Linear Digressions Agile Development for Data Scientists, Part 2: Where Modifications Help
7 snips
Aug 26, 2018 Delve into the fascinating blend of agile development and data science! Discover how collaborative practices can significantly enhance project success. Uncover the dangers of rushing projects and how simplicity can lead to better outcomes. Learn about the unique challenges in data science that require flexible management. The importance of peer reviews and weekly wrap-ups shines through as essential steps for improved communication and quality. Transform your approach to data analytics with modified agile techniques!
AI Snips
Chapters
Transcript
Episode notes
Core Agile Principles
- Agile focuses on early, continuous delivery and embraces changing requirements for customer benefit.
- It prioritizes motivated individuals, face-to-face communication, and working software as progress.
Challenges of Agile for Data Science
- Data science work often reveals unexpected findings, unlike software development where goals are clearer.
- Agile's rigid sprint planning may limit valuable exploratory dives in data analysis.
Modify Agile for Exploration
- Build slack time into schedules for exploratory data analysis and fortuitous discoveries.
- Use time-bound research spikes to explore rabbit holes without losing overall project focus.