Week 1: Finding features
Introduction to setting up a feature engineering workflow, which includes identifying problems of practice, relevant research, and brainstorming potential features.
Week 2: Data wrangling and visualization
Introduction to data wrangling, data visualization techniques, and structure discovery algorithms. Integrating theory, knowledge from practice, logic, and contextual factors into feature engineering will also be discussed.
Week 3: Modeling features
Introduction to using features within selected machine learning algorithms (e.g. logistic regression and decision tree) and the tradeoffs between interpretability and prediction.