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 presented in this book in significant detail
  • 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.

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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.

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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-Jenkin’s 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

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Produktdetaljer

ISBN
9781032415574
Publisert
2025-12-23
Utgiver
Taylor & Francis Ltd
Vekt
1130 gr
Høyde
280 mm
Bredde
210 mm
Aldersnivå
UU, 05
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
502

Biografisk notat

Susmita Bandyopadhyay, Ph.D. (Engineering), M.Tech. (Industrial Engineering and Management), MBA (Systems Management), MCA, B.Tech.-IT Diploma, currently serves as a faculty member in the Department of Business Administration, The University of Burdwan, West Bengal, India. This is her fifth book. The author has published significant number of research articles in reputed international journals and conference proceedings, and holds almost 24 years of teaching, industrial and research experience.