Statistics
Mathematical discipline for collection, analysis, interpretation of data. Foundation for all data science + machine learning. Two major schools: frequentist vs Bayesian.
Descriptive (mean, median, std dev, percentiles) vs inferential (hypothesis testing, confidence intervals, regression). Classic tests: t-test, ANOVA, chi-square, Mann-Whitney. Regression: linear, logistic, GLM. The p-value crisis of the 2010s → replication crisis in social sciences, toward better practice (preregistration, effect size over p < 0.05). Bayesian inference is growing (PyMC, Stan, brms). Modern ML is applied statistics in many ways — just neural networks hid the math behind auto-diff + GPUs.