BUSINESS ANALYTICS: THEORY AND PRACTICE
CRC PRESS; ISBN NUMBER: 9781032415574
Editor: Dr. S. Bandyopadhyay

Chapters are required for the above book on the following topics.

Introduction to Different Analytics – Descriptive Analytics; Predictive Analytics; Prescriptive Analytics; Probability – Discrete Probability Distribution; Continuous Probability Distribution; Introduction to R Software; Introduction to Python.

Basic Statistics – Central Tendency; Dispersions; Testing and Hypothesis; Statistical Inference; Sampling, ANOVA.

Multivariate Probability Distribution; Regression – Linear Regression; Multiple and Multivariate Regression; Non-Linear Regression; Logistic Regression; Path Model; Discriminant Analysis; Principal Component Analysis; Factor Analysis; Cluster Analysis; Structural Equation Modeling.

Time Series Modeling; Moving Averages; Box-Jenkins Method – ARMA, ARIMA, Others; State-Space Representation; Kalman Filter; Periodogram; Autocovariance Functions; White Noise; Partial Correlation; Levinson-Durbin Recursion.

Mathematical Optimization Techniques – Linear Programming; Overview of Advanced Linear Programming; Integer Programming; Dynamic Programming; Markov Analysis; Information Theory; Non-Linear Optimization.

Nature Based Techniques – Genetic Algorithm; Particle Swarm Optimization; Ant Colony Optimization; Artificial Immune Algorithm; Differential Evolution; Simulated Annealing; Tabu Search; Honey Bee Mating Algorithm; Frog Leaping Algorithm; Bacteria Foraging Algorithm; Firefly Algorithm; Cuckoo Search.

Neural Network - Single- and Multi-Input Neuron; Layer of Neuron; Perceptron Network; Hamming Network; Hopfield Network; Backpropagation; Learning Rules – Supervised Hebbian Learning; Widraw-Hoff Learning; Associative Learning.

Descriptive Analytics - Association Rule; Sequence Rule; Segmentation and Hierarchical Clustering; K-Means Clustering; Decision Tree – Decision Tree Rules; Decision Tree by Expected Monetary Value; Decision Induction Algorithms – ID3, C4.5, CART; Other Learning Techniques – Supervised Learning; Unsupervised Learning; Reinforcement Learning; Hybrid Learning; Multi-Task Learning; Ensemble Learning; Transfer Learning; Online Learning; Inductive, Deductive and Transductive Learning.

Discrete Event Simulation – Monte Carlo Simulation; Random Number Generator; Computer Simulation; Manufacturing Simulation; Supply Chain Simulation; Continuous Simulation; Simulation Optimization; Application of Simulation; Simulation Software.

Data Mining; Partitioning; Visualization Techniques; Dimension Reduction Techniques; Performance Metrics – Performance Metrics Based on Naïve Rule; Classification Matrix; Performance Metrics in Manufacturing; Performance Metrics in Supply Chain; Implementation of Different Methodologies with R and Python.

Send both the abstracts and full chapters to email id: bandyopadhyaysusmita2010@gmail.com with subject line, "Abstract for Business Analytics" (for abstract) or "Full Chapters for Business Analytics".
[The chapters will be checked for Plagiarism issue]