Skip to content Skip to main navigation Report an accessibility issue

Intercollegiate Programs

The University’s intercollegiate undergraduate academic programs provide students with the opportunity to earn a credential in a focused area of study that spans subject areas located in multiple colleges and that has been designed by collaborative teams of faculty members from these colleges.

Proposals to create or modify intercollegiate courses, certificates, and minors are developed by faculty teams involving faculty members from two or more colleges, and are submitted to the Undergraduate Council’s Curriculum Committee by the Vice Provost for Academic Affairs on behalf of these teams.  Faculty members who are interested in developing new intercollegiate courses, certificates, or minors should contact the Vice Provost for Academic Affairs.

Data Science Minor

Beginning in Fall 2021, undergraduate students majoring in any field can earn a minor in data science.  The data science minor consists of four core courses and one directed elective.

Core courses:

  • DATA 201: Data Knowledge and Discovery.  Introduction to the essential elements of data science through the examination of data sets drawn from a variety of fields. Explores data collection and management, exploration and visualization of data, modeling, computing, and ethical issues associated with data science. Introduces students to programming through hands-on activities. (3 credit hours; satisfies Quantitative Reasoning General Education requirement)
  • DATA 202: Data Management and Visualization.  Introduction to foundational concepts and techniques in the management and presentation of data for effective data-informed decision making. Explores data storage and indexing strategies, data warehousing, metadata management, visualization of time-series and geospatial data, and best practices for presenting data to inform decision making, such as heat maps and infographics. (3 credit hours; DATA 201 is a prerequisite)
  • DATA 301: Data Stewardship and Ethics.  Overview of the data life cycle, including creation, collection, assurance, description, discovery, integration, use, reuse, and preservation. Explores data management principles and the development and implementation of data life cycle management plans. Examines the legal, ethical, and technological challenges in developing and implementing data management policies. (3 credit hours; DATA 201 is a prerequisite)
  • DATA 302: Analytical Methods of Data Science.  Survey of modern algorithms and methods in data science, focusing on how, why, and when various methods work. Includes topics in statistics, machine learning, and optimization. (3 credit hours; DATA 202 is a prerequisite)

For more information about the Data Science minor, and a list of directed elective courses that can be used to satisfy minor requirements, please visit the 2021-22 Undergraduate Catalog.