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Python Data Science

Build data science workflows with Python — pandas DataFrames, scikit-learn models, matplotlib/plotly visualizations, and Jupyter notebooks. Generates clean, production-ready data pipelines with proper preprocessing.

This skill helps you work with data in Python. It generates pandas code for data cleaning and transformation, builds ML models with scikit-learn, creates publication-quality visualizations, and structures Jupyter notebooks for reproducible analysis. Handles common pitfalls like data leakage and proper train/test splits.

python pandas scikit-learn data-science ml

When to use

Use when exploring datasets, building ML models, creating visualizations for reports, cleaning messy data, or building ETL pipelines for recurring analysis.

Examples

Exploratory data analysis

Generate a comprehensive EDA for a new dataset

Perform exploratory data analysis on this CSV — distributions, correlations, missing values, outliers, and key insights

Classification pipeline

Build an end-to-end ML classification pipeline

Build a classification pipeline for churn prediction — preprocessing, feature engineering, model selection, and evaluation