Join Doug Rose for an in-depth discussion in this video Rely on serendipity, part of Learning Data Science: Using Agile Methodology.
- You've seen that it can be difficult…to have clearly defined objectives…with your data science team.…Still many organizations find it difficult…to even imagine working without clear objectives.…You see objectives everywhere.…Almost every management book talks about…how to set objectives.…You set career objectives.…Even when you're training,…there are clearly defined learning objectives.…These objectives guide much of what we do,…but they might not be as valuable as you think.…There's been some interesting work…in this area over the last few years.…
You'd think that questioning objectives…would come from some new age social science,…but it's actually coming from where you'd least expect.…It's coming from the world of machine learning…and artificial intelligence.…These are the same people who are…working to have computers display intelligent behavior.…They're finding that so much of what we know…is based on unplanned discovery.…We actually learn more from our wandering…than we do from our set objectives.…One of the best books on this topic is…
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