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…mehr
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.
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.
Inhaltsangabe
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
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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