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

Decision trees


Decision trees are supervised models that can either preform regression or classification.

Let's take a look at some major league baseball player data from 1986-1987. Each dot represents a single player in the league:

  • Years (x axis): Number of years played in the major leagues

  • Hits (y axis): Number of hits the player had in the previous year

  • Salary (color): Low salary is blue/green, high salary is red/yellow

The preceding data is our training data. The idea is to build a model that predicts the salary of future players based on Years and Hits. A decision tree aims to make splits on our data in order to segment the data points that act similarly to each other, but differently to the others. The tree makes multiples of these splits in order to make the most accurate prediction possible. Let's see a tree built for the preceding data:

Reading from top to bottom:

  • The first split is Years < 4.5, when a splitting rule is true, you follow the left branch. When a splitting rule is false...