I'm looking for a method to evaluate data science projects and data science teams.I looked for some directions and foundHow to quickly evaluate a data science project which focus on the data understanding, the value of the insight and the execution. It is a bit abstract.Data Science Project Checklist To Use Before You Start A Project To Convey You Can Actually Get Work Done is indeed a checklist, as the name suggests.Data science done well looks easy - and that is a big problem for data scientists is an interesting reading but general.The Joel Test for Data Science is more of what I'm looking for but I don't think that the content of the test is suitable in its current form.Cross Industry Standard Process for Data Mining is a suggested methodology but I don't think it answer my question.From my experience, when you ask on a good project why is it good, the project owner can immediately say so. The answer themselves are quite different (e.g, accuracy level, business values, etc). It seems that these project owners had in mind some definition of a good project while working on it, what helped them execute such a project.When very good project owners present a project, they say why it is good without being asked.
How to evaluate a data science project
methodology