I do my own reading for data science, and have my own side projects, but I’ve also taken some data science courses via Pluralsight. The beginner demos are done well, although less informative than the intermediate ones, which are ultimately more useful. For the latter, I typically do simultaneous coding on my own data sets, which helps learn the material.
While developing these demonstrations in logistic regression and neural networks, I used and discovered some interesting methods and techniques:
A few useful commands and packages...:
update.packages() for updating installed packages in one easy actionas.formula() for creating a formula that I can reuse and update in one action across all my code sectionsView() for looking at data framesfourfoldplot() for plotting confusion matricesneuralnet for developing neural networkscaret, used with nnet, to create predictive modelplotnet() in NeuralNetTools, for creating attractive neural network models
Resources that I used or that I would like to explore...
MS Azure Notebooks, for working online with Python, R, and F#, all part of MS's data workflowsEfficient R Programming, that seems to have many good tips on working with RData Mining Algorithms in SSAS, Excel, and R, showing various algorithms in each technologyR Documentation, a high quality, useable resource