Handouts
Full class walkthroughs, as single-page documents and as notebooks to run. Each one renders every figure from the actual data, ships the code to reproduce it, and includes solved exercises.
They complement the class pages rather than replace them: each goes deep on one topic and works both as a companion during class and as a reference afterwards.
Written in Portuguese
The handouts are course material for the in-person classes and are written in Brazilian Portuguese. The code, the library names and the figures are language-independent.
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Exploratory Data Analysis
California Housing — 20,640 districts from the 1990 Census.
Integrity audit, univariate analysis for numeric and categorical variables, all three pairings (numeric × numeric, categorical × numeric, categorical × categorical), multivariate and geospatial analysis. Includes an interactive skewness/kurtosis simulator.
~3 h · 19 figures · 10 exercises
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Normalization and Transformation
Titanic — 891 passengers, 866 missing values.
Missing data and the MCAR / MAR / MNAR mechanisms, a decision diagram of techniques by variable type, scaling, shape transformation, categorical encoding, and leakage measured across four scenarios.
~3 h · 10 figures · seven-checkpoint exercise
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Dimensionality Reduction
Iris and MNIST — from 4 dimensions to 784.
PCA from two derivations, how to choose k, reconstruction and interpretation. Where PCA fails on non-linear manifolds, then t-SNE and UMAP: what each one preserves, what each one distorts, and a decision rule for choosing between them. Appendix on the curse of dimensionality.
~3 h · 12 live simulators · eight-step Iris lab + 5 MNIST tasks
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From EDA to Dimensionality Reduction
Palmer Penguins — 344 penguins, 6 input columns, 19 missing values from two causes.
An in-class lab that runs the whole path end to end: univariate then bivariate analysis, a train/test split before any transformation, a pipeline that handles missing values and scaling without leaking, and the same data projected by PCA, t-SNE and UMAP — compared under a single criterion, then applied to unseen points.
Colab notebook · 8 steps, each ending in a check value
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Clustering
Iris and synthetic data —
make_blobs,make_moonsand a random baseline.Why grouping is a choice rather than a discovery, the algorithm and the inertia it chases, and silhouette point by point — both metrics worked by hand on eight points before any library. Then choosing k with the two of them together, across five lab steps.
50 min class + 70 min lab · 12 questions · Colab with 3 exercises
Extra — challenge
178 wines, self-guided with the answer key held back to the end: five scalings, four indices plus a gap statistic written from scratch, stability, GMM with BIC, and clustering in reduced space — then audit every decision against the key.
Classroom use
Each handout has an anchored sidebar index, a copy button on every code block, and its own print stylesheet — the menu, buttons and interactive elements are dropped when printing or exporting to PDF.