Standard syllabus
Python for data science · Undergraduate · Data science
Topics
Python for tables
- Virtual environments and package management (pip/conda survey)
- NumPy arrays, broadcasting, and vectorization
- pandas Series/DataFrame indexing and assignment rules
- Groupby, merge/join, and reshape (melt/pivot)
- Datetime, categorical, and string accessors
- Reading/writing CSV, Parquet, and SQL results (intro)
Visualization and notebooks
- matplotlib and seaborn (or plotly) for EDA
- Plot grammar choices: axes, legends, facets
- Jupyter workflow: cells, kernels, and reproducibility
- Documenting analysis with markdown narratives
- Avoiding hidden state and out-of-order execution
- Exporting figures and tables for reports
Modeling interfaces
- scikit-learn estimator API: fit/predict/transform
- Train/test splits and cross-validation (intro)
- Pipelines for preprocessing + model
- Metrics and confusion matrices (intro)
- Saving models with joblib/pickle carefully
- Debugging shape mismatches and NaN failures
Pricing calculator
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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