Book Image

Practical Data Science Cookbook

By : Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta
Book Image

Practical Data Science Cookbook

By: Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta

Overview of this book

<p>As increasing amounts of data is generated each year, the need to analyze and operationalize it is more important than ever. Companies that know what to do with their data will have a competitive advantage over companies that don't, and this will drive a higher demand for knowledgeable and competent data professionals.</p> <p>Starting with the basics, this book will cover how to set up your numerical programming environment, introduce you to the data science pipeline (an iterative process by which data science projects are completed), and guide you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples in the two most popular programming languages for data analysis—R and Python.</p>
Table of Contents (18 chapters)
Practical Data Science Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Introduction


American football is the most popular sport in the United States and is the ninth most popular sport worldwide. Every year, football fans look forward to the start of a new season in September, the 17 weeks of play that follow, the playoffs that start in January, and the championship game known as the Super Bowl in late January or early February.

In this chapter, we will obtain some football statistics, analyze them to get a sense of what the data looks like, determine a way to calculate which team should win when two teams play each other, and then use this to simulate games to produce a virtual winning team and losing team. There are many different ways in which you can construct such a simulation. For example, if you want to construct your simulation at the level of individual plays, you can get the statistics for every single player on a team and every play that a team runs, and use these to simulate a game in a play-by-play fashion. This approach would be great if we were...