Data Science for Teams - 20 Lessons from the Fieldwork

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.

List of contents

I. Set the rules
1. Team building - People over things
2. Keep the team happy, then committed
3. Give room to new ideas, but always have contingencies in place
4. In the real world, there are no well-defined tasks
5. In the real world, data are raw and not ready for use

II: Bend the rules
6. Keep things simple, but not too simple .
7. Embrace good ideas, even if they are risky
8. Avoid the ‘one tool for all’ mindset
9. Avoid the ‘minimum effort principle’
10. Always have backups - Prepare for the unexpected

III: Forget the rules
11. Embrace critical feedback, always
12. Iteration and adaptation over long-term planning
13. Managing expectations
14. Deadlines, prioritization and getting things done
15. The ‘Diminishing Residual Efforts’ effect

IV: Embed, Extend, Repeat
16. The ‘two language problem’
17. Integration - The moment of pain and suffering
18. Make things happen now, but plan for the future
19. Keep loyal to discipline, guidelines and good practices
20. Remember why you do this Moving forward

About the author

Dr. Harris Georgiou (MSc, PhD) is a Machine Learning and Data Scientist specializing
in mobility analytics, big data, dynamic systems, complex systems, signal/image
processing, Bioinformatics and Artificial Intelligence. He is a R&D consultant and
senior researcher for more than 25 years in the field in multiple post-doctorate
assignments, focusing on in sparse learning models and fMRI/EEG signal for
applications in Biomedicine and Bioinformatics, next-generation air traffic control,
maritime surveillance & urban mobility via Big data analytics & Machine Learning
methods. Since 2016 he is the active LEAR, team coordinator & scientific advisor with
the Hellenic Rescue Team of Attica (HRTA) in several EU-funded R&D projects
(H2020) for civil protection, miniaturized robotic equipment & sensors for SAR
operations and next-generation advanced technologies for first responders. He is also
course leader/lecturer, as well as private consultant, in collaboration with over 190
academic institutions, organizations and companies. He has published 88 peerreviewed
journal & conference papers, plus 83 independent & open-access works,
technical reports, magazine articles, software toolboxes and open-access datasets, a
two-volume book series on medical imaging and diagnostic image analysis,
contributed in six other textbooks and one U.S. patent in related R&D areas. He has
been a member of over 90 technical committees in international scientific journals &
conferences since 2008.

Product details

• Authors: Harris Georgiou
• Publisher: Elsevier
• Languages: English
• Product format: Paperback / Softback
• Release: 01.01.2026
• EAN: 9780443364068
• ISBN: 978-0-443-36406-8
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; machine learning, Artificial Intelligence, Information technology: general issues, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Data Science / General, Data capture and analysis