Join Doug Rose for an in-depth discussion in this video Iterate through DSLC sprints, part of Learning Data Science: Using Agile Methodology.
- Now that you've seen a data science life cycle,…you might be wondering how this looks in practice.…One of the most important things to remember about the DSLC…is that it's not designed to run in phases.…The DSLC is about focusing on five key areas.…It starts off by identifying the key players in your story.…Then you go through a loop of questions,…research, and results.…You look for insights…and then add to organizational knowledge.…This whole life cycle can be run in a short sprint.…
You may have hard the term sprint.…It's widely used in agile software,…but it actually came from product development.…A sprint is a fixed period of time…where the team runs through the entire life cycle.…Each sprint should run all five areas…of the DSLC in this fixed time.…The data science team can run in two week sprints.…That's long enough to find insights,…and yet still short enough to adapt to new ideas.…The main advantage of running in sprints…is that that it shortens the time…between concept and cash.…
Many organizations take a long time…
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