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

About the Reviewers

Samir Madhavan has over six years of rich data science experience in the industry and has also written a book called Mastering Python for Data Science. He started his career with Mindtree, where he was a part of the fraud detection algorithm team for the UID (Unique Identification) project, called Aadhar, which is the equivalent of a Social Security number for India. After this, he joined Flutura Decision Sciences and Analytics as the first employee, where he was part of the core team that helped the organization scale to an over a hundred members. As a part of Flutura, he helped establish big data and machine learning practice within Flutura and also helped out in business development. At present, he is leading the analytics team for a Boston-based pharma tech company called Zapprx, and is helping the firm to create data-driven products that will be sold to its customers.

Oleg Okun is a machine learning expert and an author/editor of four books, numerous journal articles, and conference papers. His career spans more than a quarter of a century. He was employed in both academia and industry in his mother country, Belarus, and abroad (Finland, Sweden, and Germany). His work experience includes document image analysis, fingerprint biometrics, bioinformatics, online/offline marketing analytics, and credit-scoring analytics.

He is interested in all aspects of distributed machine learning and the Internet of Things. Oleg currently lives and works in Hamburg, Germany.