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Full Description
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.
Contents
I: Set the rules
Lesson 1: Respect the basics, learn the roles
Lesson 2: Team building - People over things
Lesson 3: Keep the team happy, then committed
Lesson 4: Give room to new ideas, but always have contingencies in place
Lesson 5: In the real world, there are no well-defined tasks
II: Bend the rules
Lesson 6: In the real world, data are raw and not ready for use
Lesson 7: Keep things simple, but not too simple
Lesson 8: Embrace good ideas, even if they are risky
Lesson 9: Avoid the `one tool for all' mindse
Lesson 10: Avoid the `minimum effort principle'
III: Forget the rules
Lesson 11: Always have backups - Prepare for the unexpected
Lesson 12: Embrace critical feedback, always
Lesson 13: Iteration and adaptation versus long-term planning
Lesson 14: Managing expectations
Lesson 15: Deadlines, prioritization and getting things done
IV: Embed, Extend, Repeat
Lesson 16: The `Diminishing Residual Efforts' effect
Lesson 17: Integration - The time of pain and suffering
Lesson 18: Make things happen now, but plan for the future
Lesson 19: Keep loyal to discipline, guidelines and good practices
Lesson 20: Remember why you do this
