Data Wrangling - End-to-End Python Data Pipelines
High Distinction portfolio applying Python data-wrangling workflows - extraction, cleaning, validation and communication - across realistic datasets.
Overview
A High Distinction portfolio for Deakin's Data Wrangling unit, demonstrating complete Python-based data wrangling workflows across realistic datasets, from raw extraction through to validated, decision-ready outputs.
The problem
Realistic datasets rarely arrive clean - they carry missing values, duplication risk, join-logic errors and privacy considerations that require careful, validated handling before they can support analysis or decision-making.
My approach
Built reproducible Python (pandas) pipelines to extract, clean, consolidate and store data across multiple sources; validated every transformation with datatype checks, missingness analysis, duplication control and before/after summaries; applied statistical and machine learning techniques in exploratory analysis; and handled specialised contexts such as re-identification privacy risk, text pattern extraction and public-health-style reporting, culminating in an end-to-end High Distinction workflow.