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 4. Basic Mathematics

It's time to start looking at some basic mathematic principles that are handy when dealing with data science. The word math tends to strike fear in the hearts of many, but I aim to make this as enjoyable as possible. In this chapter, we will go over the basics of the following topics:

  • Basic symbols/terminology

  • Logarithms/exponents

  • The set theory

  • Calculus

  • Matrix (linear) algebra

We will also cover other fields of mathematics. Moreover, we will see how to apply each of these to various aspects of data science as well as other scientific endeavors.

Recall that, in a previous chapter, we identified math as being one of the three key components of data science. In this chapter, I will introduce concepts that will become important later on in this book—when looking at probabilistic and statistical models—and I will also be looking at concepts that will be useful in this chapter. Regardless of this, all of the concepts in this chapter should be considered fundamentals in your...