In this course, students will go through data science methodology such as hypothesis testing, sampling size, data cleansing and data visualization using R programming. They will go through descriptive statistics, basic probability, random variables, sampling and confidence intervals and hypothesis testing, as well as related ethical considerations and bias in data analysis.​ They will go through various ML concepts such as regression, classification techniques, decision trees, ensemble methods and support vector machines (SVM), as well as applying and evaluating classifiers  in Azure ML.