Join Doug Rose for an in-depth discussion in this video Compare project challenges, part of Delivering in Data Science Sprints.
- You've seen how traditional project management…relies on requirements and careful planning.…Remember that a typical project…has scope, cost, and schedule.…This isn't really compatible with the scientific method…used within data science teams.…Instead, data science teams are empirical and exploratory.…If you insist on a plan then you're boxing…the team into looking for what they already know.…It's tricky to imagine a team finding…new insights in a well-defined box.…If you think about the meetings in most organizations,…they're usually around planning…and hitting objectives.…
The language of most organizations…still hinges on phrases such as…mission, objectives, and outcomes.…It's difficult to step back and imagine…a team of pure exploration.…For most organizations, working with a data science team…will be a difficult transition,…so let's look at a typical project…and compare it to that of a data science team.…Then let's imagine what would happen…if you started applying planning and objectives.…
Let's start with a typical software project.…
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