is still much we don't know about extracting, organizing, interpreting, and drawing useful conclusions from data. Image from Unsplash

Christa Terry September 8, 2020

Some people assume that a Master of Science in Data Analytics is just a dumbed-down data science degree. In fact, data analytics programs are every bit as rigorous and cover a lot of the same ground.

In this course, participants learn core principles, concepts, and methods of continuous improvement and explore the history of continuous improvement efforts. In a series of lectures, Master Black Belt in Six Sigma Shane Wentz, Ph.D., enables learners to enhance, optimize, and stabilize business processes and to augment quality control through varied methodologies. Participants gain the skills and knowledge to reimagine internal processes, engage in advanced problem-solving, and empower employees. The course covers high-level foundational principles of Lean, Six Sigma, and DMAIC processes before diving deep into Lean Six Sigma methods. Learners will analyze the tools of process mapping, Kaizen (a continuous improvement method/tool deemed a prerequisite for Lean transformation success), Lean (a continuous improvement philosophy that emphasizes streamlined workflows and reducing waste), and the process improvement methodology driving DMAIC: Define, Measure, Analyze, Improve, and Control.

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We generate data at a staggering rate. According to the International Data Corporation, humans had created 33 zettabytes—that's 33 trillion gigabytes—of data by 2018. The same report projects that number will reach 175 zettabytes by 2025.

That's a mind-blowing figure. The insights hidden within all that information could be hugely valuable to industries as diverse as finance and scientific research. Unfortunately, because the useful data is buried so deep, much of it will go unfound and unused.

There is still much we don't know about extracting, organizing, interpreting, and drawing useful conclusions from data. Businesses and organizations increasingly rely on data in the decision-making process, yet we aren't anywhere close to tapping this information treasure trove's full potential.

Most experts agree that we currently suffer from a lack of qualified analytics professionals. According to a report published by IBM and Burning Glass Technologies, the demand for qualified data analytics profession is growing faster than supply. Employers often struggle to fill open analytics, data science, and business intelligence jobs.

That means that now is a great time to get into data analytics. There are positions out there for bachelor's degree holders who majored in computer science, statistics, or analytics. Still, as Randy Bartlett of Blue Sigma Analytics told the Northwestern University Graduate Programs blog, twenty-five percent of hiring managers "prefer or require candidates to have a graduate degree, making an advanced credential increasingly important if you want to stand out and be competitive."

Numerous graduate degrees can lead to a career in data analytics, but you can't go wrong with a Master of Science in Data Analytics or a related degree. These programs teach students how to collect, organize, and analyze information using the latest techniques and technology, preparing them for stable, well-paying careers in analytics.

In this article, we answer the question What is a master's in data analytics? and cover the following:

What is a master's in data analytics, and who typically pursues this degree?
How does a master's in data analytics differ from a data science master's?
Is data analytics the same thing as business analytics?
How long does it take to earn a master's in data analytics?
What do students in data analytics master's programs typically study?
Which graduate schools are known for their master's in data analytics programs?
What careers can someone pursue with a data analytics master's?
How much can someone earn with a master's in data analytics?
Is a master's in data analytics worth it?

What is a master's in data analytics, and who typically pursues this degree?

Master's in data analytics programs prepare students to step into specialist roles in analytics, business intelligence, and even data science. Typically, these students have strong STEM backgrounds, strong business backgrounds, or both. Many already work in analytics, data engineering, data architecture, or computer science and have established data set mining, data modeling, and business analytics skills.

People often think of analytics as a money-making tool, and while it can indeed help businesses boost profits, it can also be used to contribute to the "greater social good" (as Yeshiva University puts it on its Data Analytics & Visualization Master's program page). Analytics professionals also use data to "address pressing challenges in healthcare, housing, poverty, education, and transportation."

A master's in data science program may confer various degrees, including the:

Master of Computer Science with a concentration in Data Analytics
Master of Professional Studies in Data Analytics
Master of Science in 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
Master of Science in Data Science with a concentration in Data Analytics
Master of Science in Statistics with a concentration in Data Analytics

How does a master's in data analytics differ from a data science master's?

There's some confusion about what data analytics is and isn't, and how it differs—or whether it differs—from data science. Perhaps the two disciplines were clearly delineated at one point, but those days are over.

A Google search for 'data analytics versus data science' yields results all over the map. Some people claim that analytics is more business- and strategy-focused than data science. Others claim the exact opposite. It probably doesn't help that some colleges and universities have re-skinned their data analytics programs as data science programs, nor that there are Master of Science in Analytics programs that are virtually indistinguishable from data science programs. The University of Chicago's graduate-level analytics curriculum, for example, includes courses like Data Engineering Platforms, Data Mining Principles, Data Science for Consulting, and Machine Learning and Predictive Analytics.

When schools offer both a master's in analytics and a master's in data science, the analytics degree is usually administered by a business school; the data science degree is usually administered by an engineering school. Both degrees can lead to careers in business analytics and data science. At North Carolina State University at Raleigh's Institute for Advanced Analytics, for example, 30 percent of Master of Science in Analytics graduates become data scientists, while only 20 percent become analysts.

