24L/12T
Key areas of data science modeling including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision making. Through a strong emphasis on data-centric computing, quantitative critical thinking, and exploratory data analysis, this class covers key principles and techniques of data science. These include algorithms for statistical models and machine learning methods including regression, classification, neural networks, and clustering; principles behind creating informative data visualizations; and statistical concepts of measurement error and prediction.
Traditional Land Acknowledgement We wish to acknowledge this land on which the University of Toronto operates. For thousands of years it has been the traditional land of the Huron-Wendat, the Seneca, and the Mississaugas of the Credit. Today, this meeting place is still the home to many Indigenous people from across Turtle Island and we are grateful to have the opportunity to work on this land. |