A collection of financial analytics and applied data science case studies in Jupyter notebooks.
Focus: clean data workflows, feature engineering, model evaluation, and clear business-facing outputs.
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BCGX Task One https://github.com/kmaso99/financial-data/blob/main/BCGX%20Task%20One.ipynb
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BCGX Task Two https://github.com/kmaso99/financial-data/blob/main/BCGX%20Task%20Two.ipynb
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BCGX Task Three https://github.com/kmaso99/financial-data/blob/main/BCGX%20Task%20Three.ipynb
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BCGX Task Four https://github.com/kmaso99/financial-data/blob/main/BCGX%20Task%20Four.ipynb
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Quantium Part I https://github.com/kmaso99/financial-data/blob/main/Quantium%20Part%20I.ipynb
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Quantium Part II (link in repo if present; if Part II is only a checkpoint file, consider committing the non-checkpoint version)
This repo emphasizes the practical skills hiring managers look for in finance/analytics work:
- Data cleaning and tidy dataset construction
- Feature engineering for structured/tabular problems
- Baselines, model selection, and evaluation discipline
- Interpretable outputs (plots, diagnostics, and concise takeaways)
These notebooks are designed to be readable first, reproducible second:
- assumptions and transformations are documented inline
- random seeds are set when modeling is involved
- outputs are generated from code (not manually edited)
If you want to run locally, the typical setup is:
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
jupyter lab