How Much Will You Make With a Master's in Data Analytics?
March 11, 2021
Earning a master's in data analytics can lead to a six-figure salary in data science. Learn what goes into nabbing a great job, including the necessary data skills and the importance of attending a top program.
The Master of Science (MS) in Data Analytics is one of the hottest graduate degrees, with more students considering it than ever before. Why? Its excellent return on investment may be the primary driver. A master's degree in data analytics improves your skill set and broadens your career options considerably.
It also usually translates to a substantial pay raise. Not all graduate degrees result in higher income, but computer science (and its subfields, including analytics) typically does: data analytics professionals with a master's degree earn an average of $15,000 more per year than those without just a bachelor's degree. For those with job titles like data architect, data engineer, director of analytics, or data scientist, earnings can go way higher.
Here are some factors that can increase your master's in data analytics salary. This article discusses:
- What do you learn in a master's in data analytics?
- What degrees qualify as a master's in data analytics?
- What are the admissions requirements for a master's in data analytics?
- How much can you earn with a master's in data analytics?
- Does where you get your master's degree impact your earning potential?
What do you learn in a master's in data analytics program?
A master's in data analytics focuses on (of course) data analysis and the management of data sets. Many programs help students cultivate skills in cloud computing, programming, and data visualization, which is the process of designing easy-to-comprehend graphs and charts for those who are less technically inclined. You'll also likely have the opportunity to take courses in machine learning, creating programs that refine and upgrade themselves.
As a master's student in data analytics, you'll likely take course with titles like:
- Big Data Platforms
- Data Engineering Platforms for Analytics
- Data Mining Principles
- Data Science for Consulting
- Leadership Skills: Teams, Strategies, and Communications
- Linear and Nonlinear Models for Business Applications
- Machine Learning and Predictive Analytics
- Programming in Java, Python, and Hadoop
- SQL for Data Science
- Statistical Analysis
- Time Series Analysis and Forecasting
All this learning is packed into a course structure that takes around two years for full-time students to complete—though some can be shorter. Some programs, especially those offered online, allow for greater flexibility, allowing students to go at their own pace and complete courses over a more extended period, often three or more years.
What degrees qualify as a master's in data analytics?
Schools offer data analytics programs under different designations. While your program might simply be called Master of Science in Data Analytics, it might also be called:
- Master of Computer Science with a concentration in Data Analytics
- Master of Science in Computer Science with a concentration in Data Analytics
- Master of Science in Data 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 Statistics with a concentration in Data Analytics
Data analytics is a subfield of data science, so it is possible to earn a master's in data science with a specialization in analytics. You can do just that at Northwestern University offers the opportunity to for example.
You can also earn a Master of Business Administration in Data Analytics, which focuses more on management and business applications and less on programming and data architecture. A Master of Science in Business Analytics is another option; it teaches data analytics as they apply to business problems and business decisions.
What are the admissions requirements for a master's in data analytics?
Admissions requirements can be broken down into two categories: (mostly) universal graduate school requirements, and specific requirements for data analytics programs.
General graduate school requirements
Most grad schools require students to submit:
- transcripts for all previously attended institutions of higher education
- Graduate Record Examinations (GRE) scores, their undergraduate GPA (usually 3.0 or above)
- Letters of recommendation
- A professional resume
- Between one and three personal essays or statements of purpose,
- Test of English as a Foreign Language (TOEFL) scores—for international students
Many also look for at least a few years of work experience.
The better the school, the more strict these requirements become, generally speaking. Top schools want to see students who scored at least a 700 on the GRE, though there are exceptions. Some highly regarded institutions are test-optional or don't care about the GRE at all. These schools typically put a premium on work experience.
Data analytics program specific requirements
Having work experience (usually at least two years) in analytics is one of the best ways to make your application stand out. You might already be working in analytics or a similar field like:
- Data engineering
- Data architecture
- Research analytics
At the very least, you should have established skills in data mining, data modeling, and business analytics. Remember, graduate programs usually advance a student's abilities rather than teach them entirely new ones, the way bachelor's programs do.
Most programs don't require a specific bachelor's degree, but to land an entry-level job that allows you to accrue work experience usually means having postsecondary training in one of these subjects:
- Computer science
- Information science
You might still get into a master's in analytics program without a background in one of those areas, though many schools will make you catch up by taking one or more foundation courses.
How much can you earn with a master's in data analytics?
Having master's in data analytics can lead to numerous careers, so it's tough to pin down exactly what you'll earn after graduation. However, some of the top jobs include:
- Director of analytics
- Data architect
- Data engineer
- Data scientist
- Senior data analyst
Graduate training can augment your earning potential significantly. The average data analyst salary is just $60,000, while the average salary for a senior analyst, who is a management professional and lead other employees, is $80,000, according to PayScale.
The main thing to note is that a master's in data analytics is a great way to improve your salary and career prospects, but it's just one factor. Even two people with the same job title can have vastly different salaries. For example, computer network architects, which includes data architects, earn a median annual income of around $112,000, according to the Bureau of Labor Statistics. However, their yearly income ranges from about $65,000 to just under $170,000. According to Glassdoor, the salary range for data engineers is between $72,000 and $158,000, with an average base pay of around $103,000.
So, what factors go into getting a higher salary? Experience plays a role in how much you earn. According to PayScale, the average salary for a data scientist is just under $100,000 per year. However, a late-career data scientist (think nearly 20 years) makes more than $135,000.
The industry you choose can also have an impact on your salary. For instance, healthcare data analysts have a higher earning potential than market research analysts; the former tops out around $85,000, while the latter can make up to $65,000.
Where you work can be the ultimate difference-maker. Seven of the highest-paying companies for data scientists offer total compensation of over $200,000 per year—even for professionals without a ton of work experience. However, getting a job at one of the biggest names in tech is extremely competitive.
Location and job market can impact your salary, too. A director of analytics in New York City earns over $180,000, which is roughly $30,000 more than the national average for the position.
Does where you get your master's degree impact your earning potential?
This is a tricky question because taking on debt to complete a graduate program is nobody's first choice, making cheap programs attractive. You also want to put yourself in the best position possible to succeed after graduation; that could mean spending a little more on a top-tier program with an excellent track record of placing graduates in top jobs. The last thing you want to do is spend a lot of money on a middle-of-the-road program that saddles you with debt without the payoff of a top school.
Some top computer science schools that offer data analytics programs include:
- Carnegie Mellon University
- Columbia University
- Duke University
- Georgia Institute of Technology - Main Campus
- Pennsylvania State University - World Campus
Out-of-state students who attend Georgia Tech can pay more than $1,200 per credit hour (plus extra fees) for their education, which really adds up during the 36 credit hour program. However, the average 2018 graduate earned nearly $95,000 per year, and that number can increase to over $100,000 after a few more years of experience.
When considering what graduate program to attend, you'll want to look at how much it costs and your expected salary after graduation. You might be willing to spend more if you think it will help you earn more.
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