Data & Database Work
AI makes many data and database tasks significantly faster — generating SQL queries, designing schemas, cleaning messy data, and interpreting analytics results. This section covers practical workflows for each, including how to validate AI-generated SQL before running it and how to interpret AI analysis critically.
In This Section
SQL Generation & Validation
Generating SQL queries with AI — how to describe your schema, validate output, and catch common errors before execution.
Schema Design
Using AI to help design database schemas — describing requirements, evaluating normalization choices, and catching design problems early.
Data Cleaning
AI-assisted approaches to identifying and fixing data quality issues — missing values, duplicates, format inconsistencies, and outliers.
BI & Analytics
Using AI in analytics workflows — interpreting results, suggesting visualizations, and asking data questions in natural language.
Data Quality
Building data quality checks and monitoring with AI assistance — generating validation rules, profiling datasets, and catching drift.