Linear Digressions Agile Development for Data Scientists, Part 1: The Good
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Aug 19, 2018 Discover how Agile principles in software development can enhance data science workflows. The discussion highlights the iterative nature of Agile, showcasing its advantages over traditional methodologies like Waterfall. Learn about adaptive planning and the importance of setting clear goals to ensure project success. The speakers also delve into engaging stakeholders for better decision-making and the unique estimation technique of story pointing, using the Fibonacci sequence to navigate task complexity. Tune in for insights that can revolutionize your data science projects!
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Benefits of Agile Sprint Cadence
- Agile divides work into short, manageable sprints, usually two weeks long.
- This cadence enables frequent reassessment and adaptive planning, crucial for dynamic projects.
Set Clear Acceptance Criteria
- Define clear acceptance criteria upfront to specify when a task or sprint is done.
- This focus helps maintain direction and prevents wasting time on low-value tasks.
Engage Stakeholders Frequently
- Regularly communicate progress and results with stakeholders to ensure alignment.
- Involve all key users early to avoid building the wrong solution unknowingly.