How To Structure Your Machine Learning Project

Data science juniors often focus on understanding how libraries like Scikit-Learn, Numpy, and Pandas work. Many MOOCs push a lot on concepts that revolve around the latter, leaving out the management component of a data science project. As much as a junior may know about algorithms, libraries and programming in general, the success of a project is also related to its structure. A confusing structure can impact significantly on the performance of the analyst, who must continually orientate himself among a huge amount of files and TO-DOs. This is even more emphasized if more people are involved in the project.