The Art of Data Science
Chapman & Hall/CRC (Verlag)
978-1-032-81696-8 (ISBN)
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Although change is constant in business and analytics, some fundamental principles and lessons learned are truly timeless, extending and surviving beyond the rapid ongoing evolution of tools, techniques, and technologies. Through a series of articles published over the course of his 30+ year career in analytics and technology, author Doug Gray shares the most important lessons he has learned – with colleagues and students as well – that have helped to ensure success on his journey as a practitioner, leader, and educator.
The reader witnesses the Analytical Sciences profession through the mind’s eye of a practitioner who has operated at the forefront of analytically-inclined organizations, such as American Airlines and Walmart, delivering solutions that generate hundreds of millions of dollars annually in business value, and an educator teaching students and conducting research at a leading university. Through real-world project case studies, first-hand stories, and practical examples, we learn the foundational truth underlying successful analytics applications. From bridging theory and practice, to playing a role as a consultant in digital transformation, to understanding how analytics can be economically transformational, identifying required soft skills like leadership skills, and understanding the reasons why data science projects often fail, the reader can better visualize and understand the nuanced, multidimensional nature of Analytical Sciences best practices, projects, and initiatives.
The readers will gain a broad perspective on where and how to find success with Analytical Sciences, including the ability to ensure that we apply the right tool, at the right time and right place, and sometimes in different industries.
Finally, through the author’s own career synopsis on becoming a practitioner and leader, and his distilled insights, the reader is offered a view into the future that analytics holds, along with some invaluable career advice regarding where to focus, how to make good choices, and how to measure success individually and organizationally.
Douglas A. Gray is a practitioner, leader, and educator with over 30 years of experience leading award-winning teams at industry luminaries in analytics, including INFORMS Prize-winning American Airlines and Walmart. His teams have delivered advanced game-changing solutions in the airline operations, healthcare, and omnichannel retail supply chain domains, which deliver hundreds of millions of dollars in business value and economic impact annually. He teaches Analytics and AI Strategy at Southern Methodist University (SMU) in the Executive MBA, Executive Education, and MS Data Science programs, and has published over a dozen articles and two books on analytics best practices and applications.
About the Author Acknowledgements Foreword Introduction 1. Career Summary – On Becoming a Practitioner and Leader 2. The Dual Challenge of the Analytical Sciences Practitioner 3. Digital Transformation 4. Advanced Analytics is Economically Transformational 5. Airworthy: American Airlines Heavy Maintenance Planning & Scheduling 6. Consulting Concepts Learned from Airworthy 7. A Modern Day Project Applying the Same Principles: Advanced Analytics Commodity Case 8. Right Tool, Right Place, Right Time (with Nader Kabbani) 9. Under Fire: Lessons from the Front; Revenue Management for Manufacturing 10. Analytics Nontechnical Skills 11. Top 10 Analytics Leadership Skills (with Tom Davenport) 12. Top 10 Reasons Data Science Projects Fail 13. Surrounded by Success 14. O.R. in 2048 Conclusion Appendix
| Erscheint lt. Verlag | 13.3.2025 |
|---|---|
| Zusatzinfo | 13 Line drawings, black and white; 13 Illustrations, black and white |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| ISBN-10 | 1-032-81696-1 / 1032816961 |
| ISBN-13 | 978-1-032-81696-8 / 9781032816968 |
| Zustand | Neuware |
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