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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

Data mining


It is always prudent to start explaining things with a high-level definition.

Data mining can be explained simply as assembling information concerning a particular topic or belief in an understandable (and further useable) format. Keep in mind though that the information assembled is not the data itself (as with data querying) but information from the data (more on this later in this chapter).

Data mining should also not be confused with analytics, information extraction, or data analysis. Also, it can be manual or by hand, a semi-automatic, or automatic process. When working with new data, it will typically be a manual process that the data scientist will perform. Later, when working with newer versions of the same data (source), it may become automated to some level or degree.

Data mining is the probing carried out by a data scientist to find previously unknown information within the data, such as:

  • Patterns, such as groups of data records, known as clusters
  • Unusual records, known...
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