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Statistical data analytics : foundations for data mining, informatics, and knowledge discovery / Walter W. Piegorsch.

By: Material type: TextTextPublication details: Chichester : John Wiley, ©2015.Description: xi, 470 p. : illustrations ; 25 cmISBN:
  • 9781118619650
Subject(s): DDC classification:
  • 000SB:006.312 23 P613
Contents:
1. Data analytics and data mining -- 2. Basic probability and statistical distributions -- 3. Data manipulation -- 4. Data visualization and statistical graphics -- 5. Statistical inference -- 6. Techniques for supervised learning: simple linear regression -- 7. Techniques for supervised learning: multiple linear regression -- 8. Supervised learning: generalized linear models -- 9. Supervised learning: classification -- 10. Techniques for unsupervised learning: dimension reduction -- 11. Techniques for unsupervised learning: clustering and association -- A. Matrix manipulation -- B. Brief introduction to R.
Summary: A comprehensive introduction to statistical methods for data mining and knowledge discovery.Applications of data mining and 'big data' increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software. This book presents a coherent, technical introduction to modern statistical learning and analytics, starting from the core foundations of statistics and probability. It includes an overview of probability and statistical distributions, basic.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 000SB:006.312 P613 (Browse shelf(Opens below)) Available 137128
Total holds: 0

Includes bibliographical references and index.

1. Data analytics and data mining --
2. Basic probability and statistical distributions --
3. Data manipulation --
4. Data visualization and statistical graphics --
5. Statistical inference --
6. Techniques for supervised learning: simple linear regression --
7. Techniques for supervised learning: multiple linear regression --
8. Supervised learning: generalized linear models --
9. Supervised learning: classification --
10. Techniques for unsupervised learning: dimension reduction --
11. Techniques for unsupervised learning: clustering and association --
A. Matrix manipulation --
B. Brief introduction to R.

A comprehensive introduction to statistical methods for data mining and knowledge discovery.Applications of data mining and 'big data' increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software. This book presents a coherent, technical introduction to modern statistical learning and analytics, starting from the core foundations of statistics and probability. It includes an overview of probability and statistical distributions, basic.

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