Applying Agile Framework to Data Science Projects
Agile principles and values can be applied to the way you approach data science projects
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Project management methodologies are commonly used to get projects done or a product (or referred as a tool) produced. They are, in general, processes and frameworks which break down the overall objective to individual tasks organised on a timeline. This can be adapted to approach data science projects.
In the past, the traditional Waterfall methodology (dated way back to 1970) has been very popular. It defines all requirements and parameters of the product at the start, so that the project team can work towards this target in sequential phases. This method has been very suitable for the manufacturing industry where product specifications seldom vary with time. It requires very extensive upfront planning, and ideally, the output product is exactly the same as specified in the beginning.
When the Waterfall methodology is becoming unsuitable, many popular project management methodologies have emerged over the years, especially in the software development industry. Let me share the most popular one.