An Introduction to Data Mining Daniel T. Larose is a study book that provides insight into data mining, predictive analytics, and statistical analysis. The book focuses on applying data science methods to discover valuable knowledge in large datasets from various sectors such as business and healthcare.
This reference work covers current techniques and provides practical guidance for leveraging existing databases to increase profit and market share. With newly added chapters on data modeling, filling in missing data, and multivariate statistics, this second edition remains an essential resource for data science professionals.
Key features
- Comprehensive coverage of big data, predictive analytics, and statistical methods
- New chapters on Multivariate Statistics, Preparation for Modeling, and Imputation of Missing Data
- Extensive focus on R as a programming language for data analysis
- 280 practice questions after each chapter
- Available with a companion website for instructors
Product specifications
- Author: Daniel T. Larose (Central Connecticut State University)
- Publisher: John Wiley & Sons Inc
- ISBN: 9780470908747
- Pages: 336
- Subject: Data mining, Predictive analytics, Statistical analysis
About the author
Daniel T. Larose is a professor and director of data mining programs at Central Connecticut State University. He has experience as a consultant for leading companies such as Microsoft and Deloitte. This is his fourth publication with Wiley.
Chantal D. Larose is an assistant professor of statistics and data science at Eastern Connecticut State University. She is co-author of several books on data science and predictive analytics methods and contributes to the development of data science programs.