Data Science for Teams
20 Lessons from the Fieldwork
- 1st Edition - July 30, 2025
- Latest edition
- Author: Harris V. Georgiou
- Language: English
Managing human resources, time allocation, and risk management in R&D projects, particularly in Artificial Intelligence/Machine Learning/Data Analysis, poses unique challenges.… Read more
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Description
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.
Key features
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
Readership
Readership
Computer Science researchers, data science researchers, and data analysis researchers in academia and industry. The primary audience also includes R&D coordinators, Artificial Intelligence/Machine Learning/Data Analysis team leaders, senior researchers (post-doc/associates), technical managers, and R&D project managers
Table of contents
Table of contents
CHAPTER 1 Lesson 1: Respect the basics, learn the roles
1.1 Organizational options
1.2 Team roles, generic
1.3 Team roles, actual
1.3.1 Infrastructure engineer
1.3.2 AI expert
1.3.3 Software developer
1.3.4 Team mentor-coordinator
1.3.5 Other roles and specialties
1.4 Our brave little team
CHAPTER 2 Lesson 2: Team building -- people over things
2.1 Building the team
2.2 Complexities and trade-offs
2.3 Getting people onboard
2.3.1 Setting the criteria
2.3.2 Misconceptions
2.3.3 Red flags
2.3.4 How to do it right
2.4 Letting people go
2.5 Departures
CHAPTER 3 Lesson 3: Keep the team happy, then committed
3.1 Leading versus Managing
3.1.1 Data Science as Engineering
3.1.2 Data Science is not classic Project Management
3.1.3 Key priorities and the human factor
3.2 Incentives and Commitment
3.2.1 Excellence and job satisfaction
3.2.2 Handling younger members
3.3 Team roles, revisited
3.3.1 In-depth guidelines
3.3.2 Transitions and integrations
3.3.3 The kick-off
3.3.4 The daily emergencies
3.3.5 Addressing personal issues
3.4 The dress code issue
CHAPTER 4 Lesson 4: Give room to new ideas, but always have contingencies
in place
4.1 The Software Engineering paradigm
4.1.1 Key differences and similarities with DS
4.1.2 Dealing with problems and failures
4.2 Exploiting new ideas
4.2.1 Diversity and collaboration
4.2.2 Gender diversity in the team
4.2.3 Diversity and Game Theory
4.3 Contingencies
4.3.1 Groupthink
4.3.2 Backups as a team principle
4.4 The big whiteboard
PART 2 Bend the rules
CHAPTER 5 Lesson 5: In the real world, there are no well-defined tasks
5.1 Unknown unknowns
5.1.1 Recognizing the proble
5.1.2 Analysis paralysis
5.2 Use cases
5.2.1 Civil Aviation
5.2.2 Agricultural quality control
5.3 The first shock
CHAPTER 6 Lesson 6: In the real world, data are raw and not ready for use
6.1 Handling real-world data
6.1.1 Factors and issues
6.1.2 Exploring the data
6.2 Use cases
6.2.1 Civil Aviation
6.2.2 Vehicle mobility analytics
6.2.3 SARS-CoV-2 pandemic
6.3 The second shock
CHAPTER 7 Lesson 7: Keep things simple, but not too simple
7.1 The automatic control paradigm
7.1.1 Principles of automatic control
7.1.2 Automation versus human factor
7.2 Project management and leadership
7.2.1 Toxic leadership
7.2.2 Project management, the NASA way
7.2.3 The Westrum model
7.3 Simplicity as a principle
7.3.1 Dealing with complexity
7.4 Use case: Adaptive X-ray machine
CHAPTER 8 Lesson 8: Embrace good ideas, even if they are risky
8.1 Assignments and initiatives
8.1.1 Who gives the presentations?
8.1.2 Remote control
8.1.3 Blame games
8.2 Endorsing openness
8.2.1 The curse of micro-management
8.2.2 Inclusive teamwork
8.3 Use cases
8.3.1 Mammographic mass shape analysis
8.3.2 Textiles modeling
8.4 Cold feet
CHAPTER 9 Lesson 9: Avoid the “one tool for all'' mindset
9.1 Getting into the weeds
9.1.1 Traditional versus ``blind'' ML
9.1.2 Smart clouds and edges
9.1.3 “Not invented here'' syndrome
9.2 Tunnel vision
9.2.1 The “Einstellung''
9.3 Focus on the most valuable
9.4 Use cases
9.4.1 fMRI unmixing
9.4.2 COVID-19 data analysis
CHAPTER 10 Lesson 10: Avoid the “minimum effort principle''
