Master's in Data Science: Your Guide

Earning your master's degree in data science is an opportunity to pivot to a career in data science or advance in your current data science career. Through a master's program, you can expect to deepen your understanding of data science methods, develop your analytical and critical thinking skills, and qualify for advanced roles in the field.

Data scientists and data science professionals continue to be in demand due to the growing reliance on data across all sectors. In fact, the US Bureau of Labor Statistics expects data scientist openings to grow by 35 percent between 2022 and 2032—a rate much faster than average [1].

While a master's degree isn't typically required to work in data science, furthering your data science education can be an excellent way to make a career change or specialize in an area such as artificial intelligence or big data. Discover what it takes to earn a master's in data science, the coursework you can expect, and the jobs you can pursue. 

Explore if data science is the right career choice for you while gaining in-demand, job-ready skills with the IBM Data Science Professional Certification on Coursera.

Read more: Your Guide to Data Science Careers (+ How to Get Started)

Data science master's degree

As with many other types of master's degrees, data science graduate programs take about two years of full-time study. However, options like online and accelerated programs often allow you to work at your own pace, either speeding up or stretching out your time to completion. These options can be especially helpful if you have other responsibilities to factor in.  

Data science core coursework

At the master's level, you'll likely be expected to complete core classes that strengthen your understanding of core data science concepts, like data processing, and your ability to work with data sets.

Common data science coursework can include:

• Machine learning

• Probability and statistics

• Data mining

• Big data

• Data visualization

• Object-oriented programming  

• Data manipulation and management

• Analytic techniques

Some programs require a capstone course or project in which you'll apply what you learn to real-world situations. 

Data science concentrations

In addition to taking foundational coursework in data science, your program may offer you a chance to specialize in a particular area of interest. These are typically considered electives or specialty courses that strengthen your knowledge of an area within data science.

Common concentrations include:

• Analytics and modeling

• Analytics management

• Applications

• Artificial intelligence

• Big data informatics

• Business analytics

• Computational intelligence

• Data engineering

• Technology entrepreneurship

Skills

Data science master's programs are designed to introduce new technical skills and strengthen existing ones. You should also find opportunities to bolster key workplace skills that many data scientists need, such as critical thinking, problem-solving, and communication.

You may be able to develop or work on the following skills in a master's data science program:

Learn more: 7 Skills Every Data Scientist Should Have

Admission requirements to a master's in data science

To qualify for a master's in data science program, you will typically need a bachelor's degree from an accredited institution. While a bachelor's in data science or a related field, such as computer science, cybersecurity, mathematics, or statistics, can be helpful, it’s not always required. In fact, a number of programs accept students from various backgrounds.

That being said, each program sets different stipulations. Some, for example, may expect you to have some understanding of mathematics and computer science before beginning. It's important to research the application requirements of each program you're interested in attending.

Depending on the program you apply to, you may also need a resume, letter of intent, letters of recommendation, and a minimum GPA. Other programs may require you to complete one or more master's level courses and receive a minimum grade before enrolling in the full program.

Master's in data science: salary, jobs, and outlook

A master's degree in data science can be a useful credential when you want to qualify for more advanced roles in the field. Data scientists earn an average annual salary of $114,282, according to Lightcast™ [2], but you may be able to earn more because of your credentials.

That's because, on average, master's degree holders tend to earn more and experience lower bouts of unemployment than bachelor's degree holders, according to BLS [3]. What's more, the degree tends to be a popular choice among data scientists. Thirty-four percent of data scientists hold a master's degree, according to Zippia [4].

Data science jobs

Data science as a profession is in more demand than ever [1,5,6]. You can pursue a range of jobs in the field of data science with a master's degree.

• Data scientist: Data scientists collect data from data analysts and engineers and analyze it further with advanced tools. In this role, you use principles of statistics and probability to find patterns in data and make predictions so businesses can make informed decisions.

• Data analyst: A data analyst reviews data to find trends and characteristics of a customer base and creates data sets that can be quickly processed and interpreted. As a data analyst, you look for patterns that can be used as solutions for companies and organizations. 

• Data architect: A data architect is in charge of designing the policies, models, technologies, and systems to work with the processed information. 

• Data engineer: A data engineer prepares data for analytical and operational uses. You build data pipelines to bring data sets that analysts and scientists later process. 

• Data science and analytics manager: A data analytics manager joins several tasks from their team into a cohesive effort for a more extensive data project. In this profession, you research and construct methods for data collection, information analysis, and problem-solving. 

• Business intelligence analyst: A business intelligence analyst reviews data and produces financial and market intelligence reports. These reports are for pattern recognition and finding trends in a market used for a company’s financial decisions. 

• Machine learning engineer: Machine learning engineers create data filters and solutions for software. You need high-level programming and mathematical analysis skills and the ability to design, build, and maintain machine learning systems and software. 

• Statistician: Statisticians work to collect, analyze, and interpret data to find trends and recognize patterns to be used by higher-ups for decision-making and prioritization.

Learn more: Data Science Jobs Guide: Resources for a Career in Tech

Is a master's in data science worth it?

You can benefit from earning a master's in data science, even if it's not a required degree to work in the field. You may find that you qualify for more advanced roles or higher salaries thanks to your graduate-level education.

Enrolling in a master's program can also be a great opportunity to develop more specialized knowledge about a subject, setting you apart from other applicants or peers who do not have the same advanced credentials. Learn more about whether a graduate degree in the subject is right for you with Is a Master's in Data Science Worth It?

Getting started

With a growing need for data expertise across industries, a master's in data science can be a valuable investment for career changers or those seeking to advance in the field.

If you're ready to earn your master's degree, learn from top-ranked schools such as the University of Michigan's Master of Applied Data Science and the University of Colorado Boulder's Master of Science in Data Science. You can learn at your own pace from anywhere there's an internet connection. Get started by previewing a degree course today.