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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
Languages
Tools
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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 (19) Chapters Close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Transitioning from Data Developer to Data Scientist FREE CHAPTER 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 8. Database Development and Assessment

In this chapter, we will cover the practice of data (database) assessment. We will provide an understanding of what statistical assessment is, and why it is important to the data scientist, as well as providing instructive examples using R to perform various statistical assessment methods.

As we have been endeavoring to do throughout this book, we will draw similarities between certain data developer and data scientist concepts, looking at the differences between data or database development and data (database) assessment, as well as offer a comparison between the practice of data assessment and data (quality) assurance.

We've organized information in this chapter into the following areas:

  • Comparison of assessment and statistical assessments
  • Development versus assessment
  • Is data assessment an assurance of data quality?
  • Applying the idea of statistical assessment to your data using R

Let's get started!

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