Why Major in Data Sciences? 

Gain the hands-on skills to analyze complex data, uncover insights, and solve real-world problems. The Data Sciences degree program at Penn State Abington prepares you to work with cutting-edge tools in machine learning, statistics, and informatics while building the communication skills necessary to turn data into action.

You'll choose between two tracks aligned with your career goals:

Applied Option

Prepare for data-driven roles across industries by learning to solve real-world problems. 

Statistical Modeling Option

Build in-demand analytical expertise for careers in data science, analytics, and research by mastering experimental design, sampling techniques, hypothesis testing, and methods to improve the accuracy and reliability of insights.

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You might thrive in the Data Sciences major if you...

  • Think analytically and solve problems creatively
  • Enjoy working with data and technology
  • Are driven to turn insights into action

Data Sciences Careers

Job Growth

An estimated 37,700 openings for Data Sciences job are projected each year, on average*

Job Titles and Median Salary

  • Data Scientists: $112,590 

  • Operations Research Analysts: $91,290 

  • Mathematicians and Statisticians: $104,350

*Bureau of Labor Statistics, 2024  

Career and Professional Development: Setting You Up for Success

From your first day at Penn State Abington, you can access tools provided by the Career and Professional Development team to prepare for the future. 

  • On-campus networking opportunities
  • Internships
  • Supports such as resume reviews and mock interviews 

Coursework

Coursework equips you with the in-demand skills needed to collaborate on interdisciplinary teams and use data to drive business decisions, improve processes, and fuel innovation. 

Students develop practical, career-ready expertise through topics such as: 

  • Statistical reasoning and computational data analysis

  • Applied data science for real-world problem solving

  • Programming fundamentals and techniques

  • Matrix algebra for data modeling

  • Data management and organization

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