There are many declinations of Data Science projects: with or without labeled data; stopping at data wrangling or involving Machine Learning algorithms; predicting classes or predicting numbers; with unevenly distributed classes, with binary classes, or even with no examples at all of one of the classes; with structured data and with unstructured data; using past samples or just remaining in the present; with requirements for real-time or close to real-time execution or with acceptably slower performances; showing the results in shiny reports or hiding the nitty gritty behind a neutral IT architecture; and with large budgets or no budget at all.
