Data Science for Teams (book) - Available now

Data Science for Teams: 20 Lessons from the Fieldwork. The book dives into the everyday efforts of collaboration within the team working in the Data Science (DS) domain, addressing the issues of how to deal with all the unique aspects and challenges within the context of real-world R&D projects. Instead of the scientific aspect of Data Analytics (DA) and Machine Learning (ML), it shifts focus on the team itself and the adhesive substance in between that makes everything work. 

Published by Morgan Kaufmann (2025)

Paperback ISBN: 9780443364068 / eBook ISBN: 9780443364075

https://shop.elsevier.com/books/data-science-for-teams/georgiou/978-0-443-36406-8
https://www.amazon.co.uk/Data-Science-Teams-Lessons-Fieldwork-ebook/dp/B0DQPKFKDQ
 

Contents

The core material in this book is organized over four main Parts with 20 Lessons in total. Author Harris Georgiou goes into the difficulties progressively and dives into the challenges one step at a time, using a typical timeline profile of a DS 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, motivated, adjusted, and optimized. Furthermore, Data Science for Teams presents a parallel narrative journey, with an imaginary team and project assignment as an example, running a DS project from day one to its finish line. Every Lesson is explained and demonstrated within the context of such a team, including personal hints and use case paradigms from real-world projects.

Key features

  • Provides well-defined learning items in the form of Lessons, with clear structure and expected learning outcomes.

  • Presents concepts in a narrative format that includes a running case study throughout the book, for better understanding and increased engagement.

  • Demonstrates how to accomplish the fusion of organizational needs and constraints regarding a high-end R&D team, together with the requirements from the aspect of every day project management (deadlines, deliverables, milestones, scheduling, risks).

  • Shows how to transform typical project management into functional team-oriented goals and targets, in the context of iterative progress and continuous adaptation; this requires not just an Agile approach to project management, but a complete re-thinking of target setting and team evolution as a unit.

  • Provides readers with deep understanding of how such R&D projects work in the real-world, including the everyday challenges, complexities and minimum-risk solutions; for educators in academia, this is probably the last phase of preparing future AI/ML/DA professionals for the tasks they will soon face.

 

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 advances in Artificial Intelligence. He has also worked as course leader/lecturer and a A.I. consultant in the private sector for more than 25 years, in collaboration with over 240 academic institutions, organizations and companies. He has published 93 peer-reviewed journal & conference papers, plus 138 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 100 technical committees in international scientific journals & conferences since 2008, general secretary of the AC Board of the Hellenic Informatics Union since 2015.

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