Doctor I Do : (Chicago's Billionaire Doctors 2)

This book explains concepts and techniques for business analytics and demonstrate them on real life applications for managers and practitioners. It illustrates how machine learning and optimization techniques can be used to implement intelligent business automation systems. The book examines business problems concerning supply chain, marketing & CRM, financial, manufacturing and human resources functions and supplies solutions in Python. 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