Session 3: From Analysis to Dashboard

Session 3 moves from reusable agent workflows into dashboard delivery. The central shift is that analysis outputs become a contract between Python work and the dashboard layer.

Participants examine how the Seattle Public Library teaching dataset was built, then use percent-cell Python files to generate machine-readable data files, preserve provenance, and prepare outputs that a browser dashboard can read directly. Multi-agent workflows are useful here when the parent agent keeps ownership of the plan while smaller subagents handle bounded investigation or implementation tasks.

Page	Questions	Objectives
Building Datasets with Codex	How do you turn a public data source into a usable teaching dataset? When should you use complete aggregate files or sampled detail files? How do sampling choices shape the claims learners can make?	Investigate a public API access pattern before retrieval. Separate volume questions from composition questions. Define row meaning, sampling strategy, reproducibility, and validation checks.
Multi-Agent Workflows	What is a multi-agent or subagent workflow? When should you use subagents? How do you use subagents without losing control of the project?	Distinguish parent, worker, and checker responsibilities. Decide when a task is ready to delegate. Write bounded subagent task contracts and review the results.
Build and Deploy a Static Dashboard: Prompt Notes	How can Codex turn dashboard-ready CSV files into a static dashboard? How can prompts change the chart, library, or dashboard component? What steps publish a static dashboard with GitLab Pages?	Use prompts to generate and revise a static HTML/CSS/JavaScript dashboard. Keep dashboard data loaded from CSV files at runtime. Follow the basic GitLab Pages deployment sequence for static assets.
Open Dataset Starting Points	What public datasets could support a student dashboard? Which sources have fields that support filtering, aggregation, maps, or time series? How can you choose a dataset that leads to a clear dashboard question?	Compare several public data sources for dashboard potential. Select a dataset with a usable unit of analysis and meaningful dimensions. Turn a dataset idea into a focused dashboard question.