24h Pro data science in R

About 24h Pro data science in R

This course explores several modern machine 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 package

Data processing using sqldf, caret, etc.

Unsupervised techniques such as PCA, DBSCAN, K-means

Calling Deep Learning models in Keras(Python) from R

Use the powerful XGBOOST method for both regression and classification

Doing interesting plots, such as geo-heatmaps and interactive plots

Train ML train hyperparameters for several ML methods using caret

Do linear regression in R, build log-log models, and do ANOVA analysis

Estimate mixed effects models to explicitly model the covariances between observations

Train outlier robust models using robust regression and quantile regression

Identify outliers and novel observations

Estimate ARIMA (time series) models to predict temporal variables Most 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.