Week one: Coursework and Preparation
Monday, June 14th - Friday, June 18th, 2020

Possible course topics:

Numerical methods for Data Science 
Machine Learning 
Visualization techniques for data science
Elements of Mathematical Finance
Bootstrapping Methods

Possible topics and types of problems participants will encounter:

Machine learning, classification, regression models, data visualization, data cleaning, optimization, data modelling, modelling predictive uncertainty, spatial data.


What to expect?

Prior to the workshop our team of researchers will select the problem, determine its main objectives and identify the potential techniques to address the problem within the limited timeframe allowed and according to the general knowledge-base and abilities of the participating students. 

Coursework will involve an overview of relevant techniques. Emphasis will be placed on mathematical assumptions being made and the consequences of working with incorrect assumptions, as well as remedial measures, and proper interpretation of results.  Computational aspects will be discussed, and software used to implement the methods, incorporating illustrative examples that would be directly or indirectly related to the project at hand. 

During the morning sessions students will participate in three courses that will meet daily (one hour each). In the afternoons groups will be formed, meetings with the client will be held and support sessions will be offered to support the students in understanding and applying the new concepts being taught in the morning courses. 

An informal series of conversations and interviews with academic and industrial clients will also occur in the afternoons. The focus will be on best practices for collaboration and working with clients in the context of research. These sessions will offer a glimpse into what works and what doesn’t and the invaluable role that effective communication plays in these situations.