Navigating Big Data Analytics : Strategies for the Quality Systems Analyst.

Cover
Title page
CIP data
Table of Contents
List of Figures and Tables
Introduction
Chapter 1_An Introduction to
Deep Learning
When to Use This Technology
Defining the Problem
A Note About Technology
Navigating Big Data
Structure of This Book
Chapter 2_Potential Data Problems and How They Arise
Rapid Data Collection Concerns
Format Concerns
Off-line Testing Data
Historical Data
Expert Opinion
HACCP Applications
The Problem of Modified Data
Prevalence of Data Problems
Data Problem Impact
Scoping the Issues Instead of All the Problems
Chapter 3_Designed Experiments Versus Big Data Analysis
Statistically Designed Experiments
Chances of Observing Extreme Settings
Big Data Limitations
Costs of Experimentation
Time Issues
Coverage of Typical Conditions
Measurement Error
Expert Opinion
Chapter 4_The Challenge of
The Big Data Approach
Evaluating the True Impact
Types of Missing Values
Big Data Processes
More Data as a Solution
The Importance of Identifying Missing Data
Chapter 5_The Impact of Poor Randomization
Randomization and Bias
Practical Application
Other Challenges
Stability
A Resampling Approach
The Risk of Losing Data
Chapter 6_Expert Opinion
The Essential Difference of the Bayesian Approach
Lessons from Extreme Priors
Model Selection Impact
Causal Analysis
Chapter 7_Censored Data
A Big Data Approach
Shortcomings of Big Data and Censoring
Correcting the Bias Problem
Chapter 8_Other Potential Problems
The Treatment of Outliers
Missing Data Issues
Decision Error
Model Complexity
Accumulation of Knowledge
Some General Observations
Conclusion
End Notes
Glossary
Index
About the Author.