Fundamentals of Data Analytics and Predictions

This course is an introduction to data analysis and prediction techniques and tools.

List of topics

• Introduction to Statistical Learning

• Linear Regression, Logistic Regression, Linear Discriminant Analysis

• Cross-validation, the Bootstrap

• Model selection & Regularization (Ridge and Lasso)

• Dimensionality reduction methods

• Non-linear Models (Polynomial regression, Splines, Generalized additive models)

• Tree-based methods (Trees, Bagging, Random Forests, Boosting)

• Support Vector Machines

• Intro to Neural Networks

• Unsupervised Learning (Clustering Methods)

Required textbook

• An Introduction to Statistical Learning: With Applications in R, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

Recommended book: 

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie and Robert Tibshirani 

Prerequisites

PH1700-Intermediate Biostatistics and good practical R programming skills