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).

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!

• Course Overview 1min
• Introducing Exploratory Data Analysis (EDA) 12mins
• Using Visual Exploratory Techniques for Data Analysis 30mins
• Clustering Techniques for EDA 25mins
• Understanding Data Distribution Types for EDA 11mins
• Exploring Quantitative, Summary, and Descriptive Techniques for EDA 21mins
• Sampling and Correlation Techniques for EDA 20mins
• Conclusion and Next Steps 4mins