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…mehr
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.
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.
Inhaltsangabe
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
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
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