EDUCATIONAL DATA SCIENCE
Examines the practice of data science in educational contexts, examining how we draw meaning and make decisions on the basis of data related to students, teaching, formal education systems, and informal learning environments. Focuses on developing fundamental skills for actively working with and interpreting educational data related to students, teaching, formal education systems, and informational learning environments., including: transforming, combining, and effectively summarizing data; creating meaningful graphical representations of data; creating formal models based on data; and communicating findings from data analysis. Develop skills by writing computer code to create reproducible data analysis reports. After introducing foundational data science skills, work through a series of real-world case studies that provide an opportunity to practice, expand, and further refine the skills and to make connections to subject-matter knowledge in the context of education.
Prerequisites & course information
Requisites: ED POL 309 , STAT 240 , 301 , 324 , 371 or Graduate/professional standing Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Breadth - Social Science Level - Intermediate L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Learning Outcomes: 1. Apply data visualization techniques to create informative graphical representations of educational data. Audience: Both Grad & Undergrad 2. Transform and summarize educational data, including hierarchical, longitudinal, and survey/assessment formats. Audience: Both Grad & Undergrad 3. Interpret and evaluate educational data displays and models for research, instruction, and policy use. Audience: Both Grad & Undergrad 4. Write reproducible R code for cleaning, analyzing, and reporting educational data. Audience: Both Grad & Undergrad 5. Apply introductory text mining and network analysis to education datasets. Audience: Both Grad & Undergrad 6. Describe the relevance of data science methods for specific topical domains of education research. Audience: Graduate Learning Outcomes: 1. Apply data visualization techniques to create informative graphical representations of educational data. Audience: Both Grad & Undergrad 2. Transform and summarize educational data, including hierarchical, longitudinal, and survey/assessment formats. Audience: Both Grad & Undergrad 3. Interpret and evaluate educational data displays and models for research, instruction, and policy use. Audience: Both Grad & Undergrad 4. Write reproducible R code for cleaning, analyzing, and reporting educational data. Audience: Both Grad & Undergrad 5. Apply introductory text mining and network analysis to education datasets. Audience: Both Grad & Undergrad 6. Describe the relevance of data science methods for specific topical domains of education research. Audience: Graduate
Sources
- Course catalogue ↗2026-2027