Dec 2018 - Feb 2019
I started with Practical Statistics for Data Scientists. This was a great way to think about the math, visualize it and get introduced to Python/R.
Start with the definition of a mean and terminology like data frame, feature and outcomes.
Move through statistics concepts like boxplots, scatterplots, central limit theorem, binomial distribution and significance testing including P-values.
There's an entire chapter dedicated to Machine Learning algorithms like K-Nearest Neighbors, Bagging and Random Forests, Boosting and more.
Pro tip: Focus on visualizing.