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


Data communication is not an easy task. It is one thing to understand the mathematics of how data science works, but it is a completely different thing to try to convince a room of data scientists and non-data scientists alike of your results and their value to them. In this chapter, we went over basic chart making as well as how to identify faulty causation and the ability to hone our oral presentation skills.

Our next few chapters will really begin to hit at one of the biggest talking points of data science. In the last nine chapters, we spoke about everything between how to obtain data, clean data, and visualize data in order to gain a better understanding of the environment that the data represents.

We then turned to looking at the basic and advanced probability/statistics laws in order to use quantifiable theorems and tests on our data to get actionable results and answers.

In subsequent chapters, we will take a look into machine learning and the nature in which machine learning...