01. Foundational Concepts
Big data analytics, machine learning and artificial intelligence concepts; Data, statistics, and probability; Distributions; Confidence intervals
02. Basic Regression Analysis
Linear regression; Understanding regression statistics; Non-parametric regression
03. Multivariate Statistics
Dimension reduction; Cluster analysis; Data visualization
04. Machine Learning Basics
Overview of techniques; Evaluating model performance; Variable importance; Model aggregation
05. Machine Learning for Regression and Classification
Classification/regression trees; Random forest; Gradient boosting machine; Support vector machine; Neural networks; Deep learning
06. Miscellaneous Topics and Wrap-Up
Experimental design and response surface analysis; Uncertainty quantification; Selected literature review; Key takeaways and resources; Data analytics dos and don’ts