Is a Master’s Degree in Data Science Worth It?

Gaining a master’s degree in data science opens up a wide range of career opportunities. Including increased pay, a quicker route to leadership roles, and a greater understanding of the field’s theoretical and practical applications.

These days, there are a lot of misunderstandings over what data analytics is and is not and how it differs from data science. For example, data analysis is described as “combing through data to identify nuggets of awesomeness that can be used to help to attain an organization’s goals”. However, according to business technology experts Rick Delgado, it tends to be significantly more business and strategy focused on the website inside BIGDATA.

However, according to Adam Hunt, the chief data scientist at RiskIQ, humans utilize data science, not analytics, to arrive at decisions “drawing inferences that advance your data. If you’re not using data to solve a problem, then the analysis is all you’re doing. Going from analysis to science is using the results to explain anything. Data science is more about solving problems than looking at, analyzing, and plotting data.”

How do you compare these benefits with the realities of your investment, though?

Keep reading to learn about these advantages and whether earning a master’s degree in data science is worthwhile.


Get Deeper Knowledge: Fundamentals to the cutting edge

Data science is an integrative field that employs systems, processes, algorithms, and scientific methods to extract knowledge from organized and unstructured data and inform decision-making.

To become a data scientist, a solid foundation in statistics, mathematics, and computer languages.

Students can deepen their understanding of the fundamentals and build their skill sets in data management, data cleaning, data analysis, and data visualizations through graduate-level data science programs. Additionally, students pursuing higher degrees have the option to take elective data science courses that delve deeper into certain areas of the discipline, such as:

  • Advanced data analysis
  • Applied statistics
  • Data management
  • Data visualization
  • Modeling techniques
  • Programming
  • Reporting
  • Statistical analysis
  • Systems architecture
  • Working with large data sets

Additionally, several other courses cover more technical subjects like:

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

Students must also finish a capstone course, thesis, research project, or internship as part of the majority of graduate programs in data analytics. These opportunities for experiential learning give students the chance to test their developing skill set against real-world problems to prepare for their future professions in analytics.

You should be aware that almost all of them will instruct you in one way or another on how to:

  • Employ tools such as Tableau, Python, Hive, Impala, PySpark, Excel, SQL, and Hadoop
  • Manage unstructured data and find sense in raw data
  • Identify insightful trends in the data.

If a program is “worth it” to you, it will depend more on your background, hobbies, and professional goals than on your degree. For example, 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 wish to work in technology or pursue a career in data science. On the other hand, a business-focused curriculum might be a better fit if you’re more interested in occupations that employ analytics or believe you might want to work in BI consulting.


Career Opportunities and Salaries in Data Science

Making educated decisions across sectors is made possible by data science and data scientists. The Bureau of Labor Statistics (BLS) projects demands for computer and information technology professionals—including data scientists—will increase by 13% over the next eight years.

Data scientists have even more employment and salary prospects after earning advanced degrees.

With a master’s degree in data analytics, you could work in any of the following fields:

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

PayScale says the average pay for an MS in Data Analytics is just over $77,000. This sum covers the salaries of data analysts in their early careers ($60,000), senior data analysts ($80,001 or more), and data scientists ($100,000 or more).

Your background may affect how comfortable you are in a tech-focused program. Know that many online tools are available to help you understand the fundamentals of machine learning and advanced programming.


How to Be Flexible and Earn a Master’s Degree While Working?

Students can choose between part-time and full-time study options in many online master’s degree programs, giving them flexibility. In addition, students can choose to pace their degree over several semesters by taking one course at a time or finish their program in as short as one year by taking a full course load.

The ability to work around schedules is essential for keeping a job while obtaining a degree and giving students the data science skills they need to apply what they learn in the classroom to their existing positions.

In the end, enrolling in an online master’s program in data science is about more than simply receiving a degree; it’s also about balancing demanding coursework with the chance to put what you learn into practice right away and, as a result, advance your career.


How to Earn a Master’s in Data Science with Today’s Programs?

Programs for earning a master’s degree online are becoming more popular, making them more accessible and cheap than ever. Reputable institutions provide online programs that let students benefit from the adaptability of online learning while still receiving the rigorous instruction of a top-ranked program.

These courses are made to equip students with all the knowledge and abilities needed to advance in the field of data science. Typically, teaching is given to students to hone their essential abilities and provide them with the opportunity to specialize through elective courses or capstone projects.

To enroll in an online program, students must have good technical abilities in computer science, mathematics, statistics, and programming, much like in conventional degree programs.


How much return on investment can I anticipate if I get this degree?

It can be challenging to determine the return on investment, but there is little doubt in this case. A master’s degree in data analytics is worthwhile. According to data gathered by North Carolina State University at Raleigh, the average master’s in data analytics graduate recovers the cost of the 10-month program plus lost wages in less than two years


You’ll also be in demand with this degree, and your pay will reflect that. A master’s degree in data analytics can pave the way for a secure and lucrative job because the majority of experienced data analytics professionals make between $82,750 and $138,000 annually. That holds even more so if you’re considering a career in data science. In addition, salary increases for data analysts often peak after the first ten years. You can shift into data science or engineering with this degree and more training.

You might require this degree to get interviews, even for entry-level data analytics positions, making it a wise investment overall. In data analytics, master’s degrees have become the new standard. You’ll have to compete with people like them for work.


Author bio:

Emma Grace, a professional writer at, is an expert in data sciences. However, she is so talented that you can ask her to “do my contract law assignment”. Also, she provides MBA dissertation help for commerce.


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