top of page


Second Course in Foundations of Data Analytics

No Student Materials
Download Instructor Materials

You must be signed in to download materials.


The Second Course in Foundations of Data Analytics will build a practical foundation for machine learning by teaching students basic tools and techniques that can scale to large computational systems and massive data sets. Topics include algorithms, overfitting and regularization, clustering, anomaly detection, and more. Each module consists of multiple lessons, which each contain a video explaining the lesson content, external reading(s), and included course Jupyter notebooks. Each module also includes a quiz (or assessment) that tests basic mastery of the lesson contents, and a programming assignment that tests synthesis of the lesson contents. Module #1: Introduction to Machine Learning Module #2: Fundamental Algorithms Module #3: Practical Concepts in Machine Learning Module #4: Overfitting and Regularization Module #5: Fundamental Probabilistic Algorithms Module #6: Feature Engineering Module #7: Introduction to Clustering Module #8: Introduction to Anomaly Detection


Robert Brunner

Associate Dean for Innovation and Chief Disruption Officer and Professor of Accountancy and Arthur Andersen Faculty Fellow

bottom of page