Essentials of Business Analytics An Introduction to the Methodology and its Applications /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Pochiraju, Bhimasankaram (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Seshadri, Sridhar (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: International Series in Operations Research & Management Science, 264
• Θέματα: Operations research.; Decision making.; Statistics .; Big data.; Operations Research/Decision Theory.; Statistics for Business, Management, Economics, Finance, Insurance.; Big Data/Analytics.

 This comprehensive edited volume is the first of its kind, designed to serve as a textbook for long-duration business analytics programs. It can also be used as a guide to the field by practitioners. The book has contributions from experts in top universities and industry. The editors have taken extreme care to ensure continuity across the chapters. The material is organized into three parts: A) Tools, B) Models and C) Applications. In Part A, the tools used by business analysts are described in detail. In Part B, these tools are applied to construct models used to solve business problems. Part C contains detailed applications in various functional areas of business and several case studies. Supporting material can be found in the appendices that develop the pre-requisites for the main text. Every chapter has a business orientation. Typically, each chapter begins with the description of business problems that are transformed into data questions; and methodology is developed to solve these questions. Data analysis is conducted using widely used software, the output and results are clearly explained at each stage of development. These are finally transformed into a business solution. The companion website provides examples, data sets and sample code for each chapter. 

 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.-.