This book presents detailed theoretical study of Business Analytics including analytical methods from various aspects such as descriptive/predictive analytics, statistical distributions, multivariate statistical analysis, time series analysis, simulation, optimization theory, neural network concepts, learning/visualization techniques, etc.
Business Analytics is an emerging area which encompasses various technical understanding, essential for both Engineering and Management fields of study. This book presents detailed theoretical study of Business Analytics including analytical methods from various aspects such as descriptive and predictive analytics, statistical distributions, multivariate statistical analysis, time series analysis, simulation concept, optimization theory, neural network concepts, various learning techniques, visualization techniques, and performance metrics.
Features:
- Illustrates all the existing common analytics are presented in this book in significant details.
- Covers analytics, statistical concepts, multivariate statistical analysis, time series analysis, optimization, neural network, and decision analysis.
- Reviews various learning techniques, simulation, visualization techniques, and performance metrics.
- Puts emphasis on Python and R in context of business analytics.
- Includes figure-based, graphical descriptions along with numerical examples.
This book is aimed at senior undergraduate students in engineering and management including business analytics, and industrial engineering.
1. Introduction
2. Discrete and Continuous Probability Distributions
3. Introduction to R Software
4. Introduction to Python and XLSTAT
5. Basic Statistics
6. Sampling and ANOVA
7. Multivariate Probability Distribution
8. Regression
9. Path Model and Discriminant Analysis
10. PCA and Factor Analysis
11. Structural Equation Modelling
12. Moving Average and Box-Jenkins Method
13. Time Series Models Part II
14. Mathematical Optimization Techniques
15. Dynamic Programming, Markov Analysis, and Information Theory
16. Nonlinear Optimization
17. Nature-Based Techniques and Multicriteria Decision Analysis Techniques
18. Artificial Neural Network
19. Learning Rules
20. Descriptive Analytics
21. Decision Tree
22. Other Learning Techniques
23. Simulation
24. Continuous Simulation
25. Simulation Optimization
26. Data Mining and Partitioning
27. Visualization Techniques and Dimension Reduction Techniques
28. Performance Metrics
29. Additional Techniques for Diagnostic Analytics and Prescriptive
Analytics
30. Implementation of Different Methodologies with R
Susmita Bandyopadhyay, Ph.D. (Engineering), M.Tech. (Industrial Engineering and Management), MBA (Systems Management), MCA, B.Tech.-IT Diploma is currently serving as a Faculty member in Department of Business Administration, The University of Burdwan, West Bengal, India. This book is her fifth book. The author has published significant number of research articles in different referred International well-rated Journals and Referred International Conference Proceedings and holds almost 24 years of teaching, industrial and research experience.
