Subjects

Descriptive Statistics & Exploratory Data Analysis

Learn to describe centre, spread and shape, choose the right plot for the question, detect outliers and missing-data mechanisms, run data quality checks that catch leakage and unit errors, avoid Simpson's paradox, and follow a repeatable EDA checklist in pandas.

Probability Fundamentals

Master conditional probability, Bayes' theorem and the base-rate fallacy, random variables, expectation and variance, the six distributions data scientists actually meet, the Law of Large Numbers and Central Limit Theorem, and the classic interview puzzles.

SQL for Data Analysis

Master analytical PostgreSQL: query evaluation order, join fan-out traps, CTEs, window functions with frames, date bucketing, cohort retention, funnels, sessionisation, NULL pitfalls, CASE pivots, deduplication and the classic SQL interview questions.