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

Book Description

As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly! Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.

Here are just a dozen of the many questions answered within these pages:
  1. What does quantitative analysis of a system really mean?
  2. What is a system?
  3. What are big data and analytics?
  4. How do you know your numbers are good?
  5. What will the future data science environment look like?
  6. How do you determine data provenance?
  7. How do you gather and process information, and then organize, store, and synthesize it?
  8. How does an organization implement data analytics?
  9. Do you really need to think like a Chief Information Officer?
  10. What is the best way to protect data?
  11. What makes a good dashboard?
  12. What is the relationship between eating ice cream and getting attacked by a shark?
The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).

Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.

Table of 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