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

Summary


In this chapter, we took a look at some basic mathematical principles that will become very important as we progress through this book. Between logarithms/exponents, matrix algebra, and proportionality, mathematics clearly has a big role not just in the analysis of data but in many aspects of our lives.

The coming chapters will take a much deeper dive into two big areas of mathematics: probability and statistics. It will become our goal to define and interpret the smallest and biggest theorems in these two giant fields of mathematics.

It is in the next few chapters that everything will start to come together. So far in this book, we have looked at math examples, data exploration guidelines, and basic insights into the types of data. It is time to begin to tie all of these concepts together.