10.1 Minimum efforts
10.1.1 Low productivity mode
10.1.2 Knowledge silos
10.1.3 Simplicity is not laziness: The “XOR'' example
10.2 Marginally adequate
10.2.1 Quiet quitting
10.2.2 Learning versus delivering
10.2.3 Motivation alone is not enough
10.3 Opening up
PART 3 Forget the rules
CHAPTER 11 Lesson 11: Always have backups -- prepare for the unexpected
11.1 Hints from software risks
11.2 Managing risk
11.2.1 Assessment, prioritization, mitigation
11.2.2 Preventive planning
11.2.3 A little Game Theory
11.3 Team risks
11.3.1 Burnout
11.3.2 Over-confidence
11.3.3 Insecurities
11.4 Use case: Urban ETA prediction
CHAPTER 12 Lesson 12: Embrace critical feedback, always
12.1 The feedback loop
12.1.1 Reception of criticism
12.1.2 Dealing with arrogance
12.2 Conflict resolution in the team
12.2.1 Pack leaders and threshold guardians
12.2.2 Removing the barriers
12.2.3 Emergence of cooperation
12.3 Use case: Refugee influx analysis
12.4 Force Majeure
CHAPTER 13 Lesson 13: Iteration and adaptation versus long-term planning
13.1 The Software Development paradigm
13.1.1 The value of traditional approaches
13.1.2 Repetitions over strict designs
13.2 Iterative project management
13.2.1 Technical versus management issues
13.2.2 Common approaches
13.3 The OLPC example
CHAPTER 14 Lesson 14: Managing expectations
14.1 Expectations versus reality
14.2 Preemptive planning
14.3 The IPR issue
14.4 The DRS cluster example
CHAPTER 15 Lesson 15: Deadlines, prioritization, and getting things done
15.1 Priorities, preparations, and plans
15.2 Working under pressure
15.3 Tough decisions
15.4 Bending the rules
15.5 Getting things done
CHAPTER 16 Lesson 16: The “Diminishing Residual Efforts'' effect
16.1 Efforts fade out
16.2 Technical debt
16.3 Outside the comfort zone
16.4 Emergency response
CHAPTER 17 Lesson 17: Integration -- the time of pain and suffering
17.1 R&D is not a product
17.2 Canary releases and feature toggles
17.3 ``Blind'' prototyping
17.4 Quality as a goal
17.5 Vaporware
17.6 No single points of failure
17.7 Use case: search & rescue robotics
PART 4 Embed, extend, repeat
CHAPTER 18 Lesson 18: Make things happen now, but plan for the future
18.1 The value of maintainability
18.2 The COBOL example
18.3 An important balance
18.4 Accept change
18.5 Randomized modeling
18.6 Proof of work
18.7 Debugging from 25 billion km away
CHAPTER 19 Lesson 19: Keep loyal to discipline, guidelines, and good
practices
19.1 No magic tricks
19.2 Three main drivers
19.3 Excellence is a habit
19.4 Take care of your team
19.4.1 Provide help
19.4.2 Seek consensus
19.4.3 Defend your people
19.4.4 Be honest and transparent
19.5 It’s all yours forever
CHAPTER 20 Lesson 20: Remember why you do this
20.1 Critical events
20.2 Wins and loses
20.3 Successful failures
20.4 That’s what is all about
1.1 Organizational options
1.2 Team roles, generic
1.3 Team roles, actual
1.3.1 Infrastructure engineer
1.3.2 AI expert
1.3.3 Software developer
1.3.4 Team mentor-coordinator
1.3.5 Other roles and specialties
1.4 Our brave little team
CHAPTER 2 Lesson 2: Team building -- people over things
2.1 Building the team
2.2 Complexities and trade-offs
2.3 Getting people onboard
2.3.1 Setting the criteria
2.3.2 Misconceptions
2.3.3 Red flags
2.3.4 How to do it right
2.4 Letting people go
2.5 Departures
CHAPTER 3 Lesson 3: Keep the team happy, then committed
3.1 Leading versus Managing
3.1.1 Data Science as Engineering
3.1.2 Data Science is not classic Project Management
3.1.3 Key priorities and the human factor
3.2 Incentives and Commitment
3.2.1 Excellence and job satisfaction
3.2.2 Handling younger members
3.3 Team roles, revisited
3.3.1 In-depth guidelines
3.3.2 Transitions and integrations
3.3.3 The kick-off
3.3.4 The daily emergencies
3.3.5 Addressing personal issues
3.4 The dress code issue
CHAPTER 4 Lesson 4: Give room to new ideas, but always have contingencies
in place
4.1 The Software Engineering paradigm
4.1.1 Key differences and similarities with DS
4.1.2 Dealing with problems and failures
4.2 Exploiting new ideas
4.2.1 Diversity and collaboration
4.2.2 Gender diversity in the team
4.2.3 Diversity and Game Theory
4.3 Contingencies
4.3.1 Groupthink
4.3.2 Backups as a team principle
4.4 The big whiteboard
PART 2 Bend the rules
CHAPTER 5 Lesson 5: In the real world, there are no well-defined tasks
5.1 Unknown unknowns
5.1.1 Recognizing the proble
5.1.2 Analysis paralysis
5.2 Use cases
5.2.1 Civil Aviation
5.2.2 Agricultural quality control
5.3 The first shock
