There's a lot of confusion these days about what data analytics is and isn't, and how it differs from data science. On the website insideBIGDATA, business technology consultant Rick Delgado writes that data analysis involves "combing through data to find nuggets of greatness that can be used to help reach an organization's goals... [It] tends to be slightly more business and strategy focused" than data science.
On the other hand, Adam Hunt, chief data scientist at RiskIQ, told CIO Magazine that people use data science, not analytics, to come to "conclusions that drive your data forward. If you're not solving a problem with data... that's just analysis. If you're actually going to use the outcome to explain something, you're going from analysis to science. Data science has more to do with the actual problem-solving than looking at, examining, and plotting data."
Who's right? Paradoxically, they both are. That's because the gulf between data analytics and data science seems to be narrowing; as it does, definitions of each shift. Some colleges and universities, like CUNY City College, have actually transformed their data analytics programs into data science programs. Others offer one or the other, while a few offer both a Master of Science in Data Analytics and a Master of Science in Data Science (which are typically administered by a business school and an engineering school, respectively).
Outcomes suggest a degree in data analytics can lead to careers in both business analytics and data science. About 30 percent of students who graduate from North Carolina State University at Raleigh Institute for Advanced Analytics with a Master of Science in Analytics, for example, become data scientists, while only 20 percent become analysts.
Some people will tell you that a master's in data analytics isn't worth it in 2020 because most companies are looking for data scientists, not analysts. That guidance may be out-of-date; increasingly, there's no hard line between the two. Today's graduate-level analytics programs are often focused on "advanced analytics" and have a lot more in common with data science programs than business intelligence programs. Machine learning? Predictive modeling? You'll learn about both in the top data analytics master's programs.
In this article about whether a master's in data analytics is worth it, we answer the following questions:
Data analytics master's degree programs prepare students with strong STEM and/or business backgrounds to step into specialist roles in analytics, business intelligence, and even data science. Core classes in master's in data analytics programs focus on topics related to collecting, organizing, and analyzing information using a variety of techniques. Coursework typically touches on:
Many programs also cover more technical topics like:
Most graduate programs in data analytics also require students to complete a capstone course, thesis, research project, or internship. These experiential learning opportunities enable students to pit their evolving skill set against real-world challenges to prepare them for their future analytics careers.
Graduate schools have different naming conventions when it comes to master's programs in analytics and approach these programs differently, too. You might earn a Master of Science in Analytics or a Master of Data Analytics degree. Some programs pair business intelligence or applied statistics and analytics while others pair analytics and data science or visualization. There are technically advanced analytics master's programs and programs that devote more credit hours to teaching students about the types of business challenges that analytics can solve.
What you need to know is that nearly all of them will, to some degree, teach you how to:
Whether a given program is "worth it" to you will have more to do with your career aspirations, your interests, and your background than the diploma they confer. If you want to do technical work or go into data science, look for STEM-designated programs or programs like the online Master's in Data Analytics & Visualization at Yeshiva University, which includes AI and machine learning techniques in the curriculum. If you're more interested in analytics-enabled jobs or think you might want to go into BI consulting, a business-focused program may be a better fit.
Earning this master's degree can take your career in a lot of different directions. Employers in nearly every sector, from engineering to education, use big data. The sectors that employ the most data analytics professionals include finance, insurance, information management, manufacturing, scientific services, and technology. There are also data analytics jobs in agriculture, energy, entertainment, and real estate sales. After earning a master's in data analytics, you might hold any of the following job titles:
How much you can earn with a master's in data analytics depends on many factors, from your job title to your location. Data analysts earn less than data scientists. West Coast analysts tend to earn more than those working on the East Coast or in the Midwest.
The average salary associated with an MS in Data Analytics is just over $77,000, according to PayScale. That figure includes early career data analysts earning $60,000, senior data analysts earning more than $80,000, and data scientists earning around $100,000.
What's abundantly clear is that having a master's degree can have a substantial positive impact on a data analyst's earning potential and that there are plenty of affordable on-campus and online analytics programs at the graduate degree level.
Based purely on the numbers, a master's in data analytics is worth it. If you want to be totally sure that you get a big salary bump after earning this degree, choose a data science-focused program that includes core classes in data engineering, machine learning, and other high-tech topics.
The frustrating answer is sometimes. The most significant difference between master's in data analytics programs and master's in data science programs tends to be the latter's focus on predictive modeling and the development of custom algorithms.
However, as discussed earlier, the distinction is growing hazier all the time. There are Master of Science in Analytics programs that are virtually indistinguishable from data science programs. Full-time and part-time students in the University of Chicago's Graham School analytics program, for instance, take courses like Data Engineering Platforms, Data Mining Principles, Data Science for Consulting, and Machine Learning and Predictive Analytics.
Whether you'll feel comfortable in a tech-focused program may depend on your background. Know that there are plenty of resources online that can get you up to speed in advanced programming techniques and machine learning basics.
Most master's in data analytics programs require students to complete 30 or so credit hours of coursework, and most full-time programs last two years. There are, however, some data analytics master's degree programs that can be completed in just 12 months by students who take winter and summer classes.
Program length is less significant than program quality when calculating whether a master's in data analytics is worth it. A bare-bones accelerated program will get you into the workforce faster. However, you may not graduate with the same depth of knowledge as someone in a longer, more involved part-time program that offers more hands-on learning opportunities. On the other hand, if you have a strong background in computer science, programming, or statistics and can take time off to immerse yourself in an intensive one-year data analytics master's program, work can wait.
Demand for data analysts and other data specialists is high—and growing, as humanity generates more and more data. "In the past, data was gathered by individuals," explains Stephen Beyer, department chair in the School of Business and Information Technology at Purdue University (Main Campus). "Today, data is gathered by individuals and machines. While individuals sleep, machines never do… the constant collection of data, in almost all areas of life, requires people who can manage and make sense of this massive volume of data." That data can be used to drive smarter decision making in marketing, manufacturing, medicine, and other fields, but only in the hands of those who understand what to do with it.
Calculating the ROI can be tough for some degrees, but there's not much ambiguity when it comes to this one. Earning a master's in data analytics is worth it. According to data collected by North Carolina State University at Raleigh, it takes most master's in data analytics graduates less than two years to recover the cost the school's 10-month program plus lost earnings.
If you graduate from a school like Carnegie Mellon University, Massachusetts Institute of Technology (MIT), or Tufts University, you'll pay more for your degree but chances are good you'll command a higher salary after graduation, making it relatively easy to pay off your education.
You'll also be in demand with this degree, and your salary will reflect that. Most experienced data analytics professionals earn somewhere between $82,750 and $138,000, which means earning a master's in data analytics can lead to a stable and well-paid career. That's doubly true if you're open to the idea of working in data science. Data analyst salaries tend to increase over the first ten years and then max out. At that point, you can transition into data science or data engineering with this degree and some additional training.
Finally, this degree is a good investment simply because you may need it to land interviews—even for entry-level data analytics jobs. Master's degrees are the new normal in data analytics. Twenty-five percent of hiring managers looking for data analysts "prefer or require candidates to have a graduate degree," according to data scientist Randy Bartlett, who adds that having an advanced credential is increasingly essential if you want to stand out in this field. Need more proof that a master's in data analytics is worth it? Burtch Works found that 89 percent of working data analysts with less than three years of experience have master's degrees. That's who you'll be competing against for jobs.
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