From the course: Learning Data Science: Using Agile Methodology
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Loop through questions
From the course: Learning Data Science: Using Agile Methodology
Loop through questions
- 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…
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