PyCon: why data scientists should learn mathematical optimisation

In November I was fortunate to have the opportunity to present at PyCon AU in Melbourne, Australia. I was really excited to give a talk about why data scientists should be aware of the kinds of problems mathematical optimisation was really good at solving.

The recording of my presentation is below, but a few of my main points were:

• There are three forms of analytics:
  • Descriptive
  • Predictive
  • Prescriptive
• Machine learning falls into the predictive category, whereas mathematical optimisation falls into the prescriptive analytics category.
• Machine learning and mathematical optimisation are enormously complementary – you’ll often take the predictions of a machine learning model and use them as the inputs to a mathematical optimisation which will tell you the optimal decisions you should make, while subject to constraints.