Exploratory Data Analysis with Python
Learn how to get the most out of your data using Exploratory Data Analysis. In this course you'll acquire the skills to get the insight you need from your data and take better decisions.
What you'll learn
Exploratory Data Analysis (EDA) is a set of techniques that helps you to understand data, and every Data Analyst and Data Scientist should know it in depth. In this course, Exploratory Data Analysis with Python, you'll learn how to create and implement an EDA pipeline. You'll explore the available techniques, and learn why, when, and how to apply them. Finally, you'll discover how to communicate your findings to your audience. When you’re finished with this course, you will have the skills and knowledge to face any complex EDA problem.
Table of contents
- 01 Overview 1m
- 02 Tracing A Knowledge Map 3m
- 03 Characterizing Data 5m
- 04 Demo Characterizing Data 4m
- 05 Univariate Distribution Plots 5m
- 06 Demo Univariate Distribution Plots 3m
- 07 Univariate Comparison Plots 4m
- 08 Demo Univariate Comparison Plots 6m
- 09 Univariate Composition Plots 2m
- 10 Demo Univariate Composition Plots 4m
- 11 Univariate Analysis Tests 4m
- 12 Demo Univariate Analysis Tests 2m
- 01 Overview 0m
- 02 Finding Relationships In Data 4m
- 03 Demo Finding Relationships In Data 2m
- 04 Multivariate Distribution Plots 3m
- 05 Demo Multivariate Distribution Plots 3m
- 06 Multivariate Comparison Plots 1m
- 07 Demo Multivariate Comparison Plots 2m
- 08 Multivariate Relationship Plots 1m
- 09 Demo Multivariate Relationship Plots 2m
- 10 Multivariate Composition Plots 1m
- 11 Demo Multivariate Composition Plots 3m