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

Outliers and Data Quality

A $9,662/night listing and a $2,625/night listing can both be real IQR outliers — and only one of them is actually explained by anything in the rest of its own row.

Hiring signal: Investigates each outlier individually against its own other columns before deciding whether it's real, an edge case, or a likely error — never applies a blanket rule

What you will learn

Identify outliers numerically using the IQR method
Investigate whether a specific outlier is explained by other real data in its own row
Distinguish a genuine extreme value from a likely data-quality problem, with evidence
Explain why deleting every outlier by default risks removing real signal

Introduction

Type: Learn Languages: Python, pandas Prerequisites: Lesson 01 (What EDA Actually Is) Time: ~30 minutes

Objective

Learning objectives

Identify outliers numerically using the IQR method
Investigate whether a specific outlier is explained by other real data in its own row
Distinguish a genuine extreme value from a likely data-quality problem, with evidence
Explain why deleting every outlier by default risks removing real signal

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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