At a Glance

  • 100% online coursework
  • STEM-designated by the US DHS
  • Project-based analytics capstone
  • Expert faculty in data science
  • No GMAT/GRE required

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To learn more about online Master of Science in Data Science, fill out the fields in this form to download a free brochure. If you have any questions at any time, please contact an admission advisor at (866) 843-7205.

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Flexible options to build advanced quantitative skills

The groundbreaking researchers in Pace’s Seidenberg School of Computer Science and Information Systems designed our 100% online MS in Data Science to equip professionals for the complex technical problems of an unpredictable world. In this program, you’ll prepare to start or advance your career in fueling innovation with advanced quantitative methods and best practices for data governance. With the rigorous knowledge and hands-on experience that can only come from completing a STEM-designated graduate degree at a pioneering school of computer science, you’ll be ready to fulfill the growing demand for data scientists who harness analysis, visualization, and machine learning to achieve organizational goals.

Our flexible program makes data science skills accessible with the option to take courses either full or part-time on a schedule that works for you. Part-time students can finish their master’s degree in two years while working, and full-time students can complete the program in a little over one year. Along the way, you’ll work closely with Seidenberg School faculty, learning to develop vital insights through statistical programming, data management, and automation.

Learn From Top Thinkers in the Field

In the MS in Data Science program, you’ll take courses from researchers who contribute to pioneering work in fields such as artificial intelligence, software development, cybersecurity, machine learning, robotics, and computer vision. Through recorded lectures and live interactions, our faculty members bring groundbreaking insights into the virtual classroom and ensure that students are ready to take on emerging challenges in the field. Located in New York City, the world’s greatest hub for international business, Pace also offers the chance to connect and collaborate with industry experts who have evolved the field of data science.

For more details, see our faculty page.

Mudassir Ali

“In my current role at Northwell Health, because I learned about model building at Pace University, I was able to pitch business problems that revolved around predicting modeling that I am currently working on. I was able to provide sound arguments to why we need this model, how it will affect the business approach, and how the company can benefit from it. Thus, being able to work on these models has brought a whole new level of analytics to my department at Northwell Health.”

Mudassir Ali (MS in Data Science 2022)

Data Scientist, Northwell Health

A Curriculum Designed for Quantitative Problem Solvers

The Seidenberg School has decades of experience offering fully online programs, dating back to 1999. Our data science curriculum channels that expertise to delve into both quantitative theory and practical applications. In seven required courses and three electives, you’ll explore best practices for managing, structuring, and finding valuable insights in data. In addition, you’ll gain hands-on experience with data mining techniques and writing algorithms.

Focus on the topics that suit your career path by choosing electives in computer science as well as information systems, business analytics, and natural sciences. In the project-based Analytics Capstone, you can solve a real-world problem that interests you by conducting quantitative research and applying the data science methods you’ve learned in the program.

Our data science master’s program is STEM-designated, indicating a curriculum focused on science, technology, engineering, and math that can lead to rewarding career opportunities.

Visit our curriculum page to learn more.

Data Science Careers

As organizations strive to gain insights into their customers and supply chains or harness the latest advances in machine learning, they need highly skilled experts to collect, manage, and find meaning in their data. The U.S. Bureau of Labor Statistics projected a 19% increase in data science jobs between 2020 and 2030, much faster than the average of 4% for all occupations. In fact, a McKinsey Global Institute report projected there could be a shortage of 250,000 data scientists in the US alone by 2024.

Job posting data shows that the average salary offered for data scientists with a master’s degree is $117,100, and salary premiums are associated with skills in data engineering, predictive modeling, and analytics software. The top employers for professionals in this field include technical service providers, finance and insurance companies, healthcare organizations, manufacturers, and the information sector.

Online master’s in data science graduates develop the background in quantitative analysis and interpretation to succeed in any of these roles:

  • Data Scientist
  • Data Analyst
  • Database Architect
  • Quantitative Analyst
  • Data Engineer
  • Machine Learning Engineer
  • Business Analyst
  • Software Engineer
  • Network Architect

For more information about career options, read our detailed article about what you can do with a master’s in data science.

Admission Requirements

Applications are carefully reviewed by the Graduate Admission Committee utilizing a holistic approach. To apply, you should have:

  • Bachelor’s degree in any field, with proficiency in calculus, linear algebra, some knowledge of probability and statistics, and some experience in programming and databases*
  • Minimum GPA of 3.0 preferred

*Students without the required background in programming and databases will be required to satisfy the prerequisite level of knowledge by completing online bridge courses in Database Management Systems and Python Programming once enrolled.

For more details on admission requirements and the application process, visit our admission page.