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 18-21.
Aside from course prerequisites for the courses listed below, there are no other prerequisites for the minor.
| Code | Title | Credit Hours |
|---|---|---|
| Computing | ||
| 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 | ||
| Computational Reasoning 1: Expression and Media Transformation | ||
| Programming for Linguistics | ||
| Computing 2: | 3 | |
| Computational Reasoning 2: Representations of Data | ||
| Quantitative Reasoning | 6-8 | |
| 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 | ||
| Electives | 6 | |
| Elective coursework in areas related to data science; one of these courses must be in Information Science | ||
| Information Exploration 1 | ||
| Information Exposition 1 | ||
| Data and the Humanities | ||
| Ethical and Policy Dimensions of Information and Technology | ||
| Information Visualization | ||
| Survey Research Design | ||
| Applied Machine Learning | ||
| Software Engineering for Data-Centered Systems | ||
| Science, Technology and Society | ||
| Strategic Communication Analytics and Metrics | ||
| Introduction to Literary Study with Data Science | ||
| Geographic Information Science: Space Time Analytics | ||
| Data Journalism | ||
| Machine Learning and Linguistics | ||
| Total Credit Hours | 18-21 | |
| 1 | There are two sections each of INFO 3401 and INFO 3402. Section 100 is for students who have completed the prerequisites INFO 2301 and INFO 2201. Section 200 includes prerequisites for the other pathways into the minor. |