Statistics with R, zero to expert

🧮

R Basics

The console, vectors and functions: the three things every R script is made of.

8 lessons · 94 min · Beginner

Start

🗂️

Data Structures

Types, factors, lists and data frames: how R stores data and how you get at it.

8 lessons · 95 min · Beginner

Start

🔁

R Programming

Conditions, loops, functions and the apply family: turning one-off console work into code that runs on its own.

8 lessons · 89 min · Beginner

Start

🧹

Data Import and Cleaning

Reading files, fixing types, dates and missing values, and reshaping until every row is one observation.

8 lessons · 93 min · Beginner

Start

🔗

dplyr and tidyr

Six verbs, the pipe, joins and reshaping — the grammar behind almost every R data pipeline.

8 lessons · 89 min · Beginner

Start

📈

ggplot2

The grammar of graphics: build any plot from data, aesthetics, geoms, scales, facets and themes — and know what it can hide.

8 lessons · 87 min · Beginner

Start

📊

Descriptive Statistics

Centre, spread, shape, quantiles, outliers and grouped summaries — and why quantile() disagrees with Excel.

8 lessons · 90 min · Intermediate

Start

🎲

Probability Distributions

The d/p/q/r functions for normal, binomial, Poisson, exponential, uniform, t, chi-square and F — plus simulation and normality checks.

8 lessons · 91 min · Intermediate

Start

🎲

Sampling and the Central Limit Theorem

Why one sample can stand in for a population, how far its mean can be off, and what shrinks that error.

8 lessons · 96 min · Intermediate

Start

📏

Confidence Intervals

Turning a standard error into a range for the mean, a proportion or a difference — and reading that range correctly.

8 lessons · 95 min · Intermediate

Start

⚖️

Hypothesis Testing

Null and alternative, the p-value and its three wrong readings, errors, power, multiple comparisons and p-hacking — every claim checked by simulation.

8 lessons · 95 min · Intermediate

Start

🔬

t-Tests

One-sample, Welch, Student's and paired t-tests, their assumptions, Cohen's d, confidence intervals and what to do when the assumptions fail.

8 lessons · 94 min · Intermediate

Start

📐

ANOVA

One-way and two-way aov, the F statistic by hand, assumption checks, Tukey HSD, interactions, eta-squared and the Kruskal-Wallis fallback.

8 lessons · 97 min · Intermediate

Start

🔲

Chi-Square Tests

Tables in R, goodness of fit, independence, the expected-count rule, Yates and Fisher, standardized residuals, Cramér's V and McNemar for paired data.

8 lessons · 91 min · Intermediate

Start

🪜

Nonparametric Tests

Wilcoxon, Mann-Whitney, Kruskal-Wallis, Spearman, the sign test and a hand-built permutation test — what ranks buy you and what they cost.

8 lessons · 95 min · Intermediate

Start

🪢

Correlation

Covariance, Pearson, Spearman and Kendall, cor.test, matrices with missing values, partial correlation — and the four ways a correlation lies about causation.

8 lessons · 94 min · Intermediate

Start

📈

Simple Linear Regression

Least squares by hand and with lm(), every line of summary(), intervals for coefficients and predictions, R² and its limits, assumptions and log models.

8 lessons · 96 min · Advanced

Start

🧮

Multiple Regression

Several predictors at once: confounding, factors and dummies, interactions, centring and polynomials, nested-model tests, NA handling, predict() and reporting.

8 lessons · 96 min · Advanced

Start

🩺

Regression Diagnostics

Residual patterns, plot(fit), transformations, Breusch-Pagan and VIF by hand, leverage, Cook's distance and autocorrelation — and what to do about each.

8 lessons · 106 min · Advanced

Start

⚖️

Model Selection

Overfitting by simulation, train/test splits, k-fold cross-validation by hand, AIC and BIC, step() and its dangers, nested F tests, and choosing by purpose.

8 lessons · 105 min · Advanced

Start

🎯

Logistic Regression

glm(family = binomial), odds ratios, predicted probabilities, thresholds, ROC by hand, deviance tests — and what a coefficient of −4 actually means.

8 lessons · 100 min · Advanced

Start

🔢

Count Models

Poisson regression with the log link, rate ratios, offsets for exposure, overdispersion, quasi-Poisson, and the negative binomial and zero-inflation ideas.

8 lessons · 98 min · Advanced

Start

🪺

Mixed-Effects Models

Clustered and repeated-measures data: the ICC, random intercepts, aov with Error(), shrinkage by hand and what lme4 adds.

8 lessons · 100 min · Advanced

Start

📈

Time Series Basics

ts objects, decomposition, moving averages, differencing, autocorrelation and exponential smoothing, from AirPassengers to your own dated data.

8 lessons · 102 min · Advanced

Start

📈

Forecasting

Time-ordered splits, naive baselines, Holt-Winters, ARIMA, prediction intervals, error metrics and rolling-origin backtesting in base R.

8 lessons · 102 min · Advanced

Start

🧭

Principal Component Analysis

prcomp(), scaling, eigenvalues, loadings, scores, the biplot, reconstruction by hand and when PCA fails.

8 lessons · 98 min · Advanced

Start

🫧

Clustering

Distances, k-means, the elbow and silhouette, hierarchical clustering and dendrograms — and how to tell whether the clusters mean anything.

8 lessons · 95 min · Advanced

Start

🌳

Trees and Random Forests

Gini and entropy by hand, the best split, growing and pruning a tree, bagging, out-of-bag error and the random forest idea — built in base R before you call rpart or randomForest.

8 lessons · 94 min · Expert

Start

🎲

Simulation and Bootstrapping

Monte Carlo estimation, bootstrap intervals, permutation tests and power by simulation — inference when there is no formula, and checking the formulas when there is.

8 lessons · 97 min · Expert

Start

🏁

Capstone Project

One data set, start to finish: question, plan, cleaning, model choice, diagnostics, validation, effect sizes, a clear table and plot, and a reproducible report.

8 lessons · 99 min · Expert

Start