Quantitative Analysis for System Applications: Data Science and Analytics Tools and Techniques

474 Pages·2018·12.11 MB·English

by  Daniel A. McGrath Ph.D. [Daniel A. McGrath Ph.D.]

Contents
1. Introduction
2. PART I: Foundation
3. CHAPTER 1: What Does Quantitative Analysis
of a System Really Mean?
1. What is a system?
2. What are data?
3. Fundamental system characteristics: open and
closed systems
4. Other system characteristics
5. How to analyze real systems
6. How to analyze IT systems
4. CHAPTER 2: Analytic Approaches for Big Data
1. Analytics and big data defined
2. Data-centric approaches
3. 10 T’s for Data Mining
4. Case Study: Mining of data.gov holdings for
US international air travel
5. CHAPTER 3: Foundations of Data Management
1. Virtual systems fundamentals
2. Data audits – A rapid prototyping approach
for small operations
6. CHAPTER 4: Data Quality – How Do You
Know Your Numbers Are Good?
1. Data quality dimensions
2. The science part of data science
3. How do I know my results are good?
4. Measuring performance
5. Checklists
6. Daily management
7. PART II: Quantitative Tools and Techniques
8. CHAPTER 5: Characteristics of Data from
Systems
1. Types of data
2. First principles
3. Generic outputs
4. Behavior over time
9. CHAPTER 6: Fundamental Quantitative Analysis
for Systems
1. Exploratory data analysis
2. Descriptive statistics
3. Inferential statistics
4. Ishikawa’s seven tools
5. Big data tools and approaches
10. CHAPTER 7: Models and Prediction
1. Analog and digital simulation approaches
2. Building the model
3. What will the future work environment look
like?
4. System dynamics models
5. Business models
11. PART III: Looking to the Future
12. CHAPTER 8: Human Aspects
1. Individuals
2. Health and safety
3. Elicitation
4. Decision making under risk
13. CHAPTER 9: Newest Developments
1. What’s next in technology?
2. What will the future bring?
3. Internet of Everything
4. Models as Artificial Intelligence
5. Organizational challenges
6. Data Analytics Support Office (DASO)
14. APPENDIX A: Detailed Description of the 10
T’s for Data Mining
15. APPENDIX B: Million Row Data Audit
(MRDA) Process
1. Preparatory phase
2. Review phase
3. Drill down analysis phase
4. Visualization phase
5. Reporting phase
16. APPENDIX C: Analysis of Rainfall Data for
Amarillo, Texas (1880-2017)
1. Observations for annual data
2. Observations for monthly data
17. APPENDIX D: Use of Simulation Models for
Evaluating Traffic Flow Options at Special
Events Using Arena
18. APPENDIX E: Application of DOE to
Simulation Model Final Stretch
1. PowerPoint storyboard
2. DOE output
19. Acknowledgements
20. Bibliography
21. Index
Landmarks
1. Cover

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First Edition
First Printing 2018
Copyright © 2018 Daniel A. McGrath, Ph.D.
ISBN, print ed. 9781634624237
ISBN, PDF ed. 9781634624268
Library of Congress Control Number: 2018954668