Description
2. Business Intelligence Concepts & Application
3. Data Warehousing
4. Data Mining
5. Data Visualization
6. Decision Trees
7. Regression
8. Artificial Neural Networks
9. Cluster Analysis
10. Association Rule Mining
11. Text Mining
12. Naïve-Bayes Analysis
13. Support Vector Machines
14. Web Mining
15. Social Network Analysis
16. Big Data
17. Data Modeling Primer
18. Statistics Primer
19. Data Science Careers and Additional Case Studies
Appendix 1: R Tutorial for Data Mining
Appendix 2: Weka Tutorial for Data Mining
Additional Resources
This book fills the need for a concise and conversational book on the growing field of Data Analytics and Big Data. Easy to read and informative, this lucid book covers everything important, with concrete examples, and invites the reader to join this field.
This book is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms. Students across a variety of academic disciplines, including business, computer science, statistics, engineering, and others attracted to the idea of discovering new insights and ideas from data will enjoy this book. The chapters in the book are organized for a typical one-semester course.
• Every chapter begins with insightful case-lets from real-world stories invigorating interest of reader
• Provides a running case study across the chapters as exercises
• Shows clear learning objectives, review questions, and objective type questions
• Covers end-to-end data processing chain, from generation of data to the consumption of data
• Covers data mining, web mining, text mining, social analytics, and more
• Provides easy tutorials for R Programming & Weka; Primers on Statistics, and Data Modeling included in the book
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