Join Doug Rose for an in-depth discussion in this video Break down your work, part of Learning Data Science: Using Agile Methodology.
- By now you've seen the difference between…the software development and the data science lifecycle.…You've also seen how the data science lifecycle…is best delivered in two week sprints.…These sprints allow you to break down the work…and quickly deliver valuable insights.…When you're on a data science team,…there are always large data sets that need scrubbing.…There's also new data sources to explore.…A lot of what you're doing is preparing your data.…When you work in sprints,…you're forcing a team to do the minimum…amount of data preparation.…
Doing the minimum amount of preparation…might sound like a bad thing.…Most people want to do higher quality work,…plus there's a lot of emphasis…in organizations on preparation.…In actuality, when you do the minimum amount of prep,…you're forcing the team to focus on insights,…and not just on capability.…You don't want your team spending weeks…or even months just setting up the data.…Instead, you want the team to almost immediately…start exploring the data.…You'll also have to look at it from…
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