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:
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:
Data science professionals can use their knowledge and skills in many ways and in almost every industry. You might specialize in business intelligence or robotics or healthcare informatics. There are almost too many options.
90 percent of data scientists hold master’s degrees, and 47 percent hold doctoral degrees. ( )
The Bureau of Labor Statistics sets median data scientist annual pay at just over $100,000. Top-paying sectors include ( ):
- Computer and peripheral equipment manufacturing ($148,290)
- Semiconductor and other electronic equipment manufacturing ($142,150)
- Specialized information services ($139,600)
- Data processing, hosting, and related services ($126,160)
- Accounting, tax preparation, bookkeeping, payroll services ($124,440)
|University and Program Name||Learn More|
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.
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.
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.
Core courses in master’s in data analytics programs usually touch on:
More technical master’s in data analytics programs will also cover topics like:
Some of the top master’s in data analytics programs in the United States can be found at:
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:
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.
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.
Consider the following:
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.
Questions or feedback? Email firstname.lastname@example.org