To earn a Data Studies minor, students must complete at least 25 credits:

• One Data Studies course
• One Data Skills course
• CS&SS 490
• Additional electives (from any of the course lists) to reach 25 credits

Additionally

• At least 15 credits must be taken at the 300/400 level (in other words, only 10 credits of your minor coursework can be 100/200 level classes)
• You are allowed to overlap 10 credits of minor coursework with major requirements
• A maximum of 4 classes from any list can count toward the minor (i.e. you are only allowed to use 4 data studies or data skills or cross cutting class to count toward the 25 credits. Should you take more than 4 classes in any one category, the additional classes will not count for the minor).

Please Note: DARS cannot show you major/minor overlap so it is your responsibility to ensure that you are in compliance with this policy. Read instructions on how to check your own overlap.

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Data Studies

Students will acquire foundational data literacies, understand the opportunities and limitations of data-intensive methods and epistemologies, and explore the broader implications of data science. Courses will lay the conceptual and theoretical foundations for understanding the role of data in knowledge production (either within specific domains or more generally), and promote critical reflection on the ethical, social, cultural, and political dimensions of data.

Data Skills 

Students will acquire basic knowledge of data science methods such as programming, data acquisition and management practices, software tools common to data science, statistics, machine learning, data visualization, and qualitative analysis. Students will be able to choose the methods they study and the depth of their study.

Cross-Cutting 

On-Ramp

These are courses that are designed to be taken at the beginning of the program, that convey the potential of this new field in various domains and have no prereqs.

Synthesis 

Designed to be taken towards the end of the program where students use theories, questions, and data that are relevant to the domain alongside data science knowledge and skills, often in the context of a project-based learning environment.

Data Science Minor

Leverage familiarity with data science in fields outside of data science, and gain skills and fluency to work with data in your major domain of study.

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