title: How to use git for Data Science/ ML projects
body: <p>Three of my fellow students and I take part at a hackathon and have 10 days. We obtained a data set and have to predict a variable. However, I'm wondering what's the best way to use git for such projects. We have two main directories <em>model</em> and <em>data</em>. Being a data science project, a lot of data preprocessing is required. We're using jupyter notebooks both for the data cleaning and for the model itself. How can we efficiently clean the data working as a team in terms of  git-repo-structure? I am not sure how to manage the repo properly. Should everyone have his own branch? Or is it more convenient if everyone works on the master branch and pushes the updated data set and jupyter notebooks? Because there is no &quot;running system&quot; that is usually in the master branch. We're experimenting a lot with the data and the model. Does it even make sense to use git? Sorry for that chaotic question though</p>