Business Analytics for Professionals

Preface
Contents
Editors and Contributors
Methods and Technologies for Business Analytics
Business Analytics for Managers
1 Introduction
1.1 Decision-Making Process
2 Applications
3 Summary
References
Descriptive Analytics
1 Introduction
2 Key Definitions
3 Descriptive Statistics
3.1 Frequency Distributions
3.2 Measures of Central Tendency
3.3 Measures of Variability
4 Data Visualization
5 Discrete Probability Distributions
5.1 Binomial Distribution
5.2 Poisson Distribution
6 Continuous Probability Distributions
6.1 The Normal Distribution
7 Statistical Inference: Estimation
7.1 Sampling
7.2 Sampling Distribution Means
7.3 Central Limit Theorem
7.4 Confidence Intervals of Means and Proportions
7.5 Testing Hypotheses
7.6 Testing Hypotheses About the Population Mean and Proportion
7.7 Testing Hypotheses for Differences Between Two Means and Proportions
7.8 Analysis of Variance
8 Bayesian Statistics
References
Prediction Modeling
1 Introduction
2 Linear Regression
2.1 Simple Linear Regression
2.2 Multiple Linear Regression
2.3 Performance Metrics for Regression Models
2.4 Regularization in Linear Regression
3 Logistic Regression
3.1 The Reasons for Using Logistic Function
3.2 The Logistic Function and Logistic Regression
3.3 Training a Logistic Regression Model
3.4 Model Output
3.5 Model Evaluation
4 K-Nearest Neighbor (K-NN)
4.1 K-NN Algorithm
4.2 Advantages and Disadvantages of the K-NN Algorithm
5 Naive Bayes
6 Support Vector Machine
6.1 Soft Margin Classifier
6.2 Kernel Trick for Nonlinear Data
7 Decision Trees
7.1 Mathematical Formulation of Decision Trees
7.2 Classification and Regression Trees (CART) Algorithm
7.3 The Advantages and Disadvantages of Decision Trees
8 Ensemble Learning
8.1 Bagging
8.2 Random Forest
8.3 Boosting
8.4 Gradient Boosting
9 Unsupervised Methods
9.1 Clustering
9.2 K-means Algorithm
9.3 DBSCAN
9.4 Mean Shift Algorithm
9.5 Gaussian Mixture
9.6 Hierarchical Clustering
10 Reinforcement Learning
10.1 Markov Decision Process
10.2 Value Functions and Bellman Equations
10.3 Methods for Solving RL Problems
References
Time Series Analysis
1 Introduction
2 Smoothing Methods
2.1 Moving Average Methods
2.2 Simple Exponential Smoothing
2.3 Double Exponential Smoothing
2.4 Triple Exponential Smoothing
3 Box–Jenkins Models
4 Time Series Forecasting Using Explanatory Variables
5 Multi-frequency Modeling
5.1 TBATS
5.2 The Prophet Package by Facebook
6 Intermittent Time Series
References
Neural Networks and Deep Learning
1 Introduction
2 Feedforward Neural Networks
2.1 Single Neuron Single Layer
2.2 Multi-layered Neural Networks
2.3 Dot Product Solution
2.4 Loss and Cost Functions
2.5 Gradient Descent
2.6 Backpropagation
3 Recurrent Neural Networks and Sequential Modeling
3.1 Recurrent Neural Networks
3.2 Long Short-Term Memory
3.3 Example: Power Consumption Forecast
4 Convolutional Neural Networks
4.1 Convolution Layer
4.2 Pooling Layer
4.3 Dense Layer
References
Feature Engineering
1 Introduction
2 Feature Extraction and Dimensionality Reduction
3 Feature Selection
3.1 Filter-Based Methods
3.2 Embedded Methods
3.3 Wrapper Methods
4 Global Search Methods
4.1 Genetic Algorithm (GA)
4.2 Randomized Search Algorithm (RSA)
4.3 Hill Climbing Algorithm (HCA)
References
Text Analytics
1 Introduction
2 Text Processing
2.1 Data Cleansing
2.2 Stop Words Removal
2.3 Spelling Correction
2.4 Lemmatization
2.5 Stemming
2.6 Tokenization
3 Text Representation
3.1 Discrete Text Representation
3.2 Distributed Text Representation
4 Text Classification
4.1 Dataset Preparation
4.2 Feature Engineering
4.3 Model Training
5 Topic Models
