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

Chapter 7. Basic Statistics

This chapter will focus on the statistics required by any aspiring data scientist.

We will explore ways of sampling and obtaining data without being affected by bias and then use measures of statistics to quantify and visualize our data. Using the z-score and the Empirical rule, we will see how we can standardize data for the purpose of both graphing and interpretability.

In this chapter, we will look at the following topics:

  • How to obtain and sample data

  • The measures of center, variance, and relative standing

  • Normalization of data using the z-score

  • The Empirical rule