Environments and Tools for Large-scale Behavioral Data Science
Students conduct large-scale behavioral data science projects independently and collaboratively using contemporary software tools and environments.
Summary
What you’ll be able to do
Use online crowd-sourcing platforms for collecting behavioral data, such as Amazon Mechanical Turk, and understand issues of design, sampling, and interpretation associated with such platforms.
Use integrated tools for conducting, documenting, and publishing complex behavioral data analyses, including JuPyTeR notebooks and R Markdown.
Use the GitHub platform to conduct collaborative behavioral data science, including documentation, analysis development, versioning, forking and merging.
Use the SQL database management system to manage large behavioral datasets
Use docker containers and related tools for ensuring fully transparent and replicable behavioral data science
Understand how to access and use high-throughput and high-performance infrastructure for computationally expensive jobs, and when use of these platforms is warranted.
Source: UW Guide
Grade history
↗No recorded grade history.
All recorded terms · compare terms & instructors
Prerequisites
Course mapProfessors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Provides students with knowledge and experience conducting large-scale behavioral data science projects, independently and in collaboration with others, using a variety of contemporary software tools and environments.
difficulty & workload
No workload feedback recorded.
Topics
- Online crowd-sourcing platforms for data collection
- Integrated tools for behavioral data analysis and publication
- Collaborative data science and version control on GitHub
- SQL database management for large datasets
- Docker containers for reproducible science
- High-throughput and high-performance computing infrastructure
Skills
- Collecting behavioral data using online crowd-sourcing platforms like Amazon Mechanical Turk
- Conducting and publishing behavioral data analyses using JuPyTeR notebooks and R Markdown
- Collaborative data science on GitHub including versioning, forking, and merging
- Managing large behavioral datasets with SQL
- Ensuring transparency and replicability using docker containers
- Accessing and using high-throughput infrastructure for computationally expensive jobs
Grades
Grade distribution · % of letter grades
No grades available yet.
More grade details Grade mix, volume & source data
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
No records available.
Grade history
Recorded grade distributions by term, section, and instructor.
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": "787f4a7dc1bf2073661c6701a3c15e5ee355589350b6ed5802e6a2bce0eeb369",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}