1) Creating a reproducible computational environment with Docker.
2) Interactive analysis and literate programming with the IPython notebook.
3) A brief survey of the fundamental scientific Python packages: numpy, matplotlib, scipy, sympy, pandas, nose.
4) Writing efficient, compiled C/Python hybrid code with Cython.
5) Wrapping C and C++ libraries in python with XDress.
Many of the content provided here is borrowed directly from these outstanding series:
Thanks to the wonderful SciPy community!