AI Accelerator: Analytics and Data Analysis

Tutorial-style lessons for AI-assisted analytics from exploration to dashboard.

How To Read This Site

Start with the first section, then progress through the remaining modules in order.

The learning material starts notebook-first with Codex in a JupyterLab terminal, then transitions to broader CLI/project workflows for reproducible dashboard delivery.

Pre-Workshop: Set Up Your Environment and Coding Assistant

Setup page: Open Setup

Page	Questions	Objectives
Set Up	What do I need to complete before Session 1 so my machine is workshop-ready?	Verify core tooling: Python 3.11+, venv, pip, Node.js, npm, and VS Code Check that the required tools are installed and working (Python, Node.js, npm, and VS Code) Install Codex and the connector needed for the workshop Sign in to Codex using the method for your Duke affiliation Set up your project environment, install the required packages, and run the final setup checklist

Session 1: Foundations for AI-Assisted Analytics Work

Section page: Open Session 1

Use Codex as a coding agent for analytics work while keeping the analysis understandable, reviewable, and traceable.

Page	Questions	Objectives
Coding Agents for Analytics Work	What can a coding agent do, and what must its output satisfy?	Identify the understandable, reviewable, and traceable requirements for agent-produced analysis.
Prompting and Project Rules	How do prompts and an AGENTS.md file determine what Codex produces?	Write prompts and project rules that produce reproducible notebook artifacts.
Understanding the Data Before Analysis	What does one row represent, and which file answers which question?	Investigate data structure before plotting or interpreting trends.
Planning and Running an Analysis	How have physical and digital checkouts changed over time, and how can Codex help plan a new visualization?	Select the correct dataset, account for partial years, compare visualization approaches, and trace results to code.
Managing Context in Coding Agent Conversations	What belongs in the conversation, and what belongs in project files?	Distinguish working context from saved project context and decide where verified facts, decisions, and workflow rules should live.
Homework: Extend the Keyword Analysis	How can a messy keyword field be investigated with more than one model or method?	Extend the subject-tag analysis, compare approaches, and identify what should remain human-reviewed.