Data science projects are by their very nature experimental and exploratory. It can be very easy when working on a project of this kind to end up with a big mess of spaghetti code that is difficult to decipher or reproduce.
Data science projects are different from traditional software engineering projects in this way. However, it is possible to create a solid code structure that will ensure your project and its results are both reproducible and extensible by yourself and others.
In the following article, I am going to give you a recipe including the tools, processes and techniques, for setting up data science projects that will give you the following:

• A consistent project structure so that your code is easy to follow.
• Version control so that you can track and make changes without breaking the core project.
• An isolated virtual environment so that the project is easily reproducible.
• Ethical and secure projects.

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