BUSQOM 0102 - BUSINESS ANALYTICS 2
Minimum Credits: 3
Maximum Credits: 3
BA II builds on the previous two courses to focus on predictive analytics. The first half of the course covers two foundational techniques: linear (for continuous variables) and logistic (for categorical variables) regression. In addition to understanding the statistical approaches and assumptions behind them, particular emphasis is placed on (i) how to select and create new independent variables (including using data visualization); and (ii) how to avoid overfitting and evaluate model performance on out-of-sample observations. In the second half of the course, students will move beyond statistical and regression analyses and be introduced to some of the most popular machine learning algorithms. Examples include Classification and Regression Trees (CART); recommendation systems, using Association Rules; Social Network Analysis; and Natural Language Processing (e.g., for Sentiment Analysis). This list of topics will be frequently updated to ensure that students are exposed to techniques that are currently valued in business environments.
Academic Career: Undergraduate
Course Component: Lecture
Grade Component: Letter Grade
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