A first course in linear algebra designed to instill a working knowledge of the language and operations of linear algebra. This is NOT a rigorous proof-based mathematics course. It is a surface level introduction to the most important definitions and concepts that we will need in order to tackle the more advanced material required for applied data science.

Norms, Bases, Orthogonality, Eigenvalues and Eigenvectors lay the foundation for discussion of Vector Space Models, Principal Components Analysis, and Factor Analysis by Matrix Factorization.

Fast-paced introduction to applied modeling, including supervised and unsupervised techniques, and model evaluation. Dimension reduction, clustering, association analysis, rare event modeling, regularization, decision trees, random forests, gradient boosting, model interpretability (SHAP, LIME, partial dependence, etc.) and many other topics

Network terminology and visualization, descriptive analysis, centrality, community detection, hypothesis testing