This course introduces students to the theory, principles and applications of mathematical and computer modeling of data as applied to cities. It will be based on two unified themes: foundations for predictive analytics and decision-making followed by applications in data science. The 1st half of the course will cover predictive modeling using a wide array of examples, including predictive modeling, an advanced treatment of regression, visualization and graphics, and automated analysis for high dimensional data. The second half will introduce students to applications in data science such as analytics of images and video as well as subjective data processing and analysis.

• OLS
• Hypothesis testing
• PCA
• Kmeans
• Gaussian Mixture
• Logistic Regression
• Ridge and Lasso regression

1. Proofs and inductions for linear regression link
2. Proofs and inductions for multilinear regression link
3. Hypothesis testing, feature selection using ACS data link
4. Visualization of clustering techniques and hyperparameter tuning using silhouette scores link
5. Ridge and Lasso link
6. Sample midterm link
7. Final project link