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Statistics for Data Science

You're reading from Statistics for Data Science

Product type Book

Published in Nov 2017

Publisher Packt

ISBN-13 9781788290678

Pages 286 pages

Edition 1st Edition

Languages

Concepts

Table of Contents (19) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Transitioning from Data Developer to Data Scientist

2. Declaring the Objectives

3. A Developer's Approach to Data Cleaning

4. Data Mining and the Database Developer

5. Statistical Analysis for the Database Developer

6. Database Progression to Database Regression

7. Regularization for Database Improvement

8. Database Development and Assessment

9. Databases and Neural Networks

10. Boosting your Database

11. Database Classification using Support Vector Machines

12. Database Structures and Machine Learning

Chapter 4. Data Mining and the Database Developer

This chapter introduces the data developer to mining (not to be confused with querying) data, providing an understanding of exactly what data mining is and why it is an integral part of data science.

We'll provide working examples to help the reader feel comfortable using R for the most common statistical data mining methods: dimensional reduction, frequent patterns, and sequences.

In this chapter, we've broken things into the following topics:

Definition and purpose of data mining
Preparing the developer for data mining rather than data querying
Using R for dimensional reduction, frequent patterns, and sequence mining

The rest of the chapter is locked

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