Analisis Data Eksploratif di Python

What's the best way to approach a new dataset? Learn to validate and summarize categorical and numerical data and create Seaborn visualizations to communicate your findings.

Exploring and analyzing data often means dealing with missing values, incorrect data types, and outliers. In this chapter, you’ll learn techniques to handle these issues and streamline your EDA processes!

Variables in datasets don't exist in a vacuum; they have relationships with each other. In this chapter, you'll look at relationships across numerical, categorical, and even DateTime data, exploring the direction and strength of these relationships as well as ways to visualize them.

Exploratory data analysis is a crucial step in the data science workflow, but it isn't the end! Now it's time to learn techniques and considerations you can use to successfully move forward with your projects after you've finished exploring!