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Statistics for Data Science

You're reading from   Statistics for Data Science Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788290678
Length 286 pages
Edition 1st Edition
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Author (1):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
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Table of Contents (13) Chapters Close

Preface 1. Transitioning from Data Developer to Data Scientist 2. Declaring the Objectives FREE CHAPTER 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

Declaring the Objectives

This chapter introduces and explains (yet again, from a developer's perspective) the basic objectives behind statistics for data science and introduces the reader to the important terms and key concepts (with explanations and examples) that are used throughout the book.

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

  • A primer on the key objectives of data science
  • Bringing statistics into data science
  • Common terminologies used with statistics and data science
You have been reading a chapter from
Statistics for Data Science
Published in: Nov 2017
Publisher: Packt
ISBN-13: 9781788290678
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