Exploratory Data Analysis Techniques in Python
This course will teach you how to explore, analyze, and visualize large datasets by using popular python libraries like NumPy, Pandas, and Matplotlib to perform Exploratory Data Analysis (EDA).
What you'll learn
Exploratory Data Analysis (EDA) is a crucial step in any data analysis project.
In this course, Exploratory Data Analysis Techniques in Python, you'll gain the ability to perform EDA on large data sets using Python.
First, you'll learn about visual and clustering exploratory techniques to identify patterns, clusters, and relationships within your data.
Next, you'll discover data distribution, including quantitative, summary, and descriptive techniques that will help you understand the distribution of your data and its key features.
Finally, you'll understand how to use sampling and correlation techniques to explore the relationships between different variables in your data.
When you’re finished with this course, you’ll have the skills and knowledge of exploratory data analysis needed to analyze, visualize, and summarize your data better than ever before!
Table of contents
- Overview 1m
- Introducing Quantitative, Summary, and Descriptive techniques 2m
- Understanding Quantitative Exploratory Techniques Using Python 7m
- Understanding Summary Techniques Using Python 3m
- Understanding Descriptive Techniques Using Python 7m
- Compare and Contrast the Types of Quantitative, Summary, and Descriptive Techniques 2m
- Summary 1m
