Personal Learning Notes · BRUR · Statistics

Learning Notes

A single, clean hub for every course notebook and programming guide on this site. Pick a category below, then click any card to jump straight to that topic — no digging through menus required.

🎓 14 B.Sc. Courses 🎓 2 M.Sc. Courses 💻 R, Python & SQL Guides

How this page works: each card below links directly to that course's or topic's own page (course cards jump straight to the right section of the B.Sc./M.Sc. notebook; programming cards open a full guide with its own sidebar of sub-topics).

B.Sc. Courses

13 courses

Undergraduate statistics — foundations through to multivariate analysis, econometrics, and research methodology.

STAT1101 / 1201

Principles of Statistics I & II

Central tendency, dispersion, index numbers, time series, correlation & regression basics.

Probability Theory

Set theory, axioms, counting rules, conditional probability & Bayes' theorem.

Probability Distributions

Bernoulli, Binomial, Poisson, Normal, Exponential, Gamma & Beta distributions.

Regression Analysis & Diagnostics

OLS, ANOVA for regression, residual diagnostics, influential points, logistic regression.

Sampling Distribution

Sampling distribution of the mean, CLT, chi-square, t and F distributions, estimation.

ANOVA & Design of Experiment

One/two-way ANOVA, post-hoc tests, CRD, RBD, LSD, factorial & 2^k designs.

Hypothesis Testing

Neyman-Pearson lemma, UMP tests, likelihood ratio tests, p-values, non-parametric tests.

CLRM, multicollinearity, heteroscedasticity, autocorrelation, dummy variables, time series.

Multivariate Distribution

Mahalanobis distance, multivariate normal, MLE, Hotelling T-squared, MANOVA.

Multivariate Analysis II

PCA, ICA, factor analysis, cluster analysis, discriminant & classification analysis.

Sampling Techniques

SRS, stratified, systematic & cluster sampling, ratio/regression estimation, PPS.

Categorical Data Analysis

Contingency tables, chi-square tests, odds ratios, logistic & log-linear models.

Research Methodology

Research design, measurement & scales, validity & reliability, ethics, report writing.

M.Sc. Courses

2 courses

Postgraduate coursework — a growing collection, currently covering genomics and signal processing.

Statistical Genomics & Bioinformatics

Linkage analysis, QTL mapping, central dogma, LOD scores, differential gene expression, genome sequencing.

Statistical Signal Processing

Signal classification, sampling & digitisation, estimation theory, ML & EM, CRLB/BLUE, Bayesian estimation, detection theory.

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More M.Sc. courses coming soon

check back for updates

Programming

3 languages

Full learn-by-example guides for R, Python, and SQL, each with its own set of topic pages, runnable code, and output.

LEARN BY EXAMPLE

Learn R Programming

Getting started, data structures, control flow, functions & the apply family, import/export, base graphics, ggplot2, and research-ready descriptive statistics & regression.

LEARN BY EXAMPLE

Learn Python Programming

Getting started, data structures, control flow, functions & comprehensions, import/export, NumPy & pandas, Matplotlib/Seaborn, and research-ready descriptive statistics & regression.

LEARN BY EXAMPLE

Learn SQL Programming

Queries, joins, aggregations, subqueries, window functions, and database design for research and data analysis workflows.