# Topic 1. Exploratory data analysis with Pandas¶

You definitely want to immediately jump into Machine Learning and see math in action. But 70-80% of the time working on a real project is fussing with data, and here Pandas is very helpful, I use it in my work almost every day. This article describes the basic Pandas methods for preliminary data analysis. Then we analyze the dataset on the churn of telecom customers and try to predict churn without any model training, simply relying on common sense (and Pandas of course). By no means should you underestimate such an approach.

## Steps in this block¶

Read the article “Exploratory data analysis with Pandas” (same in a form of a Kaggle Notebook);

Watch a video lecture “Pandas & Data Analysis” (optional);

Complete demo assignment 1 (same as a Kaggle Notebook) where you’ll be exploring demographic data, the UCI “Adult” dataset;

Check out the solution (same as a Kaggle Notebook) to the demo assignment (optional);

Complete Bonus Assignment 1 where you’ll be analyzing the history of the Olympic Games with Pandas (optional, available under Patreon “Bonus Assignments” tier).