Interdisciplinary Applied Data Science (Minor)
The Undergraduate Minor of Interdisciplinary Applied Data Science is a 15 credit credential that offers a path towards developing essential skills in data science with depth in interdisciplinary content. Students who pursue this minor will have the opportunity to learn from data science instructors and practitioners, and interdisciplinary faculty in industry and academia, alongside their peers from various colleges. Students will pursue courses in data management, communication, applications, ethics, humanities, and sciences, among other electives and focus areas of choice.
Contact
Data Science Academy
Plan Requirements
Required Courses
| Code | Title | Hours |
|---|---|---|
| Required DSC Courses: Six credits, at least one course from each category | 6 | |
| Categories and Corresponding Category Numbers (in parentheses) | ||
Data Management & Analysis (1) | ||
Data Communication (2) | ||
Ethics, Policy, & Privacy (3) | ||
Machine Learning and AI (4) | ||
Electives or Internships & Capstones (5) | ||
| Course Options and Corresponding Category Numbers | ||
| Introduction to R/Python for Data Science (1) | ||
| Introduction to Data Visualization (2) | ||
| Data Communication (2) | ||
| Introduction to AI Ethics (3), (4) | ||
| Data Science for Social Good (3) | ||
| Introduction to Data Science for Cybersecurity (3) | ||
| Measuring Success (1), (3) | ||
| Data Wrangling and Web Scraping (1) | ||
| Exploratory Data Analysis for Big Data (1) | ||
| Data Internship Preparation for Social Impact (5) | ||
| Exploring Machine Learning (4) | ||
| Introductory Special Topics in Data Science See semesterly list of special topics courses accepted within a category | ||
| Special Topics in Data Science See semesterly list of special topics courses accepted within a category | ||
| Graduate Special Topics in Data Science See semesterly list of special topics courses accepted within a category | ||
| Courses not used for a category requirement may be applied to fulfill "Electives or Internships & Capstones (5)" | ||
| Required Depth Courses | 9 | |
| Up to 3 of the following: Humanities and Social Sciences Analytics | ||
| Critical Analysis of Communication Media | ||
| Public Policy Analysis and Evaluation | ||
| Social Welfare Policy: Analysis and Advocacy | ||
| Verbal Data Analysis | ||
| Quantitative Data Analysis in Sociology | ||
| Survey Design | ||
| Up to 3 of the following: Natural Resources Analytics | ||
| Environmental Monitoring and Analysis | ||
| GIS and Remote Sensing for Environmental Analysis and Assessment | ||
| Forest Measurement, Modeling, and Inventory | ||
| Principles of Wildlife Science | ||
| Environmental Life Cycle Analysis | ||
| Up to 2 of the following (or MAE 420 and 2 others): Engineering Analytics | ||
| Dynamic Analysis of Human Movement | ||
| Deterministic Models in Industrial Engineering | ||
| Database Applications in Industrial & Systems Engineering | ||
| Python Programming for Industrial & Systems Engineers | ||
| Data Analytics for Industrial Engineering | ||
| Applications of Data Science in Healthcare | ||
| Introduction to Machine Learning | ||
| Up to 3 of the following: Analytical Sciences | ||
| Quantitative Analysis | ||
| Mathematical Foundations of Data Science I | ||
| Mathematics of Scientific Computing | ||
| Observational Methods and Data Analysis in Marine Physics | ||
| Introduction to Regression Analysis | ||
| Introduction to Data Science | ||
| Introduction to Statistical Computing and Data Management | ||
| Intermediate SAS Programming with Applications | ||
| Statistical Learning and Data Analytics | ||
| Advanced Computing for Statistical Reasoning | ||
| Up to 3 of the following: Business & Management Analytics | ||
| Financial Analytics | ||
| Financial Modeling | ||
| Operations Modeling and Analysis | ||
| Decision Modeling and Analysis | ||
| Analytics: From Data to Decisions | ||
| People Analytics | ||
| Up to 3 of the following: Education and Learning Analytics | ||
| Robotics Education | ||
| Teaching Mathematics with Technology | ||
| Introduction to Learning Analytics | ||
| Machine Learning in Education | ||
| Text Mining in Education | ||
| Social Network Analysis in Education | ||
| Up to 1 of the following: Additional Options | ||
| R Coding for Data Management and Analysis | ||
| Computer Science Principles - The Beauty and Joy of Computing | ||
| Introduction to Computing: Python | ||
| Introduction to Computing - MATLAB | ||
| Introduction to Computing - Java | ||
| Introduction to Laban Movement Analysis and Bartenieff Fundamentals | ||
| Data Ethics | ||
| Big Data in Your Pocket: Call it a Smartphone | ||
| Textile Information Systems Design | ||
| NOTE 1: Certain courses may have prerequisites and some courses may not be offered every semester. Please check the university catalog to plan accordingly and/or contact the Minor Coordinator in the DSA. | ||
| NOTE 2: Students must be classified as seniors to pursue the 500-level ECI courses. | ||
| NOTE 3: For Applied Mathematics, Mathematics, and Statistics majors, only Free Electives and Advised Electives (as indicated on the respective degree audits) may be applied towards both the respective Majors and the Data Science minor. | ||
| NOTE 4: Students pursuing multiple Data Science Academy credentials must have at least 2 distinct 1-credit DSC courses and 2 distinct 3-credit depth courses between any two (8 distinct credits total). | ||