Statistical instruments you can turn, break, and read.
Each lab isolates one idea from the DSPA3 textbook that is easy to state and easy to get wrong — then hands you the controls that make the consequence obvious. Everything computes in your browser in TypeScript: no R, no installation, no server.
- 8
- interactive labs live
- 42
- labs mapped to the textbook
- 14
- chapters, 9 clusters
- 0
- bytes of your data uploaded
Predict, then observe
Every lab opens with a prompt whose most popular answer is wrong. The reveal comes from the instrument, not from prose.
Numerics you can inspect
Seeded RNG, QR and SVD, coordinate-descent LASSO, ROC and calibration — all pure TypeScript, all deterministic.
Every state has a URL
Found a pathological case? Copy the permalink. It reopens the exact scenario, controls and all.
Built for two audiences
Students get guided sequences and an R bridge back to the book. Teachers get presets, prompts, and teacher mode.
Start with the flagships
One lab per major theme, each carrying the misconception its chapter exists to correct.
Sampling Distributions & the Limits of the CLT
Separate the distribution of the data from the distribution of a statistic, and find where the central limit theorem stops applying.
Missingness Mechanisms: MCAR, MAR, MNAR
Delete data three different ways and watch which repairs work, which fail, and why more data never fixes a biased mechanism.
Conditioning, Collinearity, and Unstable Coefficients
Watch the condition number of a design matrix explode and see exactly which quantities become unidentifiable — and which do not.
PCA Geometry: Projection, Scale, and What Variance Means
Rotate a data cloud, change a measurement unit, and see principal components move — the projection is geometry, not magic.
Accuracy Is a Trap: Metrics Under Class Imbalance
Move a threshold, move the prevalence, and watch which performance numbers stay honest and which quietly lie.
Cluster Geometry: k-means, DBSCAN, and Shapes That Break Them
Compare centroid and density clustering on blobs, moons, and pure noise — and see why the elbow plot cannot tell you k.
Calibration Is Not Discrimination
Distort a model's probabilities without touching its ranking: AUC does not move a decimal place while every probability becomes wrong.
LASSO Paths, Cross-Validation, and False Discoveries
Trace coefficients from saturated to empty, then discover that the cross-validated λ is tuned for prediction — not for finding the right variables.
Nine clusters, fourteen chapters
Foundations & Data Quality
Ch 1, 2Simulation, reproducibility, floating point, missingness mechanisms, robustness, and multiplicity.
2 live · 8 mapped
Mathematical Core
Ch 3, 4Matrix computing, conditioning, projection geometry, and linear/nonlinear dimensionality reduction.
2 live · 6 mapped
Supervised Learning
Ch 5, 6Bayes error, kNN boundaries, metric families, leakage, kernels, ensembles, and backpropagation.
1 live · 6 mapped
Unsupervised Learning & Text
Ch 7, 8TF-IDF geometry, association rules, k-means, DBSCAN, spectral clustering, and mixtures.
1 live · 5 mapped
Validation & Feature Selection
Ch 9, 11Resampling, calibration versus discrimination, decision curves, LASSO paths, and knockoffs.
2 live · 6 mapped
Systems & Performance
Ch 10Columnar formats, chunking, Amdahl's law, and streaming computation in the browser.
0 live · 2 mapped
Temporal & Longitudinal
Ch 12Autocorrelation, stationarity, filtering, survival curves, and forecast validation.
0 live · 3 mapped
Optimization
Ch 13Loss landscapes, gradient descent variants, constraints, and Bayesian optimization.
0 live · 3 mapped
Deep Learning
Ch 14Backpropagation, convolution, sequence models, generative models, and generalization.
0 live · 3 mapped