Big Data Analytics Methods : : Analytics Techniques in Data Mining, Deep Learning and Natural Language Processing / / Peter Ghavami.

• Superior document: Title is part of eBook package: De Gruyter DG Ebook Package English 2020
• VerfasserIn: Ghavami, Peter,
• Place / Publishing House: Berlin ;, Boston : : De Gruyter, , [2019]; ©2020
• Year of Publication: 2019
• Edition: 2nd Edition
• Language: English
• Physical Description: 1 online resource (XVI, 238 p.)
• Other title: Frontmatter --; Acknowledgments --; About the Author --; Contents --; Introduction --; Part I: Big Data Analytics --; Chapter 1. Data Analytics Overview --; Chapter 2. Basic Data Analysis --; Chapter 3. Data Analytics Process --; Part II: Advanced Analytics Methods --; Chapter 4. Natural Language Processing --; Chapter 5. Quantitative Analysis—Prediction and Prognostics --; Chapter 6. Advanced Analytics and Predictive Modeling --; Chapter 7. Ensemble of Models: Data Analytics Prediction Framework --; Chapter 8. Machine Learning, Deep Learning—Artificial Neural Networks --; Chapter 9. Model Accuracy and Optimization --; Part III: Case Study—Prediction and Advanced Analytics in Practice --; Chapter 10. Ensemble of Models—Medical Prediction Case Study: Data Types, Data Requirements and Data Pre-Processing --; Appendices --; References --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9781547401567; 9783110696288; 9783110696271; 9783110610765; 9783110664232; 9783110610154; 9783110606096
• DOI: 10.1515/9781547401567
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Peter Ghavami.

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.