Key Features

Understand the fundamental concepts of exploratory data analysis using Python

Find missing values in your data and identify the correlation between different variables

Practice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python package

Book Description

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.

What you will learn

Import, clean, and explore data to perform preliminary analysis using powerful Python packages

Identify and transform erroneous data using different data wrangling techniques

Explore the use of multiple regression to describe non-linear relationships

Discover hypothesis testing and explore techniques of time-series analysis

Understand and interpret results obtained from graphical analysis

Build, train, and optimize predictive models to estimate results

Perform complex EDA techniques on open source datasets

Who this book is for

This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book.

Table of Contents

Exploratory Data Analysis Fundamentals

Visual Aids for EDA

EDA with Personal Email

Data Transformation

Descriptive Statistics

Grouping Dataset

Correlation

Time Series Analysis

Hypothesis Testing and Regression

Model Development and Evaluation

EDA on Wine Quality Data Analysis

Appendix