#### Overview of this book

Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You’ll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You’ll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you’ll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you’ll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you’ll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes.
Preface
Section 1: The Fundamentals of EDA
Free Chapter
Exploratory Data Analysis Fundamentals
Visual Aids for EDA
EDA with Personal Email
Data Transformation
Section 2: Descriptive Statistics
Descriptive Statistics
Grouping Datasets
Correlation
Time Series Analysis
Section 3: Model Development and Evaluation
Hypothesis Testing and Regression
Model Development and Evaluation
EDA on Wine Quality Data Analysis
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Appendix

# Correlation

In this chapter, we will explore the correlation between different factors and estimate to what degree these different factors are reliable. Additionally, we will learn about the different types of examinations we can carry out in order to discover the relationship between data including univariate analysis, bivariate analysis, and multivariate analysis. We will perform these analyses using the Titanic dataset. We'll also introduce Simpson's paradox. Likewise, we will take an insightful look at the well-known fact that correlation does not imply causation.

In this chapter, we will cover the following topics:

• Introducing correlation
• Understanding univariate analysis
• Understanding bivariate analysis
• Understanding multivariate analysis
• Discussing multivariate analysis using the Titanic dataset