I strongly recommend Georgia Tech's ISYE6501X course, "Introduction to Analytics Modeling" - available at edX.org[0] The professor, Joel Sokol, is a masterful lecturer with excellent pacing and a great intuition for guessing what the audience will be thinking at any given point in a lecture, and quickly addressing those thoughts.

The curriculum of the course covers foundational knowledge requisite for machine learning, data analytics, big data. The course does a fantastic job of breaking down how to select the most appropriate method for any given situation you might run into, and how to critically compare the results of various methods to see which worked best.

Personally I feel this material should be included in every STEM major's undergraduate coursework. And I'm usually displeased with the quality of online MOOC's; this particular course truly stands out.

The actual topics discussed include:

Support vector machines (SVM), classification, clustering, principal component analysis (PCA), Bayesian modeling, exponential smoothing (ARIMA, GARCH), decision trees, Markov chains, k-mean, k-nn, Q-Q plots, probability distributions, and graph analysis. Software used included R, Python, and Rockwell’s Arena Simulation Software.

0: https://www.edx.org/course/introduction-to-analytics-modelin...