Education > eBook[PDF] > Data Science for Teams. 20 Lessons From the Fieldwork By Harris V. Georgiou (All)

Data Science for Teams. 20 Lessons From the Fieldwork By Harris V. Georgiou

Document Content and Description Below

Data Science for Teams: 20 Lessons from the Fieldwork by Dr. Harris V. Georgiou (published by Morgan Kaufmann / Elsevier in July 2025) is a specialized textbook that focuses on the human dynamics, collaboration, and everyday project management realities of high-end AI, Machine Learning (ML), and Data Analytics (DA) research and development (R&D) teams. Unlike traditional data science books that focus purely on algorithms and technical coding, Dr. Georgiou shifts the lens to the "adhesive substance" that binds a technical team together to deliver real-world business value. ________________________________________ 📖 Book Structure and Core Themes The book avoids dry theory by following a parallel narrative journey. It uses an imaginary R&D team and their project assignment as a running case study from day one to the finish line. The core material is organized into four main parts, with each part containing five structured lessons (totaling 20 lessons): • Part 1: Team Formation & Alignment – Recruitment, defining clear roles, establishing baseline expectation management, and setting early technical paths. • Part 2: Planning & Execution – Dealing with deadlines, iterative progress, experimental planning, and bridging the gap between scientific exploration and delivery targets. • Part 3: Risk & Conflict Management – Handling messy data realities, system integration bottlenecks, internal technical disagreements, and shifting priorities. • Part 4: Final Delivery & Future Integration – Preparing final evaluations, delivering systems (from feasibility studies to active deployments), and team evolution. ________________________________________ 🎯 Key Learning Takeaways • Beyond Standard Agile: Illustrates why cookie-cutter Agile methods fall short in R&D. It advocates for a complete re-thinking of target-setting where objectives evolve alongside the team's data findings. • The "Learning vs. Delivering" Dilemma: Addresses the tension between a data scientist's desire to experiment indefinitely versus the management team's need to meet production deadlines. • Real-World Grounding: Every lesson draws insights from the author's 25+ years of experience across fields like medical diagnostics, big data analytics, air traffic control, and industrial optimization. • Organizational Fusion: Teaches technical professionals how to phrase their engineering constraints in ways that corporate stakeholders can understand and fund

[Show More]

Last updated: 3 months ago

Preview 3 out of 246 pages

Buy Now

Instant download