Big Data Analytics Methods

Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics.

The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.

Auteur

Peter Ghavami, Senior Vice President, Head of Wholesale Data Science & Analytics at Bank of America, USA

Contenu

Introduction

PART I: Big Data Analytics

Chapter 1. Data Analytics Overview

Chapter 2. Basic Data Analysis

Chapter 3. Data Visualization Tools

PART II: Advanced Analytics Methods

Chapter 4. Natural Language Processing

Chapter 5. Quantitative Analysis - Prediction and Prognostics

Chapter 6. Advanced Analytics & Predictive Modeling

Chapter 7. Ensemble of Models

Chapter 8. Machine Learning, Deep Learning - Artificial Neural Networks

Chapter 9. Model Accuracy & Optimization

PART III: Case Study - Prediction & Advanced Analytics in Practice

Chapter 10: Ensemble of Models - Medical Prediction Case Study

Appendix A: Prognostics Methods

Appendix B: A Neural Network Example

Appendix C: Back Propagation Algorithm Derivation

Appendix D: NeuroSolutions Software Description

Appendix E: The Oracle Program

References