Periodically during the semester you will need to get updates from the class repo, so you have to maintain your connection to it. But you also really should be pushing your work somewhere, especially if you wind up getting accused of plagiarism--it's wise to have a paper trail, just in case. What you want is to be able to keep your connection to the class repo, but make it pull-only, and at the same time have a second connection to your private repo, where you can both push and pull to support a typical workflow.

Everything I read online suggested that to fix this situation I needed to build it from source, which meant installing compilers for C++ and Fortran, Cygwin, MinGW, and other stuff, none of which I actually want on that machine. There are Python distributions that already have SciPy working, but if you already have Python installed and are happy you may not want to mess with those. The answer for my situation wound up being that I had to dump the version of NumPy I was using and get the NumPy-MKL package instead, which contains the libraries mentioned in the above errors thanks to Intel's Math Kernel Library. So, here are the steps I eventually found to allow me to use SciPy with my existing Python environment without building it from source.

So why does it matter? Because by expecting a computer science education to make you a good programmer, you are both asking too much and too little of the experience. It is too much to expect that you could learn all these skills in a classroom, and too little to expect that software development would be the sole aim of computer science.

My time in the OMSCS program has taught me to take advantage of every opportunity to stay on top of the Udacity lectures that we are responsible for watching every week. To create another avenue for doing this, I looked for a way to convert those lectures into audio files so that I could listen to them as I commute.