Join Doug Rose for an in-depth discussion in this video Add to organizational knowledge, part of Data Science Teams: Doing Agile Data Science.
- You've seen that a predictable way…to make great discoveries is to allow…your team to explore the data.…They should have the freedom to look…for interesting connections.…There's also the data science life cycle…that provides a structure for the team…to tell interesting stories.…The DSLC gives the organization visibility…into what the team is doing.…Instead of planning they're getting predictable delivery.…That's a key way to keep your team focused…on building knowledge.…You're not giving them an objective.…
Instead you're giving them a predictable framework.…They have a weekly rhythm to share their stories.…If the rest of the organization doesn't like their stories,…they can always encourage the team to go…in a different direction.…The DSLC sprints and exploration…all work together to provide insights in learning.…If you're working on a data science team,…you should try to keep these three things in balance.…The DSLC gives the team a blueprint…for how to deliver real value.…The team should identify the roles…and work in a cycle of questioning,…
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