A/B Testing for Data Analysts: From Hypothesis to Decision
Complete A/B testing workflow: hypothesis formulation, sample size calculation, z-test in Python, p-value interpretation and common pitfalls to avoid.
Practical notes about data analysis, SQL, Python and BI tools - from real projects and follow-up to 2026.
Complete A/B testing workflow: hypothesis formulation, sample size calculation, z-test in Python, p-value interpretation and common pitfalls to avoid.
ROW_NUMBER, RANK, LAG, LEAD, running totals and moving averages — every window function an analyst needs with real use cases and ready code.
Pricing, features and learning curve compared — practical guide to choosing the right BI tool for your workflow in 2026.
Build a retention matrix, heatmap and churn calculation from raw transaction data — complete guide with pandas code.
Modern SQL transformation with dbt Core: models, tests, documentation and lineage — no engineering background needed.
How to use ChatGPT, GitHub Copilot and PandasAI to work 3× faster — with real prompts and code examples.