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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

Frequent patterning


To gain an understanding of statistical patterning, let us begin with thinking about what happens when an urban area is threatened by severe weather and potentially hazardous traveling—all the local stores sell out of bread, milk, and eggs!

Patterning (which is a subfield of data mining) is the process of looking through data in an effort to identify previously unknown but potentially useful patterns consisting of frequently co-occurring events (such as the stormy weather event triggering the sale of bread, milk, and eggs) or objects (such as the products bread, milk, and eggs being typically purchased together or bundled together in the same shopping cart).

Pattern mining is the process that consists of using or developing custom pattern mining logic. This logic might be applied to various types of data sources (such as transaction and sequence databases, streams, strings, spatial data, graphs, and so on) in an effort to look for various types of patterns.

At a higher level...

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