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've seen three different case studies from three different domains using many different statistical and machine learning methods. However, what all of them have in common is that in order to solve them properly, we had to implement a data science mindset. We had to solve problems in an interesting way, obtain data, clean the data, visualize the data, and finally, model the data and evaluate our thinking process.

I do hope that you have found the contents of this book to be interesting and not just the final chapter! I leave it unto you to keep exploring the world of data science. Keep learning Python. Keep learning statistics and probability. Keep your minds open. It is my hope that this book has been a catalyst for you to go out and find even more on the subject.

For further readings past this book, I highly recommend looking into well-known data science books and blogs, such as:

  • Dataschool.io—blog by Kevin Markham

  • Python for Data Scientists by Packt

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