Offers a comprehensive introductory approach to business analytics that includes an emphasis on big data handling, applications in different verticals and case studies
Highlights big data handling, applications of analytics in different verticals, and real life case studies
Includes exercises for each chapter and downloadable use cases for students and professionals to practice and test the analytics tools

Autorentext

Bhimasankaram Pochiraju obtained his Ph.D. in Statistics from the Indian Statistical Institute. He was the Clinical Professor of Statistics, Executive Director of Applied Statistics and Computing Lab and Faculty Director, Certificate Programme in Business Analytics at the Indian School of Business. He was formerly Professor of Statistics and Head, SQC & OR Division at the Indian Statistical Institute. He co-authored a text book and a research monograph in Linear Algebra. His areas of research interest include Analytics, Causal Inference and Linear Algebra.

Sridhar Seshadri obtained his PhD at the University of California, Berkeley after graduating from the Indian Institute of Technology, Madras, India and the Indian Institute of Management, Ahmedabad, India. He is currently Professor and Area Leader of IS, Operations Management and Supply Chain Management areas at the Geis College of Business. He has also been a faculty member at The Indian School of Business, The University of Texas at Austin, New York University and the Administrative Staff College of India. During his teaching career, he was awarded the Stern School of Business Teaching Excellence Award (1998) and recognized as the Stern School of Business Undergraduate Teacher of the Year in 1997. His current research includes Analytics, Pricing and Revenue Optimization and Risk Management in supply chains.

Inhalt

Chapter 1. Introduction.- Chapter 2. Data Collection.- Chapter 3. Data Management Relational Database Systems (RDBMS).- Chapter 4. Big Data Management.- Chapter 5. Data Visualization.- Chapter 6. Statistical Methods-Basic inferences.- Chapter 7. Statistical Methods-Regression.- Chapter 8. Advanced Regression Analysis.- Chapter 9. Text Analytics.- Chapter 10. Simulation.- Chapter 11. Introduction to Optimization.- Chapter 12. Forecasting Analytics.- Chapter 13. Count Data Regression.- Chapter 14. Survival Analysis.- Chapter 15. Machine Learning (Unsupervised).- Chapter 16. Machine Learning (Supervised).- Chapter 17. Deep Learning.- Chapter 18. Retail Analytics.- Chapter 19. Marketing Analytics.- Chapter 20. Financial Analytics.- Chapter 21. Social Media and Web Analytics.- Chapter 22. Healthcare Analytics.- Chapter 23. Pricing Analytics.- Chapter 24. Supply Chain Analytics.- Chapter 25. Case study: Ideal Insurance.- Chapter 26. Case study: AAA Airline.- Chapter 27. Case study: Informedia Solutions.- Chapter 28. Appendix 1: Introduction to R.- Chapter 29. Appendix 2: Introduction to Python.- Chapter 30. Appendix 3: Probability and Statistics.-