Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery, Solutions Manual

Walter W. Piegorsch

ISBN 10: 111903065X / ISBN 13: 9781119030652

Published by John Wiley & Sons Inc, 2015

About this Item

Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery. Num Pages: 232 pages. BIC Classification: UNC; UNF. Category: (P) Professional & Vocational. Dimension: 244 x 170 x 0. . . 2015. 1st Edition. Paperback. . . . . Seller Inventory # V9781119030652

Bibliographic Details

Title: Statistical Data Analytics: Foundations for ...

Publisher: John Wiley & Sons Inc

Publication Date: 2015

Binding: Soft cover

Condition: New

Edition: 1st Edition

About this title

Synopsis:

Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery 
A comprehensive introduction to statistical methods for data mining and knowledge discovery.
Extensive solutions using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.

From the Back Cover:

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, basics of data manipulation and visualization, and the central components of standard statistical inferences.  The majority of the text extends beyond these introductory topics, however, to supervised learning in linear regression, generalized linear models, and classification analytics.  Finally, unsupervised learning via dimension reduction, cluster analysis, and market basket analysis are introduced.

Extensive examples using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.

Statistical Data Analytics:

• Focuses on methods critically used in data mining and statistical informatics. Coherently describes the methods at an introductory level, with extensions to selected intermediate and advanced techniques.

• Provides informative, technical details for the highlighted methods.

• Employs the open-source R language as the computational vehicle – along with its burgeoning collection of online packages – to illustrate many of the analyses contained in the book.

• Concludes each chapter with a range of interesting and challenging homework exercises using actual data from a variety of informatic application areas.

This book will appeal as a classroom or training text to intermediate and advanced undergraduates, and to beginning graduate students, with sufficient background in calculus and matrix algebra. It will also serve as a source-book on the foundations of statistical informatics and data analytics to practitioners who regularly apply statistical learning to their modern data.