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

K-means clustering


K-means clustering is our first example of an unsupervised machine learning model. Remember this means that we are not making predictions; we are trying instead to extract structure from seemingly unstructured data.

Clustering is a family of unsupervised machine learning models that attempt to group data points into clusters with centroids.

Note

Definition:

Cluster: A group of data points that behave similarly.

Definition:

Centroid: The center of a cluster. Can be thought of as an average point in the cluster.

The preceding definition can be quite vague, but it becomes specific when narrowed down to specific domains. For example, online shoppers who behave similarly might shop for similar things or at similar shops, whereas similar software companies might make comparable software at comparable prices.

Here is a visualization of clusters of points:

In the preceding figure, our human brains can very easily see the difference between the four clusters. Namely that the red cluster...