We used SCIKIT for an entire year and while it works for demos with small data (except Neural Networks code incomplete) we discovered that for realistic applications we could not train the sample data set.
We did have a full installation with Anaconda, the account you were given had access accordingly. We installed everything!
I give you examples why we dropped its usage.
For SVM/SVR it provides access to limited KERNELS which are not producing accurate enough forecasts, so we wanted to add our own WAVELET KERNEL and it was a nightmare! Finally a world class Python programmer gave up!
For Knn we wanted to use our own metrics or other known metrics, again same.
No parallelization support, therefore for large datasets it is useless.
NO SYMBOLIC COMPUTATIONS at all! This is a huge issue when one does algorithms and needs to wrestle the algebras.
Neural Network is still not supported which is a big problem.
For professional use and serious research, the kinds John requires I do not recommend SCIKIT. If you want to teach classes for undergrads, maybe a good option.
So we decided to write our own code and contribute to their source tree, and it looked like there are political issues amongst the organizers and certain developments are blocked e.g. Neural Networks. We found their API interface cumbersome for quick contributions.
Finally I switched to Mathematica and C.
I AM WILLING TO PROVIDE THE PYTHON ANACONDA INSTALLATIONS REQUIRED FOR SCIKIT, if a competent programmer could show some serious results, but personally I cannot contribute with SCIKIT