Phase 5: Capstone · ~40 minutes · Python · pandas

Choose and Load

A first-week data analyst's job was never to answer a pre-assigned question. It was to load something real and messy and find out what's actually in it.

Hiring signal: Runs a genuine first-pass exploration on unfamiliar real data and generates specific, checkable questions, rather than jumping straight to an assumed analysis

What you will learn

Load a real, previously unseen dataset and run a genuine first-pass EDA
Apply Phase 04's question-generation methodology to new real data
Identify real candidates for data-quality investigation during a first pass
Write specific, investigable questions grounded in real sample sizes

Introduction

Type: Project Languages: Python, pandas Prerequisites: Phases 00-04 (all of Data Analysis & Visualization) Time: ~40 minutes

Objective

Learning objectives

Load a real, previously unseen dataset and run a genuine first-pass EDA
Apply Phase 04's question-generation methodology to new real data
Identify real candidates for data-quality investigation during a first pass
Write specific, investigable questions grounded in real sample sizes

What you're building

Your own first-pass EDA report (initial_pass.md or a script with clear printed sections), covering:

.head(), .info(), and .describe() on the full real dataset — run genuinely, not skipped
At least 2 more real oddities you find yourself (beyond the price extremes shown above) — a suspicious column, a surprising missing-data pattern, an inconsistency between two related fields
Three specific, real, investigable questions this pass raises, each stated precisely enough that Lessons 02-04 could actually go answer it
The real sample size behind anything you report as a pattern (Phase 04's discipline — an average of 2 listings is not the same claim as an average of 2,000)

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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