# ðŸ“Š Introduction to Statistical Learning in R

## Table of Contents

## Description

This course offers an introduction to statistics and supervised machine learning. It adopts a problem-to-solution teaching approach, defining a practical problem and illustrating how statistics can enable understanding to make critically informed decisions about a population by examining a random sample. It uses a learning-by-doing approach based on real-world examples in various contexts. This also teaches how to conduct statistical data analysis in R. The course is organised around 6 sessions. Each session is designed to provide a combination of key statistical concepts and practical application through the use of R.

## Learning outcomes

Having successfully completed this course, you will be able to:

- Conduct exploratory statistical data analysis.
- Have an understanding of elementary probability distributions and data types.
- Perform correlation and regression data analysis using real-world data.
- Assess the statistical significance between different data types.
- Carry out statistical data analysis in R.
- Have a basic understanding of supervised machine learning and cross-validation.

## Structure

The notes for each session are:

Session 1

**Introduction to R**: Data types & probability distributionsSession 2

**Descriptive Statistics**: Measures of centrality & dispersion for continuous & categorical dataSession 3

**Statistical Significance**: Hypothesis testing & confidence intervalsSession 4

**Correlation**: Correlation visualisation & measuresSession 5

**Regression Analysis**: Linear regression, dummy variables & logistic regressionSession 6

**Supervised Machine Learning**: Tree Regressions, Random Forest & Cross-validation