Book Image

Principles of Data Science

Book Image

Principles of Data Science

Overview of this book

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.
Table of Contents (20 chapters)
Principles of Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

When graphs and statistics lie


I should be clear, statistics don't lie, people lie. One of the easiest ways to trick your audience is to confuse correlation with causation.

Correlation versus causation

I don't think I would be allowed to publish this book without taking a deeper dive into the differences between correlation and causation. For this example, I will continue to use my data of TV consumption and work performance.

Correlation is a quantitative metric between -1 and 1 that measures how two variables move with each other. If two variables have a correlation close to -1, it means that as one variable increases, the other decreases, and if two variables have a correlation close to +1, it means that those variables move together in the same direction—as one increases, so does the other, and vice versa.

Causation is the idea that one variable affects another.

For example, we can look at two variables: the average hours of TV watched in a day and a 0-100 scale of work performance (0 being...