Data Science (Minor)
Housed in the Technology and Digital Studies Program, the Minor in Data Science is a cross-college venture between the College of Arts and Letters and the College of Engineering involving departments from across the university. The Data Science Minor offers courses that focus on the acquisition, management, analysis, and use of complex data, including machine learning and generative artificial intelligence, as well as communication about data with an understanding of the broader implications for society.
Upon completion of the minor, students should be able to:
- Collect data in its different forms, including by scraping web sources and querying databases and APIs.
- Parse and transform data into structures designed for analysis.
- Visualize data to improve understanding.
- Statistically analyze data to summarize, draw inferences, and make predictions.
- Apply machine learning to classify, cluster, predict, and discover patterns in large data sets.
- Use generative AI tools, including large language models, to support data analysis, coding, and communication.
- Critically assess the capabilities, limitations, and risks of machine learning and AI systems, including issues of bias and reliability.
- Collect, analyze, and present data in an ethically responsible manner and understand privacy issues.
Data Science is a fifteen (15) credit interdisciplinary minor, offering classes from departments across the university, including Sociology, Computer Science Engineering, Psychology, Economics, English, Philosophy and Design.
Prerequisite: One class in Statistics
| Code | Title | Hours |
|---|---|---|
| The minor accepts the following classes: | ||
| SOC 30903 | Statistics for Sociological Research | 3 |
| ECON 30330 | Statistics for Economics | 3 |
| MATH 30540 | Mathematical Statistics | 3 |
| PSY 30100 | Statistics for Behavioral Sciences | 4 |
| ACMS 20340 | Statistics for Life Sciences | 3.5 |
| ACMS 30440 | Probability and Statistics | 3 |
| ACMS 30600 | Statistical Methods & Data Analysis I | 3.5 |
| ITAO 20200 | Statistical Inference in Business | 3 |
Students may petition to have other statistics courses accepted to fulfill the requirement, by contacting the Director of Undergraduate Studies for the minor, David Smiley (dsmiley@nd.edu).
Minor Requirements
| Code | Title | Hours |
|---|---|---|
| Required Courses | ||
| CSE 10101/CDT 30010 | Elements of Computing I | 3 |
| MDSC/SOC 20009 | Introduction to Data Science | 3 |
| Electives | ||
| To complete the minor, students must take 9 credits (3 courses) from a provided list of electives, available in a wide variety of areas, from philosophy to physics, and English to epidemiology. Students may choose a set of electives that enables them to specialize | 9 | |
| Total Hours | 15 | |
Analytics Track
The Data Science Minor–Analytics Track is designed for undergraduate students with a particular interest in the analytic/modeling phase of the data science workflow, and who have completed prerequisites of Calculus III (MATH 20550) and ACMS 30600 (or equivalent, as detailed below).
| Code | Title | Hours |
|---|---|---|
| Prerequisites | ||
| MATH 20550 | Calculus III ((or equivalent)) | 3.5 |
| ACMS 30600 | Statistical Methods & Data Analysis I ((or equivalent)) | 3.5 |
| Required Courses (6 Credits) | ||
| CSE 10101 | Elements of Computing I | 3 |
| MDSC 20009 | Introduction to Data Science | 3 |
| Electives | ||
| Students in the Analytics Track must take 9 credits (3 courses) from the list of approved electives. | 9 | |
Notes
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ACMS 30600 Statistical Methods & Data Analysis I is a prerequisite. For approvals, please consult Prof. Alan Huebner, Director of Undergraduate Studies, ACMS. Acceptable alternatives include:
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Econometrics (ECON 30331) if students also have demonstrated competency in R programming;
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Advanced Statistics (PSY 40120); and
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other approved combinations of R programming, inference, and multiple regression.
-
-
R for Data Science (PSY 30109) will not count if students have already taken ACMS 24215.