Hands-On Exploratory Data Analysis with Python

By Suresh Kumar Mukhiya , Usman Ahmed
  • Instant online access to over 7,500+ books and videos
  • Constantly updated with 100+ new titles each month
  • Breadth and depth in over 1,000+ technologies
  1. Section 1: The Fundamentals of EDA

About 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.

Publication date:
March 2020


Section 1: The Fundamentals of EDA

The main objective of this section is to cover the fundamentals of Exploratory Data Analysis (EDA) and understand different stages of the EDA process. We will also look at the key concepts of profiling, quality assessment, the main aspects of EDA, and the challenges and opportunities in EDA. In addition to this, we will be discovering different useful visualization techniques. Finally, we will be discussing essential data transformation techniques, including database-style dataframe merges, transformation techniques, and benefits of data transformation. 

This section contains the following chapters:

About the Authors

  • Suresh Kumar Mukhiya

    Suresh Kumar Mukhiya is a PhD candidate, currently affiliated to the Western Norway University of Applied Sciences (HVL). He is a big data enthusiast, specializing in Information Systems, Model-Driven Software Engineering, Big Data Analysis, Artificial Intelligence and Frontend development. He has completed a Masters in Information Systems from the Norwegian University of Science and Technology (NTNU, Norway) along with a thesis in processing mining. He also holds a bachelor's degree in computer science and information technology (BSc.CSIT) from Tribhuvan University, Nepal, where he was decorated with the Vice-Chancellor's Award for obtaining the highest score. He is a passionate photographer and a resilient traveler.

    Browse publications by this author
  • Usman Ahmed

    Usman Ahmed is a data scientist and Ph.D. candidate at Western Norway University of Applied Science (HVL). He has rich experience in building and scaling high-performance systems based on data mining, natural language processing, and machine learning. Usman's research interests are sequential data mining, heterogeneous computing, natural language processing, a recommendation system, and machine learning. He has completed a Master's of Science in computer science from Capital University of Science and Technology, Islamabad, Pakistan. Usman Ahmed was awarded Gold Medal in Bachelor of Computer Science from Heavy Industries Taxila Education City.

    Browse publications by this author
Book Title
Unlock this full book FREE 10 day trial
Start Free Trial