Join Doug Rose for an in-depth discussion in this video See how to use a DSLC, part of Learning Data Science: Using Agile Methodology.
- Data science doesn't work very well…with existing process life cycles.…It's not enough like software to fit into the SDLC.…The CRISP-DM data mining process…is a little too rigid for quick results.…That doesn't mean that a data science team…should work in whatever way feels right.…There's some real value in these life cycles.…One value is that they give you…a high level map of where you're going.…This is useful when you're just starting…a data science team. You can get…a clear sense of the path forward.…That way, you'll start with the end in mind.…
The danger in these life cycles…is that it becomes a primary focus of your work.…You want to use the life cycles…as a way to do better data science.…You don't want to follow the process…for the sake of following the process.…A good life cycle should be like a hand rail.…It's good to know it's there, but you don't want…to cling to it with every step.…After a while, you shouldn't even notice that it's there.…For data science projects, you can use…a data science life cycle, or DSLC.…
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