Data Sciences (DATSC) | College of Information Sciences and Technology
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Data Sciences (DATSC)

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In today’s information society, professionals who can make sense of big data are in high demand. The new Data Sciences degree program at Penn State is part of an intercollege initiative between the College of Information Sciences and Technology (IST), College of Engineering, and Eberly College of Science to meet that need.

The program will educate students on the technical fundamentals of data sciences, with a focus on developing the knowledge and skills needed to manage and analyze large-scale, unstructured data to address an expanding range of problems in industry, government, and academia. As a result, data sciences graduates will possess the core skills and problem-solving approaches to compete for leading-edge analytics positions across many different industry sectors.

Students will gain breadth of knowledge through common core classes, as well as depth in one of three options. After taking common courses during the pre-major stage, students will choose among options focused on application, computation, and science. Students in all three options will come together in their junior and senior years for two shared capstone experiences.

Options in the Data Sciences degree program include:

Applied Data Sciences

College of Information Sciences and Technology
This option focuses on the principles, methods, and tools for management, integration, analysis, visualization, and predictive modeling of massive, complex data. Read more...

Computational Data Science

College of Engineering
This option focuses on the computational foundations of the data sciences, including the design, implementation and analysis of software that manages the volume, heterogeneity and dynamic characteristics of large data sets and that leverages the computational power of multicore hardware. Read more...

Statistical Modeling Data Sciences

Eberly College of Science
This option focuses on the statistical models and methods that are needed to discover and validate patterns in datasets of all sizes. Read more...


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