AI prompts for SQL queries, data visualization, statistical analysis, and building dashboards and reports.
Always specify your database dialect (PostgreSQL, BigQuery, Snowflake) for correct SQL syntax
Include sample data schemas so ChatGPT understands your table structure
Ask for code comments and documentation alongside the analysis code
Request both the code and an explanation of the methodology for learning
Specify your Python library preferences (pandas, polars, duckdb) for consistent code
Write complex SQL queries with CTEs and window functions in minutes
Generate Python visualization code ready for executive presentations
Create data quality audit scripts for new data sources
Build statistical analysis frameworks for A/B tests and experiments
Draft stakeholder-ready summaries from raw analysis results
ChatGPT writes good SQL drafts, but always review for: correct table/column names, appropriate indexing, query performance, and edge cases. It's excellent for complex window functions and CTEs that would take time to write manually.
Absolutely. AI tools don't replace analytical thinking, but they dramatically speed up the coding and documentation parts of analysis. Analysts who leverage AI can take on 2-3x more projects with higher quality output.
ChatGPT understands statistical concepts well and generates correct code for standard tests. However, always verify the statistical approach is appropriate for your data — assumptions about distribution, sample size, and independence still require human judgment.
Take our free AI course and learn advanced prompting techniques.
Start Free AI Course →