Master's in Data Science: Your Guide

Key takeaways

A master’s in data science includes coursework in machine learning, big data, and data mining, and prepares you for various job opportunities.

• As a data science master’s student, you may concentrate your studies in such areas as analytics and modeling, artificial intelligence, business analytics, or data engineering.

• To apply to a master’s in data science program, you’ll need to earn a bachelor’s degree in data science, computer science, or a related field and submit your resume, letter of intent, and letters of recommendation.

• You can pursue various job opportunities, such as data analyst, data architect, data engineer, or business intelligence analyst, with this master’s degree.

Discover what it takes to earn a master's in data science, the coursework you can expect, and the jobs you can pursue. 

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.

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 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. 

Is data science heavy in math?

Data science requires a strong foundation in math, particularly statistical concepts, but it’s not a math-heavy field. With your math foundation, you can rely on a variety of tools, including R and Python programming, Excel, and Tableau, to help you explore data and find trends.

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 in data science program:

Learn more: 7 Skills Every Data Scientist Should Have

How to get a master’s in data science: Admission requirements

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, a 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 a median total salary of $156,000, according to Glassdoor [1], but you may be able to earn more because of your credentials. This figure includes base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

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 the US Bureau of Labor Statistics (BLS) [2]. 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 [3].

Data science jobs

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 34 percent between 2024 and 2034, a rate much faster than average [4].

What can you do with a master's in data science?

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 that 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.

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?

Prepare for a data science career with free resources

Visit our Career Resource Hub, where you can assess your skills and explore career paths. You can also access these free resources to learn more about a career in data science:

• Consider your path: Data Science Learning Roadmap: Beginner to Expert 

• Learn your terms: Data Science Terminology and Definitions 

• Read an insider story: Meet the 18-Year-Old Getting His Master’s Degree in Data Science 

Explore flexible master’s degree programs available from Coursera’s esteemed college and university partners.