Book description
Learn the techniques and math you need to start making sense of your data
About This Book
 Enhance your knowledge of coding with data science theory for practical insight into data science and analysis
 More than just a math class, learn how to perform realworld data science tasks with R and Python
 Create actionable insights and transform raw data into tangible value
Who This Book Is For
You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you.
What You Will Learn
 Get to know the five most important steps of data science
 Use your data intelligently and learn how to handle it with care
 Bridge the gap between mathematics and programming
 Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results
 Build and evaluate baseline machine learning models
 Explore the most effective metrics to determine the success of your machine learning models
 Create data visualizations that communicate actionable insights
 Read and apply machine learning concepts to your problems and make actual predictions
In Detail
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.
Style and approach
This is an easytounderstand and accessible tutorial. It is a stepbystep guide with use cases, examples, and illustrations to get you wellversed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.
Downloading the example code for this book. You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the code file.
Publisher resources
Table of contents

Principles of Data Science
 Table of Contents
 Principles of Data Science
 Credits
 About the Author
 About the Reviewers
 www.PacktPub.com
 Preface
 1. How to Sound Like a Data Scientist
 2. Types of Data
 3. The Five Steps of Data Science
 4. Basic Mathematics
 5. Impossible or Improbable – A Gentle Introduction to Probability
 6. Advanced Probability
 7. Basic Statistics
 8. Advanced Statistics
 9. Communicating Data
 10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials
 11. Predictions Don't Grow on Trees – or Do They?
 12. Beyond the Essentials
 13. Case Studies
 Index
Product information
 Title: Principles of Data Science
 Author(s):
 Release date: December 2016
 Publisher(s): Packt Publishing
 ISBN: 9781785887918
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