A Python package develop for transportation spatio-temporal big data processing, analysis and visualization.

Framework to Automatically Determine the Quality of Open Data Catalogs

Collection of R scripts to test packages in conducting data quality assessments

Sumeh — Unified Data Quality Framework Sumeh is a unified data quality validation framework supporting multiple backends (PySpark, Dask, Polars, DuckDB, Pandas) with centralized rule configuration.

This project involves analyzing Sprocket Central Pty Ltd Data to help the marketing department unveil useful insights that could help them optimize resources allocation for targeted marketing

collection of Jupyter Notebooks in both English and Spanish, dedicated to performing data quality analysis using the R programming language

Free, local health check for dbt projects. Finds cost, data quality, security and code issues in your models — no warehouse connection, no credentials, no signup.

Reproducible SQL Server 2022 analytics lab with PowerShell provisioning, data integrity, a verified star schema and GitHub Actions.

Data ETL & Valuation Pipeline that normalizes crawled laptop listings, parses hardware specifications using declarative JSON rules, and computes dynamic configuration and value-for-money scores.

Interactive Excel dashboard analysing 307,973 UK road traffic accidents (2021–2022) across 422 districts. Covers severity, temporal, infrastructure and environmental risk factors with cross-filtering slicers. Documents a data coverage gap affecting year-on-year comparison, with a full PowerPoint report included.

This repository showcases my project completed during the KPMG AU Virtual Internship by Forage. I had the opportunity to take the mystery out of big data and learn what it's like to produce business results with the KPMG data analytics team. I am excited to share my learnings and the outcomes of this enriching internship.

This repository contains my solutions and project work for the KPMG AU Data Analytics Virtual Internship offered by Forage.

End-to-end MySQL data cleaning project covering duplicate detection, data standardization, NULL handling, date transformation, and data-quality validation using a layoffs dataset.