A Step-by-Step Guide to Failing a Data Science Project

Practicing data science and working with real-world data and business problems is rather different than, for instance, building data science projects in Python using toy datasets. While being a part of a data science team in an enterprise, one should expect many challenges, including messy data, lack of data, unclear goals, difficult communication with business managers who want quick results, model performance in production being very different from testing performance, etc. Therefore, to become a successful data scientist with a portfolio of outstanding projects, it is not enough to be good at coding and building machine learning models. One should further be able to approach a project strategically and consider many different factors, not only from the viewpoint of a data scientist but also from a business perspective. However, what if you are actually not interested in succeeding in data science? In that case, read carefully through the tips provided below.