Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide
Purchase of the print or Kindle book includes a free PDF eBook
Key Features
- Gain practical experience in conducting EDA on a single variable of interest in Python
- Learn the different techniques for analyzing and exploring tabular, time series, and textual data in Python
- Get well versed in data visualization using leading Python libraries like Matplotlib and seaborn
Book Description
Exploratory data analysis (EDA) is a crucial step in data analysis and machine learning projects as it helps in uncovering relationships and patterns and provides insights into structured and unstructured datasets. With various techniques and libraries available for performing EDA, choosing the right approach can sometimes be challenging. This hands-on guide provides you with practical steps and ready-to-use code for conducting exploratory analysis on tabular, time series, and textual data.
The book begins by focusing on preliminary recipes such as summary statistics, data preparation, and data visualization libraries. As you advance, you’ll discover how to implement univariate, bivariate, and multivariate analyses on tabular data. Throughout the chapters, you’ll become well versed in popular Python visualization and data manipulation libraries such as seaborn and pandas.
By the end of this book, you will have mastered the various EDA techniques and implemented them efficiently on structured and unstructured data.
What you will learn
- Perform EDA with leading Python data visualization libraries
- Execute univariate, bivariate, and multivariate analyses on tabular data
- Uncover patterns and relationships within time series data
- Identify hidden patterns within textual data
- Discover different techniques to prepare data for analysis
- Overcome the challenge of outliers and missing values during data analysis
- Leverage automated EDA for fast and efficient analysis
Who this book is for
If you are a data analyst interested in the practical application of exploratory data analysis in Python, then this book is for you. This book will also benefit data scientists, researchers, and statisticians who are looking for hands-on instructions on how to apply EDA techniques using Python libraries. Basic knowledge of Python programming and a basic understanding of fundamental statistical concepts is a prerequisite.
Table of Contents
- Generating Summary Statistics
- Preparing Data for EDA
- Visualising Data in Python
- Performing Univariate Analysis in Python
- Performing Bivariate analysis in Python
- Performing Multivariate analysis in Python
- Analysing Time Series data
- Analysing Text data
- Dealing with Outliers and Missing values
- Performing Automated EDA in Python
Ayodele is a certified data professional with a rich cross functional background that spans across strategy, data management, analytics and data science. He currently leads a team of data professionals that spearheads data science and analytics initiatives across a leading African non-banking financial services group. Prior to this role, he spent over 8 years at a big four consulting firm working on strategy, data science and automation projects for clients across various industries. In that capacity, he was a key member of the data science and automation team which developed a proprietary big data fraud detection solution used by many Nigerian financial institutions today. To learn more about him, visit his LinkedIn profile
