Educational Data Science
Educational Data Science examines drawing meaning and making decisions from data on students, teaching, and education systems. Students develop skills in data transformation, visualization, modeling, and reproducible R-based reporting.
Summary
What you’ll be able to do
Apply data visualization techniques to create informative graphical representations of educational data.
Transform and summarize educational data, including hierarchical, longitudinal, and survey/assessment formats.
Interpret and evaluate educational data displays and models for research, instruction, and policy use.
Write reproducible R code for cleaning, analyzing, and reporting educational data.
Apply introductory text mining and network analysis to education datasets.
Describe the relevance of data science methods for specific topical domains of education research.
Source: UW Guide
Grade history
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Prerequisites
Course mapED POL 309,STAT 240, 301, 324, 371 or Graduate/professional standing
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Professors
Spring 2027No instructors recorded for this selection.
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Calendar & sections
Spring 2027Schedule loads here as you scroll.
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Student experience
the class
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.
difficulty & workload
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Topics
- Students, teaching, and education systems
- Data transformation and summarization
- Graphical representation of data
- Formal modeling
- Real-world case studies
Skills
- Data visualization for educational datasets
- Transformation and summarization of hierarchical, longitudinal, and survey data
- Interpretation and evaluation of educational data models
- Reproducible data analysis using R
- Introductory text mining and network analysis
Grades
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Sources & history
Catalog & offerings
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Grade history
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Explore recorded gradesRecorded 2026-10-14
Model outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "db3f8589339e36ccb06d4e93e2d86dbb1d0ac9fe3934aef89b4c33c2da6eb807",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}