5.1 Latent Dirichlet Allocation (LDA)
5.2 Latent Semantic Indexing (LSI)
5.3 Non-negative Matrix Factorization (NMF)
6 Sentiment Analysis (Opinion Mining)
6.1 Rule-Based Sentiment Analysis
6.2 Statistical Sentiment Analysis
6.3 Deep Learning-Based Sentiment Analysis
7 Advanced Topics in Natural Language Processing (NLP)
7.1 Similarity
7.2 POS Tagging
7.3 Named Entity Recognition (NER)
7.4 Clustering
7.5 Disambiguating
7.6 Language Identification and Translation
References
Image Analysis
1 Introduction to Image Processing
2 Image Filtering
2.1 Spatial Filtering
2.2 Frequency Analysis
2.3 Image Filtering in Frequency Domain
2.4 Advanced Filtering Techniques
3 Image Segmentation
3.1 Binary Image Processing
3.2 Image Segmentation via Unsupervised Learning
4 Feature Extraction
4.1 Geometric Features
4.2 Textural Features
5 Example: Predicting the Number of Glass Sheets Using Image Analysis
References
Prescriptive Analytics: Optimization and Modeling
1 Linear Programming
1.1 Diet Problem
1.2 Police Station Problem
2 Integer Programming
2.1 Example: Production Planning
2.2 Example: Warehouse Location
2.3 Example: Revised Warehouse Location
Reference
Big Data Management and Technologies
1 Introduction
2 Big Data Technologies
3 The Big Data Idea: A Methodology of Information Acquisition
4 Big Data Management and Business Applications
5 Big Data Characteristics
6 Big Data Analytics
7 Architectural Designs
7.1 Big Data Architecture for Manufacturing Processes: An Intelligent Maintenance
7.2 Big Data Infrastructure Design and Definitions—Fundamentals
7.3 Big Data Architectural Components
8 Summary
References
Business Applications
Supply Chain Analytics
1 Introduction
2 Demand Forecasting
3 Inventory Management
4 Network Optimization
5 Route Planning and Transportation
6 Aggregate Production Planning
References
CRM and Marketing Analytics
1 Introduction
2 Revenue Management and Pricing
2.1 Problem Definition
2.2 Case Study: A White Goods and Technology Company (Company A)
2.3 Model
2.4 Solutions and Analysis
2.5 Conclusion
3 Customer Churn Analysis
3.1 Customer Relationship Management and Customer Churn
3.2 Case Study: A Telecommunication Industry
4 Social Media and Web Analytics
4.1 Problem Definition
4.2 Case Study: Customer Segmentation Based on Page View Data
4.3 Model and Application
4.4 Solution and Analysis
5 Complaint Analysis
5.1 Problem Definition
5.2 Case Study: Clothing Product-Based E-Commerce Website
5.3 Model
5.4 Analysis and Results
6 Recommendation Systems
6.1 Problem Background
6.2 Problem Definition
6.3 Case Study: Movie Recommendation System
7 Conclusion
References
Financial Analytics
1 Introduction
2 Financial Statements, Ratio Analytics and Bankruptcy Prediction
2.1 Problem Definition
2.2 Case Study: A Technology Company
2.3 Model
2.4 Fundamental and Ratio Analysis
2.5 Bankruptcy Analysis
2.6 Solution and Analysis
2.7 Fundamental Analysis and Ratio Analysis
2.8 Financial Ratio Analysis
2.9 Bankruptcy Prediction
3 Conclusion
4 Credit Risk Analysis
4.1 Problem Definition
4.2 Case Study
4.3 Data Information
4.4 Data Cleaning and Preparing
4.5 Model
5 Investment Analytics
5.1 Introduction to Portfolio Analytics
5.2 Return & Risk Calculations
5.3 Performance Metrics
5.4 Factor Models
5.5 Modern Portfolio Theory
5.6 Black and Litterman
6 Financial Hedging Analysis
6.1 Problem Definition
6.2 Case Study
References
Human Resources Analytics
1 Introduction
2 Employee Turnover (Attrition)
3 Recruitment Analytics
4 Performance Analytics
5 Training Analytics
References
Manufacturing Analytics
1 Introduction
2 Statistical Quality Control
3 Predictive Maintenance
References