# Python for Data Science

Pandas fundamentals, EDA, visualization, data cleaning, and feature engineering.

Use this subtrack when you want applied analytics and notebook workflow practice before branching into heavier modeling or infrastructure work.

## How To Use This Subtrack Well

- Build one full exploratory workflow from raw data to cleaned, interpretable outputs.
- Focus on data quality, feature reasoning, and communication rather than only library coverage.
- Pair this subtrack with [../../02-data-science/README.md](../../02-data-science/README.md) if you need stronger fundamentals underneath the project work.

## What Comes Next

- Continue to [../README.md](../README.md) for the broader practical data science roadmap.
- Continue to [../machine-learning/README.md](../machine-learning/README.md) if you want to turn the cleaned data into predictive models.
- Continue to [../../16-model-evaluation/README.md](../../16-model-evaluation/README.md) if you want stronger measurement and reporting discipline.
