Choosing Problems in Data Science and Machine Learning
Read OriginalThis article provides a framework for data science and machine learning leaders to prioritize team projects. It explains how to use cost-benefit analysis to quantify potential returns, discusses evaluating problem severity and impact, and categorizes solutions from incremental to disruptive. It also covers common pitfalls in the problem-selection process.
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