Exploratory Data Analysis — [Public Dataset]

2026-08-01Planned

A deep exploratory analysis of a public dataset — data cleaning, hypothesis generation, statistical testing, and a clear written narrative of the findings.

eda pandas statistics data storytelling

Planned — write-up coming

This write-up will be completed as the project is finished. The sections below show the problem and intended approach; results only get added once they’re real.

Problem

Most data work isn't modeling — it's understanding what a dataset can and cannot say before anyone builds on top of it. This project demonstrates that skill directly: taking a public dataset from raw download to a written analysis with defensible claims, explicit caveats, and charts that each state a takeaway. [PLACEHOLDER: name the dataset and the central question once chosen.]

Data

[PLACEHOLDER: dataset name] ([PLACEHOLDER: source])

Approach

Systematic exploration: a data quality audit first (missingness, duplicates, impossible values), then univariate and bivariate analysis, hypothesis framing, and statistical tests where appropriate. The deliverable is a written narrative a non-technical reader could follow — not a notebook dump.

Intended workflow: audit data quality, explore, test hypotheses — looping back as new questions emerge — then write a narrative with explicit caveats. — intended workflow

Result

Results will be published when the project is complete. No metrics appear here until they’re real.

demo video

a short recorded walkthrough goes here once the project is complete

What I'd do differently

Filled in honestly once the project is done — including what didn’t work and what I’d change with hindsight.

repo and notebook links will be added when the project is published