skills
The working toolkit, honestly framed. Everything listed here is something I have genuinely used — and the last section is what I'm learning right now, listed openly because trajectory matters.
Python & data manipulation
Comfortable with — my daily working environment for analysis and modeling.
- Python
- pandas
- NumPy
- Jupyter notebooks
Demonstrated in Customer Churn Prediction
SQL & databases
Working knowledge — joins, aggregations, window functions, and query readability.
- SQL
- [PLACEHOLDER: SQLite / PostgreSQL / BigQuery — whichever you have used]
Demonstrated in SQL Analytics Case Study (upcoming)
Machine learning
Working knowledge — supervised learning with a baseline-first, evaluation-focused workflow.
- scikit-learn
- Classification & regression
- Cross-validation
- Feature engineering
Demonstrated in Customer Churn Prediction
Statistics & experimentation
Working knowledge — descriptive statistics, hypothesis testing, and knowing when a result is noise.
- Hypothesis testing
- Regression
- [PLACEHOLDER: other methods actually studied]
Data visualization & communication
Comfortable with — charts that state a takeaway, and write-ups a non-technical reader can follow.
- Matplotlib
- Seaborn
- Plotly
- Written analysis narratives
Workflow & tooling
Comfortable with — reproducible, version-controlled project work.
- Git & GitHub
- Virtual environments
- Clean notebook structure
Currently learning
In progress — listed openly because trajectory matters as much as the current toolkit.
- [PLACEHOLDER: e.g. deep learning fundamentals]
- [PLACEHOLDER: e.g. dbt / data pipelines]
- [PLACEHOLDER: current course or certification]
Why no skill bars?
Percentage bars and star ratings look precise but measure nothing. Prose framing — “comfortable with”, “working knowledge”, “currently learning” — is what I’d actually say in an interview, so it’s what the page says too.