The explosion of wireless sensing, smart devices as well as advanced communication techniques have generated the vast amount of big data. While data are the foundation of the Digital Age, Data Analytics is imperative to organize, analyze and interpret the valuable information underlying the massive data. This course provides an overview of advanced analytics algorithms, with applications across various engineering domains, including manufacturing, healthcare, and smart energy. The major topics include model assessment and selection, advanced regression (ridge/Lasso, mixed effects, nonparametric), support vector machine and kernel methods, ensemble learning (stochastic gradient boosting), dimension reduction (subspace learning, non-negative matrix factorization, functional PCA), anomaly detection (unsupervised, high-dimension data), data visualization (multi-dimensional scaling and manifold learning). 3 credits Prerequisites: SSIE 605 Applied Multivariate Data Analysis for SSIE graduate students, prior course work on regression/classification for non-SSIE graduate students, or instructor approval. Fall offering.