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
Skill Level Appropriate for all
- Working as part of a data science team requires a very different mindset. Most organizations still rely on planning, objectives and concrete deliverables. Your data science team needs to question, explore and react. The science, in data science, refers to using the scientific method. This scientific method creates a loop of discovery, your team will ask interesting questions, and then you'll research those questions to come up with new insights. That's a pretty big disconnect from how most organizations view their work.
For a data science team to be successful you'll have to rewire how your organization thinks about the work, you have to shake loose the notion of planning and delivering and replace it with the notion of exploring and discovering. The first thing you have to do is communicate what makes data science different. In this course you'll see how data science teams work differently from a typical project. Then you'll see how traditional notions of planning and delivering don't work well for data science.
Finally, you'll find out how to use a data science lifecycle to deliver real business value in team sprints. Whether you're a longtime project manager or a first-year developer, this course will help you thrive in this new field. So let's get started delivering in data science sprints.