Book Image

Hands-On Exploratory Data Analysis with Python

By : Suresh Kumar Mukhiya, Usman Ahmed
Book Image

Hands-On Exploratory Data Analysis with Python

By: Suresh Kumar Mukhiya, Usman Ahmed

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.
Table of Contents (17 chapters)
1
Section 1: The Fundamentals of EDA
6
Section 2: Descriptive Statistics
11
Section 3: Model Development and Evaluation

Correlation does not imply causation

Correlation does not imply causation is an interesting phrase that you will hear mostly in statistics and when learning about data science in detail. But what does it mean? Well, it merely indicates that just because two things correlate does not always mean that one causes the other. For example, the Norwegian winter is cold, and people tend to spend more money on buying hot foods such as soup than they do in summer. However, this does not mean that cold weather causes people to spend more money on soup. Therefore, although the expenditure of people in Norway is related to cold weather, the spending is not the cause of the cold weather. Hence, correlation is not causation.

Note that there are two essential terms in this phrase: correlation and causation. Correlation reveals how strongly a pair of variables are related to each other and change...