Masters in Data Science at Notre Dame — calculus, statistics, programming, linear models, machine learning, databases. Lessons built by your AI tutor from real problem sets and pretests.
Limits, derivatives, integrals, multivariable — built from real pretests and quiz problems as you progress through the course.
5 modules · 1 activeProbability, distributions, hypothesis tests, confidence intervals, regression.
Coming soonSupervised, unsupervised, model selection, regularization, evaluation.
Coming soonOLS, GLMs, mixed effects, diagnostics, interpretation.
Coming soonnumpy, pandas, scikit-learn, statsmodels, matplotlib idioms.
Coming soontidyverse, ggplot2, lme4, broom, modelr idioms.
Coming soonSQL, normalization, joins, indexing, query optimization.
Coming soonPrintable, formula-focused reference cards. Print at 100% on letter paper — fits 3 columns, lamination-friendly. The kind of thing you tape to a wall or keep in a binder.
Limits, derivatives, integrals, series, key theorems. Full Calc I/II formulas on one page, 3-column print.
1 reference cardDistributions, tests, intervals, regression.
Coming when we cover the unitLoss functions, regularization, evaluation metrics, gradient methods.
Coming when we cover the unitOLS formulas, GLM link functions, diagnostics, VIF, AIC/BIC.
Coming when we cover the unitnumpy, pandas, scikit-learn idioms — one-page lookup.
Coming when we cover the unittidyverse verbs, ggplot2 geoms, model formulas, broom.
Coming when we cover the unit