- Gebundenes Buch
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung
This book is written for Analytics Professionals (APs) who want to increase their probability of success in implementing analytical solutions.
Andere Kunden interessierten sich auch für
- Gerald W. Evans (University of Louisville, Kentucky, USA)Multiple Objective Analytics for Criminal Justice Systems68,99 €
- Wolfgang GarnData Analytics for Business60,99 €
- Harvard Business ReviewHBR Guide to Data Analytics Basics for Managers (HBR Guide Series)16,99 €
- Harvard Business ReviewHBR Guide to Data Analytics Basics for Managers43,99 €
- Howard EisnerOperations Research and Systems Engineering70,99 €
- Konrad KulakowskiUnderstanding the Analytic Hierarchy Process200,99 €
- Paul R. RasmussenThe Quest to Feel Good73,99 €
-
-
-
This book is written for Analytics Professionals (APs) who want to increase their probability of success in implementing analytical solutions.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Chapman and Hall/CRC Focus Case Studies in Analytics and OR
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 124
- Erscheinungstermin: 30. August 2022
- Englisch
- Abmessung: 222mm x 145mm x 11mm
- Gewicht: 250g
- ISBN-13: 9781032065250
- ISBN-10: 1032065257
- Artikelnr.: 64360369
- Herstellerkennzeichnung
- Books on Demand GmbH
- In de Tarpen 42
- 22848 Norderstedt
- info@bod.de
- 040 53433511
- Chapman and Hall/CRC Focus Case Studies in Analytics and OR
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 124
- Erscheinungstermin: 30. August 2022
- Englisch
- Abmessung: 222mm x 145mm x 11mm
- Gewicht: 250g
- ISBN-13: 9781032065250
- ISBN-10: 1032065257
- Artikelnr.: 64360369
- Herstellerkennzeichnung
- Books on Demand GmbH
- In de Tarpen 42
- 22848 Norderstedt
- info@bod.de
- 040 53433511
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.
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.
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.
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.