Checkout/View Shopping Cart >>
×


 x 

Shopping cart

Shop By

Categories

Home » Computing » Databases » Data mining » Discovering Knowledge in Data
Daniel T. Larose - Discovering Knowledge in Data - 9780470908747 - V9780470908747
Stock image for illustration purposes only - book cover, edition or condition may vary.

Discovering Knowledge in Data

€ 124.15
FREE Delivery in Ireland

Usually ships in 7 to 11 working days

Description for Discovering Knowledge in Data Hardcover. The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. Series: Wiley Series on Methods and Applications in Data Mining. Num Pages: 336 pages, illustrations. BIC Classification: UNF. Category: (P) Professional & Vocational. Dimension: 236 x 164 x 25. Weight in Grams: 632.

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before.

This book provides the tools needed...
Read more

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before.

This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining.
  • The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis.
  • Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization
  • Offers extensive coverage of the R statistical programming language
  • Contains 280 end-of-chapter exercises
  • Includes a companion website for university instructors who adopt the book

Product Details

Format
Hardback
Publication date
2014
Publisher
John Wiley and Sons Ltd United Kingdom
Number of pages
250
Condition
New
Series
Wiley Series on Methods and Applications in Data Mining
Number of Pages
336
Place of Publication
New York, United States
ISBN
9780470908747
SKU
V9780470908747
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-50

About Daniel T. Larose
Daniel T. Larose earned his PhD in Statistics at the University of Connecticut. He is Professor of Mathematical Sciences and Director of the Data Mining programs at Central Connecticut State University. His consulting clients have included Microsoft, Forbes Magazine, the CIT Group, KPMG International, Computer Associates, and Deloitte, Inc. This is Larose’s fourth book for Wiley. Chantal D. Larose is...
Read more
Daniel T. Larose earned his PhD in Statistics at the University of Connecticut. He is Professor of Mathematical Sciences and Director of the Data Mining programs at Central Connecticut State University.  His consulting clients have included Microsoft, Forbes Magazine, the CIT Group, KPMG International, Computer Associates, and Deloitte, Inc. This is Larose’s fourth book for Wiley. Chantal D. Larose is an Assistant Professor of Statistics & Data Science at Eastern Connecticut State University (ECSU).  She has co-authored three books on data science and predictive analytics.  She helped develop data science programs at ECSU and at SUNY New Paltz.  She received her PhD in Statistics from the University of Connecticut, Storrs in 2015 (dissertation title: Model-based Clustering of Incomplete Data).

Reviews for Discovering Knowledge in Data

Goodreads reviews for Discovering Knowledge in Data