Walt DeGrange is the Director of Analytics Capabilities for CANA. He has extensive experience implementing analytical models in both the Department of Defense and commercial areas. Prior to CANA, Walt served 21 years in the US Navy as a Supply Corps Officer. He was the Director of Operations Research at several military commands as well as a Military Assistant Professor on faculty at the Naval Postgraduate School in the Operations Research Department. Walt is also very active in analytics education. He is an adjunct faculty member at the University of Arkansas with both the Master of Science Operations Management and Engineering Management programs. He is an MBA Executive Advisor at the NC State University Poole School of Management. Walt serves as the Military Operations Research Society (MORS) Course Director for the Critical Skills for Analytics Professionals Certificate Program, and he teaches the Analytics Capability Evaluation (ACE) Coaching Course for INFORMS. Lucia Darrow is a data visualization expert with experience implementing analytical models in a variety of environments, including healthcare, manufacturing, defense, and finance. An industrial engineer by training, Lucia also has extensive experience with Lean manufacturing and the modeling of complex systems. In a professional role as a learning and development lead and as a community events organizer, she has facilitated many successful networking events, tutorials, and classes in a wide range of analytics topics. Lucia is actively involved in the analytics community through RLadies and recently co-organized the first Vancouver, BC Datajam. She holds a Master of Science in Industrial Engineering from Oregon State University and a Bachelor of Science in Mathematics from Dickinson College. Lucia is currently a Content Marketing Data Analyst with RepRisk AG, an environmental, social, and corporate governance (ESG) data science company based in Zurich, Switzerland.
Inhaltsangabe
1. Introduction. 1.1. Defining an Analytics Professional. 1.2. Design of the Book. 1.3. Purpose of the Book. 1.4. Kidney Paired Donation Case Study. 2. Defining Analytics. 2.1. Real World Impact of Analytics. 2.2. Types of Analytics. 2.3. The Analytics Process. 2.4. Selecting an Analytical Approach. 2.5. Model Building. 2.6. Deploying a Model. 2.7. Post Deployment: Model Maintenance and Data Management. 2.8. Wrapping up Analytics. 2.9. Analytics Example: Kidney Exchange Model. 3. Trust. 3.1. What is Trust and Why it is Important. 3.2. How to Build Swift Trust. 3.3. Conventional Trust. 3.4. Trust in the Math Model. 3.5. Ethical Issues. 3.6. Real World Impact - Predictive Policing. 4. Communication. 4.1. The Goal of Communication. 4.2. Laying the Groundwork for Successful Communication. 4.3. Setting the Context. 4.4. Managing Expectation. 4.5. Make the Audience Care. 4.6. Atomizing Knowledge. 4.7. Levels of Understanding. 4.8. Gathering Information on Processes and Data. 4.9. Communicating with a Technical Audience. 4.10. Guiding the Conversation. 4.11. Using Data Visualization Effectively. 4.12. Minimizing Misleading Communication. 4.13. Spreading your Message. 4.14. Communications Example: Kidney Exchange Model. 5. Experience. 5.1. Organizational Experience. 5.2. Types of Experience. 5.3. Specialists Versus Generalists. 5.4. Learning Through Failure. 5.5. Starting Off: Creating Ways to Gain Experience. 5.6. Analytics Self Care. 6. Convince Them. 6.1. The Compelling Analytics Message. 6.2. Dimensions of Model Success. 6.3. Creating and Sustaining the Change. 6.4. How the Elements Interact. 6.5. Data and Denim. 7. Conclusion. 7.1. Why the Human Element Matters. 7.2. The Human Advantage over Automation. 7.3. Summing it Up. 7.4. Putting into Practice.
1. Introduction. 1.1. Defining an Analytics Professional. 1.2. Design of the Book. 1.3. Purpose of the Book. 1.4. Kidney Paired Donation Case Study. 2. Defining Analytics. 2.1. Real World Impact of Analytics. 2.2. Types of Analytics. 2.3. The Analytics Process. 2.4. Selecting an Analytical Approach. 2.5. Model Building. 2.6. Deploying a Model. 2.7. Post Deployment: Model Maintenance and Data Management. 2.8. Wrapping up Analytics. 2.9. Analytics Example: Kidney Exchange Model. 3. Trust. 3.1. What is Trust and Why it is Important. 3.2. How to Build Swift Trust. 3.3. Conventional Trust. 3.4. Trust in the Math Model. 3.5. Ethical Issues. 3.6. Real World Impact - Predictive Policing. 4. Communication. 4.1. The Goal of Communication. 4.2. Laying the Groundwork for Successful Communication. 4.3. Setting the Context. 4.4. Managing Expectation. 4.5. Make the Audience Care. 4.6. Atomizing Knowledge. 4.7. Levels of Understanding. 4.8. Gathering Information on Processes and Data. 4.9. Communicating with a Technical Audience. 4.10. Guiding the Conversation. 4.11. Using Data Visualization Effectively. 4.12. Minimizing Misleading Communication. 4.13. Spreading your Message. 4.14. Communications Example: Kidney Exchange Model. 5. Experience. 5.1. Organizational Experience. 5.2. Types of Experience. 5.3. Specialists Versus Generalists. 5.4. Learning Through Failure. 5.5. Starting Off: Creating Ways to Gain Experience. 5.6. Analytics Self Care. 6. Convince Them. 6.1. The Compelling Analytics Message. 6.2. Dimensions of Model Success. 6.3. Creating and Sustaining the Change. 6.4. How the Elements Interact. 6.5. Data and Denim. 7. Conclusion. 7.1. Why the Human Element Matters. 7.2. The Human Advantage over Automation. 7.3. Summing it Up. 7.4. Putting into Practice.
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