Managing human resources, time allocation, and risk management in R&D projects, particularly in Artificial Intelligence/Machine Learning/Data Analysis, poses unique challenges. Key areas such as model design, experimental planning, system integration, and evaluation protocols require specialized attention. In most cases, the research tends to focus primarily on one of the two main aspects: either the technical aspect of AI/ML/DA or the teams’ effort, or the typical management aspect and team members’ roles in such a project. Both are equally import for successful real-world R&D, but they are rarely examined together and tightly correlated. Data Science for Teams: 20 Lessons from the Fieldwork addresses the issue of how to deal with all these aspects within the context of real-world R&D projects, which are a distinct class of their own. The book shows the everyday effort within the team, and the adhesive substance in between that makes everything work. The core material in this book is organized over four main Parts with five Lessons each. Author Harris Georgiou goes into the difficulties progressively and dives into the challenges one step at a time, using a typical timeline profile of an R&D project as a loose template. From the formation of a team to the delivery of final results, whether it is a feasibility study or an integrated system, the content of each Lesson revisits hints, ideas and events from real-world projects in these fields, ranging from medical diagnostics and big data analytics to air traffic control and industrial process optimization. The scope of DA and ML is the underlying context for all, but most importantly the main focus is the team: how its work is organized, executed, adjusted, and optimized. Data Science for Teams presents a parallel narrative journey, with an imaginary team and project assignment as an example, running an R&D project from day one to its finish line. Every Lesson is explained and demonstrated within the team narrative, including personal hints and paradigms from real-world projects.
1. Introduction - p. 1 2. Chapter 1: Lesson 1: Respect the basics, learn the roles - p. 3 3. Chapter 2: Lesson 2: Team building – people over things - p. 15 4. Chapter 3: Lesson 3: Keep the team happy, then committed - p. 25 5. Chapter 4: Lesson 4: Give room to new ideas, but always have contingencies in place - p. 37 6. Part 2: Bend the rules - p. 47 7. Introduction - p. 49 8. Chapter 5: Lesson 5: In the real world, there are no well-defined tasks - p. 51 9. Chapter 6: Lesson 6: In the real world, data are raw and not ready for use - p. 63 10. Chapter 7: Lesson 7: Keep things simple, but not too simple - p. 75 11. Chapter 8: Lesson 8: Embrace good ideas, even if they are risky - p. 85 12. Chapter 9: Lesson 9: Avoid the “one tool for all” mindset - p. 95 13. Chapter 10: Lesson 10: Avoid the “minimum effort principle” - p. 105 14. Part 3: Forget the rules - p. 112 15. Introduction - p. 113 16. Chapter 11: Lesson 11: Always have backups – prepare for the unexpected - p. 115 17. Chapter 12: Lesson 12: Embrace critical feedback, always - p. 125 18. Chapter 13: Lesson 13: Iteration and adaptation versus long-term planning - p. 135 19. Chapter 14: Lesson 14: Managing expectations - p. 145 20. Chapter 15: Lesson 15: Deadlines, prioritization, and getting things done - p. 155 21. Chapter 16: Lesson 16: The “Diminishing Residual Efforts” effect - p. 163 22. Chapter 17: Lesson 17: Integration – the time of pain and suffering - p. 169 23. Part 4: Embed, extend, repeat - p. 175 24. Introduction - p. 177 25. Chapter 18: Lesson 18: Make things happen now, but plan for the future - p. 179 26. Chapter 19: Lesson 19: Keep loyal to discipline, guidelines, and good practices - p. 187 27. Chapter 20: Lesson 20: Remember why you do this - p. 195 28. Appendix A: Almost there - p. 199 29. References - p. 203 30. Index - p. 211
Managing human resources, time allocation, and risk management in R&D projects, particularly in Artificial Intelligence/Machine Learning/Data Analysis, poses unique challenges. Key areas such as model design, experimental planning, system integration, and evaluation protocols require specialized attention. In most cases, the research tends to focus primarily on one of the two main aspects: either the technical aspect of AI/ML/DA or the teams’ effort, or the typical management aspect and team members’ roles in such a project. Both are equally import for successful real-world R&D, but they are rarely examined together and tightly correlated. Data Science for Teams: 20 Lessons from the Fieldwork addresses the issue of how to deal with all these aspects within the context of real-world R&D projects, which are a distinct class of their own. The book shows the everyday effort within the team, and the adhesive substance in between that makes everything work. The core material in this book is organized over four main Parts with five Lessons each. Author Harris Georgiou goes into the difficulties progressively and dives into the challenges one step at a time, using a typical timeline profile of an R&D project as a loose template. From the formation of a team to the delivery of final results, whether it is a feasibility study or an integrated system, the content of each Lesson revisits hints, ideas and events from real-world projects in these fields, ranging from medical diagnostics and big data analytics to air traffic control and industrial process optimization. The scope of DA and ML is the underlying context for all, but most importantly the main focus is the team: how its work is organized, executed, adjusted, and optimized. Data Science for Teams presents a parallel narrative journey, with an imaginary team and project assignment as an example, running an R&D project from day one to its finish line. Every Lesson is explained and demonstrated within the team narrative, including personal hints and paradigms from real-world projects.
1. Introduction - p. 1 2. Chapter 1: Lesson 1: Respect the basics, learn the roles - p. 3 3. Chapter 2: Lesson 2: Team building – people over things - p. 15 4. Chapter 3: Lesson 3: Keep the team happy, then committed - p. 25 5. Chapter 4: Lesson 4: Give room to new ideas, but always have contingencies in place - p. 37 6. Part 2: Bend the rules - p. 47 7. Introduction - p. 49 8. Chapter 5: Lesson 5: In the real world, there are no well-defined tasks - p. 51 9. Chapter 6: Lesson 6: In the real world, data are raw and not ready for use - p. 63 10. Chapter 7: Lesson 7: Keep things simple, but not too simple - p. 75 11. Chapter 8: Lesson 8: Embrace good ideas, even if they are risky - p. 85 12. Chapter 9: Lesson 9: Avoid the “one tool for all” mindset - p. 95 13. Chapter 10: Lesson 10: Avoid the “minimum effort principle” - p. 105 14. Part 3: Forget the rules - p. 112 15. Introduction - p. 113 16. Chapter 11: Lesson 11: Always have backups – prepare for the unexpected - p. 115 17. Chapter 12: Lesson 12: Embrace critical feedback, always - p. 125 18. Chapter 13: Lesson 13: Iteration and adaptation versus long-term planning - p. 135 19. Chapter 14: Lesson 14: Managing expectations - p. 145 20. Chapter 15: Lesson 15: Deadlines, prioritization, and getting things done - p. 155 21. Chapter 16: Lesson 16: The “Diminishing Residual Efforts” effect - p. 163 22. Chapter 17: Lesson 17: Integration – the time of pain and suffering - p. 169 23. Part 4: Embed, extend, repeat - p. 175 24. Introduction - p. 177 25. Chapter 18: Lesson 18: Make things happen now, but plan for the future - p. 179 26. Chapter 19: Lesson 19: Keep loyal to discipline, guidelines, and good practices - p. 187 27. Chapter 20: Lesson 20: Remember why you do this - p. 195 28. Appendix A: Almost there - p. 199 29. References - p. 203 30. Index - p. 211
