Data science is a multidisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.

This minor provides students with an introduction to the core concepts and skills of data science in computing, statistics and information science to complement existing majors in CMCI fields, in the social sciences and in the arts and humanities.

The program is specifically designed as an add-on to existing quantitative methods courses and sequences in the social sciences. Students in such degree programs who wish to add data science experience and credentials to their course of study can complete the minor without additional course prerequisites. For this reason, some aspects of the curriculum (particularly the quantitative methods area) are quite flexible, allowing students to acquire this background through subject-specific study in a variety of disciplines.

Required Courses and Credits

The minor is divided into three areas: computing, quantitative methods, and electives. Computing courses cover basic programming and data structures with an emphasis on the Python programming language. Because of the variation in credit hours associated with quantitative methods courses, the total hours for the minor vary between 19–22. Students may apply no more than six credit hours of transfer work, including three hours of upper-division credit.

Information Science majors may not receive an Information Science minor nor a Data Science minor. Students may not receive both the Information Science minor and the Data Science minor.

All coursework applied to the minor must be completed with a grade of C- or better (no pass/fail work may be applied). The GPA for all coursework attempted in the minor department must be equal to 2.00 (C) or higher.

Aside from course prerequisites for the courses listed below, there are no other prerequisites for the minor.

Computing 1:3-4
Choose one from the following list:
Computational Foundations 1
Introduction to Computational Thinking
Computer Science 1: Starting Computing
Geographic Information Science: Spatial Programming
Programming for Information Science 1
Programming for Linguistics
Computing 2:4
Programming for Information Science 2
Quantitative Reasoning6-8
Choose a two-course sequence from the following options:
Statistics for Information Science
and Quantitative Reasoning for Information Science
Quantitative Methods in Anthropology
and Quantitative Reasoning for Information Science
Statistics and Geographic Data
and Advanced Quantitative Methods for Spatial Data
Quantitative Research Methods
and Applied Political Science Research
Psychological Science I: Statistics
and Psychological Science 2: Research Methods in Psychology
Introduction to Social Statistics
and Sociological Research Methods
Inclusive Interdisciplinary Data Science for All
and Quantitative Reasoning for Information Science
Elective coursework in areas related to data science; one of these courses must be in Information Science
Information Exploration
Information Exposition
Data and the Humanities
Music as Information
Ethical and Policy Dimensions of Information and Technology
Information Visualization
Survey Research Design
Applied Machine Learning
Software Engineering for Data-Centered Systems
Information and Data Retrieval Systems
Defamiliarizing Data: The Ethnography and Design of Making Data Strange
Introduction to Literary Study with Data Science
Science, Technology and Society
Strategic Communication Analytics and Metrics
Introduction to Literary Study with Data Science
Literary Study with Data Science
Geographic Information Science: Space Time Analytics
Data Journalism
Machine Learning and Linguistics
Total Credit Hours19-22