Data-driven analytics methodologies are presently at the forefront of efficient decision making and decision support in many industries. One prominent set of examples of this state-of-the-art computational data science and optimization tools that made headway is optimizing energy generation, storage, transmission and delivery, and trading. In a sense, nowadays, the employment of computational data science methodologies form a necessary condition for such systems to remain sustainable in the  long-run and thrive, as we make a global transition to knowledge-and- information-based economy. These applications spread from operational to strategic time horizons. To name a few, optimization combined with machine-learning methods is successfully used to improve the efficiency SAGD process in oil recovery; optimization models and methods play a key role in determining efficient energy storage and dispatch strategies for smart grids, as well as help determine effective layouts for wind and solar farms; quantitative modelling and optimization occupy a central role when trading (energy) financial derivatives. However, despite these recent advances there is still a large gap between the most recent and vastly superior analytical tools available, and their practical applications.

The focus of the winter school is to train a new batch of highly qualified personnel that are essential in bridging the existing industry-to-academia gap. In addition, bringing together optimization thinking with more traditional data science approaches will help generate ideas for new approaches or improvements in existing approaches.

Paul McNicholas (McMaster University / MacDATA Institute, Canada) and Yuriy Zinchenko (Gurobi Inc. / University of Calgary) will give 5 half-days lectures on Computational Data Science and Optimization with Paul focusing on the Data Science aspects and Yuriy focusing on the optimization aspects.