Haskayne School of Business, University of Calgary

Calgary, Alberta, Canada

Students will be able to take organizational datasets and form business problems around the data and the business. They can then use statistical and data analytics techniques to explore and model the data to provide insights and solutions to the business problems. The analysis of the data includes data cleaning, exploratory analysis and visualization, and various classification and regression models (Bayes, KNN, Tree-based methods, logistic regression, clustering, SVM, text analytics, ML, and AI). These analyses are mainly done through R programming. A project appropriate for this course ideally includes an introduction to the business and its value chain, and more importantly, a relatively clean dataset (a table is preferred over a database) which usually includes an outcome variable of interest (inputs used to predict an output, e.g. prediction of users' type or demand). Projects without data are not acceptable.