Join Doug Rose for an in-depth discussion in this video Focus on just a few meetings, part of Learning Data Science: Using Agile Methodology.
- Your data science team will typically want to work…in two week sprints.…The team will have a lot to do,…so they need some structure, to stay efficient.…Remember that you'll go through every area…of the data science life cycle in each sprint.…To work at that pace,…the team needs a consistent amount of time to work.…They won't be able to attend many open-ended meetings.…Each meeting will need a separate timebox.…A timebox is pretty much what it sounds like.…It's a box of time…that the team agrees upon before the meeting.…
Let's say your team has a meeting…with a one-hour timebox.…Whatever they decide at the end of that timebox…will last until the end of the sprint.…You can never reschedule or follow up…on a time-boxed meeting.…They start and then they end when the timebox expires.…In most organizations, meetings aren't a bad thing.…Meetings are a good way to bring up issues…and reinforce culture.…The challenge with meetings…is that they can add a lot of unpredictability.…You never know if your meeting will run long,…
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