Join Doug Rose for an in-depth discussion in this video Work on a data science project, part of Learning Data Science: Using Agile Methodology.
- There's an old joke, that if you have a shiny new hammer,…then everything looks like a nail.…Maybe you've seen how project management…has been very successful in many organizations.…It's been a shiny hammer that helps nail down costs…and schedules, while managing scope.…It's been successful enough that organizations…use project management for many of their efforts.…That doesn't mean that project management…works for data science.…Typical projects demand upfront requirements.…You need to understand what you're going to build…before you start planning your project.…
They also focus on delivering…within a scope, cost and schedule.…You can't effectively manage these projects…without some sense of these constraints.…Finally, a typical project delivers a product or service.…There should be a deliverable at the end of your project.…Maybe you complete a report. Your team…may have delivered a software product.…In the end, you have to deliver something…so that you know it's complete.…Data science is different; your team…
This course shows how to structure your work within a two-week sprint. See how to work within a data science life cycle (DSLC)—a methodology for cycling through questions, research, and reporting every two weeks. Explore key practices to help your team break down the work so it fits within a two-week sprint. Learn how to use tools like question boards to encourage discussion and find essential questions. And most importantly, learn how to grow your team's shared knowledge and avoid common pitfalls.
- Defining data science success
- Determining project challenges and criteria for success
- Using a DSLC
- Iterating through DSLC sprints
- Creating a question board
- Breaking down your work
- Adding to organizational knowledge
- Avoiding pitfalls