analytics

A/B testing statistics explained: hypotheses, sample size, p-values, power, confidence intervals, and the seven mistakes that invalidate experiments.

Data visualization tools compared: Matplotlib, seaborn, Plotly, Tableau, Power BI, and Looker Studio, with code, pricing, trade-offs, and how to choose.

Learn the SQL data scientists use daily: SELECT, JOINs, GROUP BY, window functions, CTEs, and date handling, with PostgreSQL examples and common pitfalls.

Which data science tools people actually use in 2026, from Python, SQL and pandas to Jupyter and PyTorch, compared with survey data and official documentation.