SQL and Python from my data analysis projects. Each folder holds the queries behind a published Tableau dashboard.
Dashboards: public.tableau.com/app/profile/lautaro.pintos
Revenue and cost analysis across countries, customer demographics and product mix. End-to-end: SQL for aggregation, Python (pandas) for exploratory analysis, Tableau for the dashboard.
Script_Bike_sales.sql · bike_sales.ipynb
Infection and mortality rates by country and continent. Uses SQL views to expose each analysis as a reusable query — death rate per country, infection rate against population, aggregation by continent.
Proyect_Covid.sql · Data_Usage.sql
Pricing and supply distribution across Buenos Aires neighbourhoods.
exploration.sql
Executive sales overview — segment, category and regional profitability.
exploration.sql
olist-ecommerce-analysis — my most complete project: a reproducible pipeline over 9 related tables (1.5M rows), with a documented data-quality audit. Start there if you only look at one.
SQL (MySQL, SQLite) · Python (pandas) · Tableau
Note: these use well-known public datasets. They demonstrate query technique; for analysis depth and data-quality work, see the Olist project above.