Data Science in K-12 Education (Minor)
The Undergraduate Minor of Data Science in K-12 Education is a 15 credit credential that offers a path towards developing essential skills in data science with depth in education content. Students who pursue this minor will have the opportunity to learn from data science instructors & practitioners, and interdisciplinary faculty in industry & academia, alongside their peers from various colleges. Students will pursue courses in data management, communication, applications, ethics, and education, among other electives and focus areas of choice.
Contact
Data Science and AI Academy
Plan Requirements
Required Courses
| Code | Title | Hours |
|---|---|---|
| Required DSA 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 | |
| Mathematics & Statistics Courses (Choose 1) | ||
| Topics in Contemporary Mathematics | ||
| Introductory Linear Algebra and Matrices | ||
or MA 405 | Introduction to Linear Algebra | |
| Statistics by Example | ||
| Introduction to Statistics Course credit from AP Statistics can be substituted for this course and requirement | ||
| STEM Education Courses (Choose 1) | ||
| Children's Thinking and Additive Reasoning | ||
| Instructional Materials in Science | ||
| Teaching Mathematics with Technology | ||
| Technology Through Engineering and Design II | ||
| Robotics Education | ||
| Interdisciplinary Society, Data, & Technology Courses (Choose 1) | ||
| Data Ethics | ||
| Big Data in Your Pocket: Call it a Smartphone | ||
| Contemporary Science, Technology and Human Values | ||
| Technology and American Culture | ||
| NOTE 1: Certain courses may have prerequisites. Please check the university catalog to plan accordingly and/or contact the Minor Coordinator in the DSA. | ||
| NOTE 2: Students pursuing multiple Data Science and AI Academy credentials must have at least 2 distinct 1-credit DSA courses and 2 distinct 3-credit depth courses between any two credentials (8 distinct credits total). | ||
| Total Hours | 15 | |