CHAPTER 6 Lesson 6: In the real world, data are raw and not ready for use
6.1 Handling real-world data
6.1.1 Factors and issues
6.1.2 Exploring the data
6.2 Use cases
6.2.1 Civil Aviation
6.2.2 Vehicle mobility analytics
6.2.3 SARS-CoV-2 pandemic
6.3 The second shock
CHAPTER 7 Lesson 7: Keep things simple, but not too simple
7.1 The automatic control paradigm
7.1.1 Principles of automatic control
7.1.2 Automation versus human factor
7.2 Project management and leadership
7.2.1 Toxic leadership
7.2.2 Project management, the NASA way
7.2.3 The Westrum model
7.3 Simplicity as a principle
7.3.1 Dealing with complexity
7.4 Use case: Adaptive X-ray machine
CHAPTER 8 Lesson 8: Embrace good ideas, even if they are risky
8.1 Assignments and initiatives
8.1.1 Who gives the presentations?
8.1.2 Remote control
8.1.3 Blame games
8.2 Endorsing openness
8.2.1 The curse of micro-management
8.2.2 Inclusive teamwork
8.3 Use cases
8.3.1 Mammographic mass shape analysis
8.3.2 Textiles modeling
8.4 Cold feet
CHAPTER 9 Lesson 9: Avoid the “one tool for all'' mindset
9.1 Getting into the weeds
9.1.1 Traditional versus ``blind'' ML
9.1.2 Smart clouds and edges
9.1.3 “Not invented here'' syndrome
9.2 Tunnel vision
9.2.1 The “Einstellung''
9.3 Focus on the most valuable
9.4 Use cases
9.4.1 fMRI unmixing
9.4.2 COVID-19 data analysis
CHAPTER 10 Lesson 10: Avoid the “minimum effort principle''
10.1 Minimum efforts
10.1.1 Low productivity mode
10.1.2 Knowledge silos
10.1.3 Simplicity is not laziness: The “XOR'' example
10.2 Marginally adequate
10.2.1 Quiet quitting
10.2.2 Learning versus delivering
10.2.3 Motivation alone is not enough
10.3 Opening up
PART 3 Forget the rules
CHAPTER 11 Lesson 11: Always have backups -- prepare for the unexpected
11.1 Hints from software risks
11.2 Managing risk
11.2.1 Assessment, prioritization, mitigation
11.2.2 Preventive planning
11.2.3 A little Game Theory
11.3 Team risks
11.3.1 Burnout
11.3.2 Over-confidence
11.3.3 Insecurities
11.4 Use case: Urban ETA prediction
CHAPTER 12 Lesson 12: Embrace critical feedback, always
12.1 The feedback loop
12.1.1 Reception of criticism
12.1.2 Dealing with arrogance
12.2 Conflict resolution in the team
12.2.1 Pack leaders and threshold guardians
12.2.2 Removing the barriers
12.2.3 Emergence of cooperation
12.3 Use case: Refugee influx analysis
12.4 Force Majeure
CHAPTER 13 Lesson 13: Iteration and adaptation versus long-term planning
13.1 The Software Development paradigm
13.1.1 The value of traditional approaches
13.1.2 Repetitions over strict designs
13.2 Iterative project management
13.2.1 Technical versus management issues
13.2.2 Common approaches
13.3 The OLPC example
CHAPTER 14 Lesson 14: Managing expectations
14.1 Expectations versus reality
14.2 Preemptive planning
14.3 The IPR issue
14.4 The DRS cluster example
CHAPTER 15 Lesson 15: Deadlines, prioritization, and getting things done
15.1 Priorities, preparations, and plans
15.2 Working under pressure
15.3 Tough decisions
15.4 Bending the rules
15.5 Getting things done
CHAPTER 16 Lesson 16: The “Diminishing Residual Efforts'' effect
16.1 Efforts fade out
16.2 Technical debt
16.3 Outside the comfort zone
16.4 Emergency response
CHAPTER 17 Lesson 17: Integration -- the time of pain and suffering
17.1 R&D is not a product
17.2 Canary releases and feature toggles
17.3 ``Blind'' prototyping
17.4 Quality as a goal
17.5 Vaporware
17.6 No single points of failure
17.7 Use case: search & rescue robotics
PART 4 Embed, extend, repeat
CHAPTER 18 Lesson 18: Make things happen now, but plan for the future
18.1 The value of maintainability
18.2 The COBOL example
18.3 An important balance
18.4 Accept change
18.5 Randomized modeling
18.6 Proof of work
18.7 Debugging from 25 billion km away
CHAPTER 19 Lesson 19: Keep loyal to discipline, guidelines, and good
practices
19.1 No magic tricks
19.2 Three main drivers
19.3 Excellence is a habit
19.4 Take care of your team
19.4.1 Provide help
19.4.2 Seek consensus
19.4.3 Defend your people
19.4.4 Be honest and transparent
19.5 It’s all yours forever
CHAPTER 20 Lesson 20: Remember why you do this
20.1 Critical events
20.2 Wins and loses
20.3 Successful failures
20.4 That’s what is all about
Product details
Product details
- Edition: 1
- Latest edition
- Published: August 13, 2025
- Language: English
About the author
About the author
HG
Harris V. Georgiou
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.
Affiliations and expertise
Hellenic Rescue Team of Attica (HRTA), GreeceView book on ScienceDirect
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