CS2 - Dealing with Outliers

Detecting, Handling, and Leveraging Anomalies in Data

Outliers: What, Why, and How?

Outliers are the peculiar data points that don’t quite fit with the rest of the dataset. These anomalies can arise from:

• Data entry errors 🖋️

• Measurement issues 🛠️

• Genuine rare events 🌟

While they can skew your analysis and lead to incorrect conclusions, they can also hold valuable insights (think fraud detection or identifying unique customer segments).

This leads us to the main question…

So, what’s the best way to approach them?

So let’s dive into how to deal with outliers!

Before starting, here you have the full-resolution cheatsheet 👇🏻