[HD] STAT5003 Notes | Weeks 1–13 | Formulas, R Workflow & Exam-Style Practice

Description

Written by a student who achieved a High Distinction (HD), this 33-page independent study guide brings the main STAT5003 Computational Statistical Methods topics together in one focused revision PDF. It covers: - descriptive statistics, data types and reproducible R workflows - regression, inference, smoothing and density estimation - k-means, hierarchical clustering, PCA, t-SNE and MDS - classification methods, including logistic regression, LDA, kNN and SVMs - cross-validation, confusion-matrix metrics, ROC/AUC and model evaluation - bootstrap, missing data and class imbalance - subset selection, ridge, lasso and elastic net regularisation - decision trees, pruning, bagging, random forests and boosting - Monte Carlo methods and Markov chain Monte Carlo The PDF includes a key-formula reference, method-comparison tables, R workflow/code references, worked-example templates, exam tips and revision prompts organised by week. Format: PDF, 33 pages. Scope: Weeks 1–13. Assessment context is included only as study context; verify current requirements independently before relying on it. Independently authored study material. Not official University of Sydney material and not affiliated with or endorsed by the University.