Discovering Knowledge in Data
An Introduction to Data Mining
Wiley · Print & ebook · July 8, 2014
The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence.
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- Who it’s for
- Explore data mining methods, including preparing data, imputing missing values, and multivariate statistical analysis.
- Themes
- Data mining, Predictive analytics, Statistical analysis
- Genres
- Data Mining, Machine Learning, Business Intelligence
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Start with this bookAbout this book
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.
Continue reading
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
Preparing Data
Explore data mining methods, including preparing data, imputing missing values, and multivariate statistical analysis.
Come here for
- Using company databases to increase profits and market share
- Coverage of predictive analytics and statistical analysis
Expect
- R statistical programming language coverage
- End-of-chapter exercises and a companion website for university instructors
Where it sits
| Data Mining | 65% |
|---|---|
| Machine Learning | 38% |
| Business Intelligence | 22% |
Percentages are BookFrontier’s reading of the book.
Publisher categories
- Data Mining
- Data Warehousing
- Authors
- Daniel T. Larose, Chantal D. Larose
- Publisher
- Wiley
- Published
- July 8, 2014
- Format
- Print & ebook
- Theme
- Data mining · Predictive analytics · Statistical analysis
- Reading lane
- Data Mining