Master's in Data Science: Your Guide

Written by Coursera • Updated on

With a master’s degree, you can work on projects that require expertise in statistics, machine learning, artificial intelligence, and more. Learn more about advancing your education in this field.

[Featured image] A student in a beige jacket sits in front of two monitors working on a master's in data science project.

Earning your master's degree in data science is an opportunity to deepen your understanding of data science methods, develop your analytical skills, and qualify for advanced roles in the field of 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.  

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

Read more: What is Data Science?

Data science master's programs

Master's degree programs in data science take about two years of full-time study. However, some accelerated programs are designed to take less time and there may also be part-time options that take longer but accommodate your work schedule or other responsibilities. 

Although each program differs, you may be expected to have a strong understanding of mathematics and computer science before beginning.

Data science coursework

At the master's level, you'll likely be expected to complete core classes in machine learning, modeling, statistics, and databases, as well as elective courses related to data science. You may also take courses in programming languages, such as Python, R, or SQL. Some programs require a capstone course in which you'll apply what you learn to real-world problems. 

In many data science programs, it’s common to take courses in:

  • Machine learning

  • Probability and statistics

  • Data mining

  • Big data

  • Object-oriented programming  

  • Data manipulation and management

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specialization

Data Science Fundamentals with Python and SQL

Build the Foundation for your Data Science career. Develop hands-on experience with Jupyter, Python, SQL. Perform Statistical Analysis on real data sets.

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Average time: 7 month(s)

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Skills you'll build:

Data Science, Github, Python Programming, Jupyter notebooks, Rstudio, Data Analysis, Pandas, Numpy, Probability And Statistics, Regression Analysis, Data Visualization (DataViz), Statistical Hypothesis Testing, Basic Descriptive Statistics, Cloud Databases, Relational Database Management System (RDBMS), SQL

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. Common concentrations include:

  • Analytics and modeling

  • Analytics management

  • Applications

  • Artificial intelligence

  • Big data informatics

  • Business analytics

  • Computational intelligence

  • Data engineering

  • Technology entrepreneurship

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Data Science as a Field

This course provides a general introduction to the field of Data Science. It has been designed for aspiring data scientists, content experts who work with ...

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INTERMEDIATE level

Average time: 1 month(s)

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Skills you'll build:

Data Science, Applied Mathematics, Information Science, Statistics, Computer Science

Admission requirements

To qualify for enrollment in a master's in data science program, you will typically need a bachelor's degree from an accredited college or university. While a bachelor's in data science or a related field such as computer science, cybersecurity, accounting, mathematics, or statistics can help you prepare for master's coursework, it’s not always required. However, you may be required to prove foundational knowledge in linear algebra, statistics, and computer programming. 

Depending on the program you apply to, you might also need a CV, personal statement or essay, letters of recommendation, or 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.

Where can I study for a master's in data science?

Many state and private universities offer master's degrees in data science, and you may have the option of taking your classes online or in person. When exploring schools, you may find master's in data science programs called by different names. Examples include:

  • Master of Liberal Arts, Data Science

  • Master of Science in Data Science

  • Master in Interdisciplinary Data Science

  • Master of Science in Applied Data Science

  • Master of Advanced Studies – Data Science and Engineering

  • Master of Science in Data Analytics

  • Master of Arts in Mathematics with an emphasis in Data Science

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Is a master's in data science worth it?

Not all data science jobs require a master's in data science, but by earning the degree, you can gain a deeper understanding of concepts you’ll need in the field. Thirty-nine percent of data scientist and advanced data analyst positions call for a master’s degree or higher [1]. Graduate programs will often complement your undergrad knowledge with real-life scenarios.

Many master's programs include a capstone project or an internship, which gives you valuable work-related experience. The knowledge and skills you can gain may help you stand out to employers and be ahead of the curve once you enter the workforce. 

Learn more: Is a Master's Degree Worth It?

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

A master's degree in data science is a common requirement for a range of jobs in the field of data science. Some positions may require more math, programming, or organization skills than others. Some roles you might pursue include:

  • Data scientist: Data scientists collect data from data analysts and engineers to further analyze it with advanced tools. They 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 create data sets that can be quickly processed and interpreted. They 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. These professionals 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. They 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. They 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

Get started

If you're ready to enroll in a master's in data science program, explore the convenience of earning your degree online with the Master of Applied Data Science from the University of Michigan or the Master of Science in Data Science from the University of Colorado Boulder. Learn at your own pace from anywhere with an internet connection. Experience for yourself what it's like by previewing a degree course:

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Data Science Foundations: Data Structures and Algorithms

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Algorithm Design, Python Programming, Data Structure Design, Analysis of Algorithms, Hashtables, Graphs Algorithms, Intractability

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Article sources

1. Burning Glass. “The Quant Crunch, https://www.burning-glass.com/wp-content/uploads/The_Quant_Crunch.pdf." Accessed August 9, 2022.

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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