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

Dummy variables


Dummy variables are used when we are hoping to convert a categorical feature into a quantitative one. Remember that we have two types of categorical features: nominal and ordinal. Ordinal features have natural order among them, while nominal data does not.

Encoding qualitative (nominal) data using separate columns is called making dummy variables and it works by turning each unique category of a nominal column into its own column that is either true or false.

For example, if we had a column for someone's college major and we wished to plug that information into a linear or logistic regression, we couldn't because they only take in numbers! So, for each row, we had new columns that represent the single nominal column. In this case, we have four unique majors: computer science, engineering, business, and literature. We end up with three new columns (we omit computer science as it is not necessary).

Note that the first row has a 0 in all the columns, which means that this person...