Phase 4: Exploratory Data Analysis · ~30 minutes · Python · pandas

Asking Good Questions of Data

A finding that only shows up in the whole dataset, and hasn't been checked against any slice of it, isn't a finding yet — it's a first guess that happens to look plausible.

Hiring signal: Checks whether a finding holds across at least one different real slice of the data before reporting it, and can tell the difference between a robust result and a plausible-looking one resting on too little data

What you will learn

Apply EDA as an iterative methodology: question, look, refine the question, look again
Check whether a finding holds across a different slice of the same data
Identify when a finding rests on too small a sample to trust, even if the overall pattern looks clean
Propose a specific check that could break a claimed finding

Introduction

Type: Learn Languages: Python, pandas Prerequisites: Lesson 03 (Correlation Is Not the Whole Story) Time: ~30 minutes

Objective

Learning objectives

Apply EDA as an iterative methodology: question, look, refine the question, look again
Check whether a finding holds across a different slice of the same data
Identify when a finding rests on too small a sample to trust, even if the overall pattern looks clean
Propose a specific check that could break a claimed finding

Unlock the full lesson

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

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