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B.S. in Data Science

Program Overview

The B.S. in Data Science will require a minimum of 40 credits. The curriculum includes three tracks: Data Application, Algorithms, and Spatial Data Analytics. The degree program culminates in a capstone experience. Each track will further strengthen and deepen students’ understanding in data science.

The focus of the core curriculum is to provide students with a solid foundation in data science. Students learn data science theory and applications, including critical evaluation of how data can be used to solve novel problems, deliberation (considering the ethical, moral, and societal implications of data science), and communication. Through the core curriculum students learn the basics of programing, modeling, machine learning, data visualization, database structures, and ethics in data science. Students also will take one course in linear algebra and two courses in mathematical statistics. The curriculum provides opportunities for students to use their skills and knowledge to manage and analyze large data sets efficiently and effectively and to identify and answer novel questions in a variety of settings.

Students will choose a track area to gain knowledge, skills, and abilities that are more specific to particular career aspirations. They are required to take three courses from one of the following tracks: Data Application, Algorithms, or Spatial Data Analytics. Coursework for the Data Application track focuses on teaching additional skills (e.g., data with time dependencies) and providing a more in-depth understanding of analytical and data visualization tools commonly used by data scientists employed by the private industry or government. Coursework for the Algorithms track focuses on expanding students’ abilities to develop new software or algorithms for the ingestion or analysis of large sources of frequently near-real-time data (this track is not available for students already majoring in Computer Science). Coursework for the Spatial Data Analytics track focuses on integration of analytical and visualization tools that data scientists typically use when working with data that have spatial dependencies.

In the capstone experience, each student will work closely with a program faculty member to conduct a substantial research project that focuses on synthesis and critical analysis, problem solving in an applied and/or academic setting, creation of original material or original scholarship, and effective communication with diverse audiences.

Check the Data Science Course Catalog for course listings

For more information, contact a Data Science advisor.

First Semester Course Guide by Major