AI-Assisted Exploratory Data Analysis

Course details

Turn a raw data file into a one-page visual summary your stakeholders will actually use. Join instructor Mo Chen for a five-part process for AI-assisted exploratory data analysis: brief the AI on what the data is (and isn't), profile it for problems, test hypotheses against a split of the data, land on one honest headline, and build the charts that back it up. Mo also shows where generic AI prompts fall short, and how to tell a real finding from a pattern that just happens to look like one.

Learning objectives
Frame an exploration goal that focuses AI-assisted analysis on what actually matters.
Use AI to profile a dataset for quality issues, distributions, and anomalies.
Generate and test hypotheses from a data profile, distinguishing genuine patterns from statistical artifacts.
Synthesize findings into a single honest headline that supports a real decision.
Produce a one-page visual EDA summary using a reusable design spec.

Concepts

Introduction

Where do you start with a pile of data

Setting Up for AI-Assisted Exploratory Data Analysis (EDA)

Set up the exploration before you touch the data
Compare a cold analyze this with a briefed prompt

Profiling and Understanding Your Data

Treat a raw export as a first draft, not the truth
Catch what a general data check quietly misses

Generating and Testing Hypotheses

Split the data before you believe a pattern
Test different patterns and see which ones hold

Synthesizing Your Findings

Turn a pile of findings into one honest story
Let AI draft the spine, then own the judgment on it

Building the Visual Summary

Get presentation-grade charts out of a reusable brief
Build the one-page summary a founder can act on