Big Data Analytics Methods

Analytics Techniques in Data Mining, Deep Learning and Natural Language Processing
2. Auflage 2019
ISBN: 978-1-5474-1795-7
Verlag: De Gruyter

Buch, Englisch, 238 Seiten, Format (B × H): 170 mm x 240 mm, Gewicht: 438 g

Reihe: Business & economics

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.

Zielgruppe

Data analysts, Data engineers, Data scientists, Business intellig

Autoren/Hrsg.

Ghavami, Peter

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data

Inhaltsverzeichnis

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

Über Autor(innen)

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