Is data analytics the same thing as business analytics?

The distinction between data analytics and business analytics is also frustratingly vague. Academic institutions and employers define the scope of each of these terms in any way they wish, and may actually use them interchangeably.

Martin Schedlbauer, director of Northeastern University's information and data sciences programs, claims that, "In the simplest terms, data is a means to the end for business analysts, while data is the end for data analysts." Interestingly, some people use almost the same language to describe the difference between analytics and data science: by this formulation, the former is a means to the end, while the latter is purely concerned with data.

Who's right? In a way, they all are. Data analytics, business analytics, and even data science can all be utilized to make practical, data-driven business decisions. All three disciplines can involve technology like AI and machine learning.

How long does it take to earn a master's in data analytics?

Most master's in data analytics programs require students to complete about 30 credit hours of coursework plus an internship or capstone project. Full-time programs may take anywhere from 18 months to two years; part-time programs can take longer. Intensive accelerated data analytics master's degree programs can be completed in 10 or 12 months, provided students take classes during winter and summer sessions. Longer programs often cover more ground, but the coursework may also cover foundational material that advanced students have already mastered. Students with strong technical skills and work experience related to computer science may get more out of a shorter program entirely focused on higher-level analytics skills and technologies.

What do students in data analytics master's programs typically study?

Core courses in master's in data analytics programs usually touch on:

Advanced data analysis
Applied statistics
Computer programming languages like Python, SQL, and R
Data collection
Data management
Data warehousing
Data visualization
Predictive models and descriptive models
Reporting
Statistical analysis
Systems architecture
Tools like Tableau and Hadoop
Working with structured and unstructured large data sets

More technical master's in data analytics programs will also cover topics like:

Artificial intelligence
Data mining
Data structures and algorithms
Information systems
Machine learning
Software engineering
Visual analytics

Which graduate schools are known for their master's in data analytics programs?

Some of the top master's in data analytics programs in the United States can be found at:

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 of Business Analytics through the Sloan School of Management
North Carolina State University, 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

What careers can someone pursue with a data analytics master's?

A master's in data analytics is a versatile degree that prepares the holder for analytics careers in varied fields. Companies across sectors are looking for new and innovative ways to leverage the data they collect; graduates of data analytics master's programs have what it takes to help them. The IBM report linked above found that the sectors that employ the most data analytics professionals are finance, insurance, information management, manufacturing, scientific services, and technology. Data analysts also work in agriculture, energy, real estate, and entertainment. Some of the job titles they hold are:

Analytics architect
Analytics manager
Analytics specialist
Analytics product manager
Big data analyst
Business intelligence analyst
Business intelligence architect
Data analyst
Data analytics consultant
Data engineer
Data mining analyst
Data scientist
Marketing analytics manager

Some choose to specialize after earning a master's in data analytics. You might, for instance, become a healthcare data analyst, though it's entirely possible to succeed in analytics as a generalist. In its predictions for the future of the workforce, the World Economic Forum stated that 96 percent of companies are already planning to create permanent analytics roles in the near future and 85 percent of companies will be using big data and analytics technologies by 2022.

How much can someone earn with a master's in data analytics?

Answering this question is tough because salaries in data analytics are influenced by many factors, including job title, experience, location, and industry. The average salary associated with a Master of Science in Data Analytics is about $77,000, but that figure was calculated using self-reported data from a broad range of professionals: early-career data analysts earning $60,000, senior data analytics managers earning more than $80,000, and data scientists and data engineers earning $100,000 or more.

How much you can earn after graduating from a master's in data analytics program will likely depend more on your career trajectory than on the specific diploma you hold, though a master's degree will almost certainly impact your earning potential positively. Most experienced data analytics professionals earn somewhere between $82,750 and $138,000.

Is a master's in data analytics worth it?

Consider the following:

A master's in data analytics can open doors: While you can launch a career in data analytics and advance in the field with just a bachelor's degree, you may advance more quickly with a master's degree.
You'll make valuable connections in a master's degree program: The experiential learning components of master's in data analytics programs may actually have a bigger ROI than the coursework.
Data analytics is a high-tech discipline: Most graduate-level analytics programs are focused on "advanced analytics" (versus business intelligence). The online Master's in Data Analytics and Visualization at Yeshiva University, for instance, includes AI and machine learning techniques in the curriculum.
The master's is becoming the entry-level analytics degree: Burtch Works found that 89 percent of working data analysts with less than three years of experience have master's degrees.

So, yes, a master's degree in data analytics is worth it. Data analysts are some of the most in-demand professionals in the world. The rate of information creation is likely going to keep increasing. And even though headline predictions that AI will replace analysts abound, chances are that human insights will always be a necessary part of data analytics. You can't automate creativity with a machine learning algorithm, which means that there will always be a place in analytics for highly trained experts with credentials like the master's in data analytics.

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