The simplicity of Python along with its robustness makes it very suitable for applications in scientific research. Many students and researchers use Python for their work but writing code for yourself is very different from writing code for the world. Good code can not only accelerate research but also make way for new collaborations.

1. Learn the basics of Git for contributing to an open source Python package - pull, commit, push. 2. Develop a new Python module of your own implementing a scientific model. 3. Follow recommended coding styles and guidelines. The power of unit testing and CI. 4. Documentation and publishing your code online for the world - PyPI and conda-forge ! Produce output and follow through !