Computational Business Analytics

Computational Business Analytics

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Features

  • Incorporates descriptive, predictive, and prescriptive analytics processes
  • Presents case studies that illustrate statistics-, AI-, and ML-based analytics systems in numerous domains, including loan processing, credit risk assessment, life status estimation, fraud detection, and customer sentiment analysis
  • Provides a refresher on the basics of analytics, along with the necessary mathematical and statistical preliminaries
  • Covers computational technologies for information extraction and text classification in text analyses
  • Contains an abundance of references to the theory of statistics and probability, AI, ML, control theory, and other relevant areas

Summary

Learn How to Properly Use the Latest Analytics Approaches in Your Organization

Computational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies.

The book first covers core descriptive and inferential statistics for analytics. The author then enhances numerical statistical techniques with symbolic artificial intelligence (AI) and machine learning (ML) techniques for richer predictive and prescriptive analytics. With a special emphasis on methods that handle time and textual data, the text:

  • Enriches principal component and factor analyses with subspace methods, such as latent semantic analyses
  • Combines regression analyses with probabilistic graphical modeling, such as Bayesian networks
  • Extends autoregression and survival analysis techniques with the Kalman filter, hidden Markov models, and dynamic Bayesian networks
  • Embeds decision trees within influence diagrams
  • Augments nearest-neighbor and k-means clustering techniques with support vector machines and neural networks

These approaches are not replacements of traditional statistics-based analytics; rather, in most cases, a generalized technique can be reduced to the underlying traditional base technique under very restrictive conditions. The book shows how these enriched techniques offer efficient solutions in areas, including customer segmentation, churn prediction, credit risk assessment, fraud detection, and advertising campaigns.

Table of Contents

Analytics Background and Architectures
ANALYTICS DEFINED
ANALYTICS MODELING
ANALYTICS PROCESSES
ANALYTICS AND DATA FUSION

Mathematical and Statistical Preliminaries
STATISTICS AND PROBABILITY THEORY
LINEAR ALGEBRA FUNDAMENTALS
MATHEMATICAL LOGIC
GRAPHS AND TREES
MEASURES OF PERFORMANCE
ALGORITHMIC COMPLEXITY

Statistics for Descriptive Analytics
PROBABILITY DISTRIBUTIONS
DISCRETE PROBABILITY DISTRIBUTIONS
CONTINUOUS PROBABILITY DISTRIBUTIONS
GOODNESS-OF-FIT TEST

Bayesian Probability and Inference
BAYESIAN INFERENCE
PRIOR PROBABILITIES

Inferential Statistics and Predictive Analytics
CHISQUARE
TEST OF INDEPENDENCE
REGRESSION ANALYSES
BAYESIAN LINEAR REGRESSION
PRINCIPAL COMPONENT AND FACTOR ANALYSES
SURVIVAL ANALYSIS
AUTOREGRESSION MODELS

Artificial Intelligence for Symbolic Analytics
ANALYTICS AND UNCERTAINTIES
NEO-LOGICIST APPROACH
NEO-PROBABILIST
NEO-CALCULIST APPROACH
NEO-GRANULARIST

Probabilistic Graphical Modeling
NAIVE BAYESIAN CLASSIFIER (NBC)
KDEPENDENCE NAIVE BAYESIAN CLASSIFIER (KNBC)
BAYESIAN BELIEF NETWORKS

Decision Support and Prescriptive Analytics
EXPECTED UTILITY THEORY AND DECISION TREES
INFLUENCE DIAGRAMS FOR DECISION SUPPORT
SYMBOLIC ARGUMENTATION FOR DECISION SUPPORT

Time Series Modeling and Forecasting
PROBLEM MODELING
KALMAN FILTER (KF)
MARKOV MODELS
DYNAMIC BAYESIAN NETWORKS (DBNS)

Monte Carlo Simulation
MONTE CARLO APPROXIMATION
GIBBS SAMPLING
METROPOLIS-HASTINGS ALGORITHM
PARTICLE FILTER (PF)

Cluster Analysis and Segmentation
HIERARCHICAL CLUSTERING
K-MEANS CLUSTERING
K-NEAREST NEIGHBORS
SUPPORT VECTOR MACHINES
NEURAL NETWORKS

Machine Learning for Analytics Models
DECISION TREES
LEARNING NAIVE BAYESIAN CLASSIFIERS
LEARNING OF KNBC
BAYESIAN BELIEF NETWORKS
INDUCTIVE LOGIC PROGRAMMING

Unstructured Data and Text Analytics
INFORMATION STRUCTURING AND EXTRACTION
BRIEF INTRODUCTION TO NLP
TEXT CLASSIFICATION AND TOPIC EXTRACTION

Semantic Web
RESOURCE DESCRIPTION FRAMEWORK (RDF)
DESCRIPTION LOGICS

Analytics Tools
INTELLIGENT DECISION AIDING SYSTEM (IDAS)
ENVIRONMENT FOR FIFTH GENERATION APPLICATIONS (E5)
ANALYSIS OF TEXT (ATEXT)
R AND MATLAB
SAS AND WEKA

Analytics Case Studies
RISK ASSESSMENT MODEL I3
RISK ASSESSMENT IN INDIVIDUAL LENDING USING IDAS
RISK ASSESSMENT IN COMMERCIAL LENDING USING E5 AND IDAS
FRAUD DETECTION
SENTIMENT ANALYSIS USING ATEXT

Appendix A: Usage of Symbols
Appendix B: Examples and Sample Data
Appendix C: MATLAB and R Code Examples

Index

Further Reading appears at the end of each chapter.

Author Bio(s)

Subrata Das is the founder and president of Machine Analytics and also serves as a consulting scientist to other companies. He has many years of experience in industrial, government, and academic research and development. He earned his Ph.D. in computer science and master's in mathematics.

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