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Pathways to Data Science

Data Science Major

Our major in Data Science prepares students for a variety of data-related careers by providing in-depth and hands-on course work in programming, applied statistics, data management, data visualization, ethics, and machine learning.

Data Science Minor

Our minor in Data Science is a six course sequence that focuses on developing students expertise with core Data Science tools and techniques. The minor is designed to be a great supplement to any major that involves the use of data for exploration or decision making. 

Data Analytics Certificate

Our certificate in Data Analytics is a three course sequence that focuses on introductory programming, statistics, and data visualization. It is ideal for students interested in developing their data analytic skills and will expose students to a variety of commonly used data analytic tools and techniques. 


The Student Experience

Project-Based Application-Focused Learning

Our Data Science courses emphasize learning with real-world data and student projects that can make an impact beyond the classroom. We work with community partners, including non-profits, local government, and businesses or all sizes to develop Data Science projects that allow students to learn by doing. Students work on every part of developing projects for our community partners, from data cleaning, to analysis, to the final presentations. 

Small Classes with Experienced Faculty

Our students don't get lost in the crowd. Our typical Data Science class has fewer than 20 students. Small class sizes allows us to move beyond just lectures and offer many opportunities for one-on-one interaction with faculty. Our faculty have deep expertise in teaching the foundations of Data Science including Computer Science, Mathematics, and applied Statistics.

Our faculty have earned Ph.D.s from leading institutions including University of Michigan, Purdue University, the University of Wisconsin, Madison, and Auburn University. Faculty research interests include: a). algorithmic number theory and its applications to cryptography, b). infeasible constraint systems, c). high-throughput systems for collection and analysis of behavioral data, d). decompositions, embeddings, and labelings of graphs, digraphs, and hypergraphs.

Proven Career Outcomes

Our recent graduates have found employment in the private sector, with companies such as Price Waterhourse Cooper, Deloitte, and State Farm, and in state and local government, including the Federal Bureau of Investigation. Our graduates have also gone on to top graduate programs in a variety of data-related fields including programs in Accounting, Data Science, and Business. 


Brad Sheese - Professor of Computer Science

Department - Computer Science