Join Doug Rose for an in-depth discussion in this video Next steps, part of Learning Data Science: Using Agile Methodology.
- In this course we introduced you to how deliver insights with a data science lifecycle, while working in sprints. In this course, you saw the three key roles. There was the research lead, data analyst, and project manager. Then you saw how these roles divided up key characteristics of the data science mindset. The research lead was focused on asking good questions. The analyst created interesting reports. The project manager communicated the insights to the rest of the organization. You can also see more examples in in-depth information in my book: Data Science: Create Teams That Ask the Right Questions, And Deliver Real Value.
It's available through Apress Publishing, and in most online book stores. I hope you enjoyed this course on delivering in data science sprints. Feel free to follow me on LinkedIn. There you'll see more examples of visualizations and strategies that you might try with your data science team. Thank you for watching, and good luck with your career.
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