Stronger LLM collaboration - You will learn to write prompts like precise analytical briefs, so you can get more relevant outputs faster for product analysis, data exploration, and insight generation.
SQL built and checked with AI - You will use AI to turn business questions into SQL, spot missing filters, and refactor existing queries, helping you move from request to reliable result much faster.
Faster Python analysis - You will speed up Python-based analysis, from exploratory work and result interpretation to improving existing analytical code, while keeping full control over logic and quality.
Stronger data sanity checks - You will learn how to use AI to detect anomalies, inconsistencies, and suspicious outputs, so you can catch data issues earlier before presenting findings to stakeholders.
Better product insights - You will practice generating summaries, hypotheses, and initial interpretations with strong business context, making it easier to turn numbers into product decisions and actions.
More reliable A/B tests - You will see how AI can support experiment design and A/B test interpretation, while also learning to identify methodological mistakes and the risks of overreading results.
Fewer false conclusions - You will understand LLM limitations, common hallucinations, and ways to control them, so you do not base your analysis on convincing but flawed interpretations or assumptions.
An AI-powered analyst workflow - You will build a practical way of working with AI in daily analytical tasks, from prompt design to result validation, improving efficiency without giving up accountability.
Data analysis basics - You should be comfortable working with tabular data, metrics, segments, and basic relationships in data, so you can judge whether AI-generated answers actually make sense.
SQL fundamentals - You should know core SQL concepts such as SELECT, JOIN, WHERE, and GROUP BY, because the training shows how AI can support writing, checking, and improving analytical queries.
Basic Python skills - You should understand the basics of Python used in data analysis, so you can benefit from modules on exploration, code review, and evaluating AI-suggested code changes.
Analytical experience - You should have hands-on exposure to product analytics or data analytics, since the training focuses on real scenarios, insights, experiments, and validating outcomes.
