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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
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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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Toc

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

Mining versus querying

Data querying is the process of asking specific, structured questions of data in search of a specific answer, while data mining is the process of sifting through data to identify patterns and relationships using statistical algorithms.

The following matrix may help the data developers in gaining an understanding of the differences between data querying and data mining:

Example

Data querying or mining

What was the total number of technical books sold last month worldwide?

Data querying

What factors affected the type of technical books sold worldwide last month?

Data mining

How many different technologies' technical books were sold last quarter?

Data querying

Which technologies were purchased as part of a set?

Data mining

Does a technology tend to be purchased in hardcopy or electronic version?

Data mining

Which...

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