Personal · Digital Archive
FavourKemele
A data scientist with a physiology background who turns messy datasets into clear answers with Python, SQL, and machine learning.
- Focus
- Python · SQL · ML
- Based in
- NG
- Status
- Open to roles
the analystI studied physiology before I studied data.
That meant I learned to ask “what's actually going on here” long before I learned to code. The instinct followed me into Python: cleaning messy datasets, questioning what a spike or a gap really means, and building small, focused tools that answer one question well.
I love to work in places that treats analysis as a way of understanding people, not just decorating a report.
The instruments I reach for.
Enough range to take a question from a raw CSV to a chart someone can act on.
- Python
- SQL
- Pandas
- NumPy
- Scikit-learn
- TF-IDF
- Cosine similarity
- Intro ML
- Matplotlib
- Seaborn
- Power BI
- Tableau
- Excel
- Git
- GitHub
- MySQL
- Jupyter
Five questions, five datasets.
Each project built end-to-end to answer one real question with one real dataset.
Notes from the notebook.
Essays on data, method, and reading the story inside a dataset.
I learned to ask what's actually going on here long before I learned to code — and that question is still the most useful tool I own.
Pattern before tool
Understand the shape of the data before choosing a model. The instinct came from physiology; the discipline stayed.
One question, answered well
Small, focused tools that answer one thing clearly beat dashboards that answer nothing in particular.
People, not decoration
Every row is a person. Analysis is a way of understanding them, not a way of dressing up a report.