Documenting and applying advanced SQL techniques for filtering, transforming, aggregating, summary stats and reporting on data using CASE, subqueries, window functions, and table reshaping tools like ROLLUP, CUBE, and pivoting.
Advanced SQL techniques for transforming and filtering data: WHERE, ORDER BY, LIMIT, CASE, aggregates, subqueries, and derived columns.
This project is based on the DataCamp course Data Manipulation in SQL and expands it with structured, business-oriented documentation and reproducible examples. It showcases advanced data analysis techniques using PostgreSQL.
- Filtering & conditional logic:
WHERE,CASE, boolean conditions - Sorting and limiting with
ORDER BY,LIMIT - Aggregation & grouping:
GROUP BY,HAVING,ROLLUP,CUBE - Window functions:
RANK(),LAG(),LEAD(),ROWS BETWEEN - Subqueries: scalar, correlated, nested
- CTEs vs. subqueries: modular query design
- Pivots with
CROSSTAB - Set operations:
UNION,INTERSECT,EXCEPT - SQL reporting design & storytelling with layered metrics
- Data validation and analysis with nested queries
sql/→ Themed SQL files (e.g., case statements, window functions, joins)data/→ Datasets used for query building (.csv)docs/→ Markdown explanations of key SQL concepts and techniquesvisuals/→ Optional screenshots or query results for referencecertificate.pdf→ DataCamp course certificate of completion
🔗 View certificate: DataCamp: PostgreSQL Summary Stats and Window Functions
- case-and-conditional-logic.md
- short-vs-correlated-subqueries.md
- cte-vs-subqueries.md
- window-functions-explained.md
- pivot-rollup-cube-guide.md
- reporting-strategies.md
- business-value-of-case_and_window_functions.md
These documents support the technical reasoning and business value behind each SQL concept in this project.
Create a professional, reference-ready SQL project that showcases real analytical skill — not just for passing a course, but for solving real data problems in business environments.