This course explores several modernmachine learning and data science techniques in R. As you probably know, R is one of the most used tools among data scientists. We showcase a wide array of statistical and machine learning techniques. In particular:Using R\'s statistical functions for drawing random numbers, calculating densities, histograms, etc.Supervised ML problems using the CARET packageData processing using sqldf, caret, etc.Unsupervised techniques such as PCA, DBSCAN, K-meansCalling Deep Learning models in Keras(Python) from RUse the powerful XGBOOST method for both regression and classificationDoing interesting plots, such as geo-heatmaps and interactive plotsTrain ML trainhyperparameters for several ML methods using caretDo linear regression in R, build log-log models, and do ANOVA analysisEstimate mixed effects models to explicitly model the covariances between observationsTrain outlierrobust models using robust regression and quantile regressionIdentify outliers and novel observationsEstimate ARIMA (time series) models to predict temporal variablesMost of the examples presented in this course come from real datasets collected from the web such as Kaggle, the US Census Bureau, etc. All the lectures can be downloaded and come with the corresponding material. The teaching approach is to briefly introduce each technique, and focus on the computational aspect. The mathematical formulas are avoided as much as possible, so as to concentrate on the practical implementations.This course covers most of what you would need to work as a data scientist, or compete in Kaggle competitions. It is assumed that you already have some exposure to data science / statistics.