Join Doug Rose for an in-depth discussion in this video Loop through questions, part of Learning Data Science: Using Agile Methodology.
- Waterfall-style life cycles are not…a great fit for data science.…A data science life cycle, or DSLC…is much more lightweight that also stays closer…to the scientific method.…Remember that data science is exploratory.…You need an empirical process to react to new data.…If your data science team finds out something new then…they don't to fight a waterfall process to add any value.…The data science life cycle has six areas.…There is identify, question, research,…results, insights, and learn.…
These six areas are not like…the software development life cycle.…It's not a like a waterfall…when each step leads to the next.…Instead, focus on the three areas in the middle as a cycle.…Your data science team should be cycling through…the questions, research, and results.…This cycle of questions, research, and results…will be the engine that drives your data science team.…Each of the three roles on your team…focuses on one of these areas.…The research lead focuses on creating the right questions.…
The data analyst will work with the research lead…
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