Function Callers Vs Data Scientists
I've done my Masters in Analytics from National University of Singapore.

The answer which I'm going to give is based on my experience.

Ofcourse all the topics are important in the above mentioned list but if you need to prioritize from the topics which you have mentioned above I think the following are very important for any person who is working in a Data Science field. :


Various Probability and Data Distribution: Here you can give basic intro on Probability. You don't need a separate Probability class. A bit more its applications.
Single variable calculus/Multi variable calculus: There are very important, when you try to forecast something this plays an important role.
Graphical Models: Yes, these are helpful in implementing all kinds of community detection algorithms, social networking and many more.
Bayesian Network: This is one of the basic techniques which is important and should be known by all statisticians.
Statistical Models: They need to be exposed to different statistical models as you know there are many models which can perform better in different scenarios, It comes only by practice. 


Rest are not so important at the start, you can conduct another session if necessary based on the outcome of the 1st session. Where you can cover all the topics like Linear Algebra (advanced),  Vector calculus, Optimization techniques, Maximum likelihood methods. As you know these techniques would come into play after getting the basic models ready and you would use these if you want to improve the accuracy of the model or tweaking the model WRT to your Business Problem.

I hope this answer may help you.