Courses & Programmes

With the increased availability of data and cheap and fast computing
power, analyses in many areas of human endeavour have become more and
more data driven. Finance is no exception. Applying machine learning
techniques to traditional finance questions might improve our
understanding.

To date, applying these techniques has been the realm of IT savvy
researchers. With the increased availability of open source software
these techniques are becoming widely available. To fruitfully apply
them, however, finance professionals should get a much better grasp of
their strengths and weaknesses and this requires first hand experience.

Topics covered so far in the Bachelor of Finance – or equivalent – are
of course many. For example, you will have seen derivative valuation
models and corporate finance topics. In this course we aim to revisit
some of these topics with the aim of solving them with open source
tools. This builds on the Python first principles course in the first
block. Then, once we have covered the basics, we will move to the more
advanced topics. This is difficult since there is so much to choose
from. Possible topics are:
– working with large dataset: database management
– high performance computing
– cooperation: GIT