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BIG DATA, DATA MINING, AND MACHINE LEARNING: VALUE CREATION FOR BUSINESS LEADERS AND PRACTITIONERS

	Big Data, Data Mining, And Machine Learning: Value Creation For Business Leaders And Practitioners by Jared Dean
	Price : Rs 699.00
	Your Price : Rs 594.15

With the exponential growth of data comes an ever-increasing need to process and analyze the so-called Big Data. High performance computing architectures have been devised to address the needs for handling Big Data processing not only from a transaction processing viewpoint but also from an analytics perspective. This book provides a comprehensive view on the recent trend toward high performance computing architectures especially as it relates to analytics and data mining.Topics that are covered include: big data (and its characteristics), high performance computing for analytics, massively parallel processing (MPP) databases, algorithms for big data, in-memory databases, implementation of machine learning algorithms for big data platforms, and analytics environments. However none gives a historical and comprehensive view of all these separate topics in a single document. Through the understanding of these topics corporations can create an ideal analytic environment that is better suited to the challenges of today`s analytics demands.

Forward
Preface
Acknowledgments
Introduction

Big Data Timeline
Why This Topic Is Relevant Now
Is Big Data a Fad?
Where Using Big Data Makes a Big Difference
Part One The Computing Environment

Chapter 1 Hardware
Storage (Disk)
Central Processing Unit
Memory
Network

Chapter 2 Distributed Systems
Database Computing
File System Computing
Considerations

Chapter 3 Analytical Tools
Weka
Java and JVM Languages
R
Python
SAS
Part Two Turning Data into Business Value

Chapter 4 Predictive Modeling
A Methodology for Building Models
SEMMA
Binary Classification
Multilevel Classification
Interval Prediction
Assessment of Predictive Models

Chapter 5 Common Predictive Modeling Techniques
RFM
Regression
Generalized Linear Models
Neural Networks
Decision and Regression Trees
Support Vector Machines
Bayesian Methods Network Classification
Ensemble Methods

Chapter 6 Segmentation
Cluster Analysis
Distance Measures (Metrics)
Evaluating Clustering
Number of Clusters
K]means Algorithm
Hierarchical Clustering
Profiling Clusters

Chapter 7 Incremental Response Modeling
Building the Response Model
Measuring the Incremental Response

Chapter 8 Time Series Data Mining
Reducing Dimensionality
Detecting Patterns
Time Series Data Mining in Action: Nike+ FuelBand

Chapter 9 Recommendation Systems
What Are Recommendation Systems?
Where Are They Used?
How Do They Work?
Assessing Recommendation Quality
Recommendations in Action: SAS Library
Chapter 10 Text Analytics
Information Retrieval
Content Categorization
Text Mining
Text Analytics in Action: Let`s Play Jeopardy!
Part Three Success Stories of Putting It All Together

Chapter 11 Case Study of a Large U.S.]Based Financial Services Company
Traditional Marketing Campaign Process
High]Performance Marketing Solution
Value Proposition for Change

Chapter 12 Case Study of a Major Health Care Provider
CAHPS
HEDIS
HOS
IRE

Chapter 13 Case Study of a Technology Manufacturer
Finding Defective Devices
How They Reduced Cost

Chapter 14 Case Study of Online Brand Management

Chapter 15 Case Study of Mobile Application Recommendations

Chapter 16 Case Study of a High]Tech Product Manufacturer
Handling the Missing Data
Application beyond Manufacturing

Chapter 17 Looking to the Future
Reproducible Research
Privacy with Public Data Sets
The Internet of Things
Software Development in the Future
Future Development of Algorithms
In Conclusion
About the Author
Appendix
References
IndexISBN - 9788126550906