The persuasion of a masters degree has for the last two years has exposed me to scientific computing on a regular basis. In light of that experience, scientific computing demands a distinct way of information representation, interpretation and processing. While general purpose programming languages and platforms are capable of getting the job done, a specialized scientific computing platform allows the scientist to be the scientist more often than being the programmer.
There are several scientific computing platforms. MATLAB is perhaps the most widely known proprietary option. There are a few open source alternatives too, R is one example. My favorite used to be MATLAB. That changed about two years ago when I started working in Python.
In my experience the following list of Python packages and modules comprise of a satisfying scientific computing environment. The libraries are listed bellow:
Core Python Packages/Modules:
sysFor passing command line arguments to your python script
timeFor timestamping or for benchmarking
stringRead and write text in different formats including parsing
mathA very handy library for, obviously, mathematical operations. This library has several built in statistical functions too.
randomAllows creating of randomized samples from various probability distribution functions.
Third Party Packages/Modules
Qt. It comes with a standard set of widgets as well as the option of making custom ones. Meets the need for smart clean GUI for Scientific computing. PyQt has a good chance of becoming one of the leading GUI library for scientific and advanced computing. This video shows an impressive GUI for particle system in Maya.
PyQt GUI for Maya from Tim Withers on Vimeo.