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About Data Science with Python: Arrays to Models at Scale

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From the ndarray up through DataFrames, the modern columnar stack (Arrow, Polars, DuckDB, FireDucks), and visualization that survives 100 million points, to scikit-learn modeling discipline, PyTorch training, and graph analytics with large-graph rendering. A student who finishes can load, clean, explore, and model real datasets, explain why each tool in the 2026 stack is fast, and avoid the classic evaluation traps: leakage, overfitting, and misleading metrics.

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What You'll Learn

Concepts:
Bias-Variance Tradeoff DuckDB Out-of-Core Analytics PyTorch Training Loop Pandas DataFrame Semantics Classification Evaluation Metrics Datashader Rasterization Cross-Validation Scikit-Learn Estimator API Graph Analytics with NetworkX Matplotlib Figure and Axes Model Apache Arrow Columnar Format FireDucks Drop-In Acceleration Exploratory Data Analysis NumPy Arrays and Broadcasting Large-Graph Rendering Polars Lazy Execution Data Leakage Experiment Tracking Feature Engineering Split-Apply-Combine Aggregation

What you will learn

No introduction video available

About Demerzel

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