A simple life cycle of Data Science activities
Artificial Intelligence. Data Science. Magic! Businesses struggle to understand the life cycle of Data Science activities. They miss the key supporting activities required for Data Science success. They impose unreasonable restrictions and bureaucracy on what is an inherently iterative approach. Their engagement models do not accommodate the inevitable disruptions and resets of progress. Below are the key activities in the Data Science life cycle. What is obvious is the highly iterative nature of these activities and how early activities often need to be revisited based on the outcomes from later activities. Guerrilla Analytics is a set of principles and practice tips that allow your Data Science teams to embrace these iterative activities while always progressing towards algorithms in production – the ultimate goal of data science projects.