Standard syllabus
Python for data science · Undergraduate · Data science
Learning objectives from the Python for data science syllabus, grouped by unit. Click an objective for study materials.
Learning objectives
Click an objective for study materials.
Python for tables
- Virtual environments and package management (pip/conda survey) — Virtual environments and package management (pip/conda survey)
- NumPy arrays, broadcasting, and vectorization — NumPy arrays, broadcasting, and vectorization
- pandas Series/DataFrame indexing and assignment rules — pandas Series/DataFrame indexing and assignment rules
- Groupby, merge/join, and reshape (melt/pivot) — Groupby, merge/join, and reshape (melt/pivot)
- Datetime, categorical, and string accessors — Datetime, categorical, and string accessors
Visualization and notebooks
- matplotlib and seaborn (or plotly) for EDA — matplotlib and seaborn (or plotly) for EDA
- Plot grammar choices: axes, legends, facets — Plot grammar choices: axes, legends, facets
- Jupyter workflow: cells, kernels, and reproducibility — Jupyter workflow: cells, kernels, and reproducibility
- Documenting analysis with markdown narratives — Documenting analysis with markdown narratives
- Avoiding hidden state and out-of-order execution — Avoiding hidden state and out-of-order execution
Modeling interfaces
- scikit-learn estimator API: fit/predict/transform — scikit-learn estimator API: fit/predict/transform
- Train/test splits and cross-validation (intro) — Train/test splits and cross-validation (intro)
- Pipelines for preprocessing + model — Pipelines for preprocessing + model
- Metrics and confusion matrices (intro) — Metrics and confusion matrices (intro)
- Saving models with joblib/pickle carefully — Saving models with joblib/pickle carefully
What each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Python for tables
Virtual environments and package management (pip/conda survey)
Coming soon - Visualization and notebooks
matplotlib and seaborn (or plotly) for EDA
Coming soon - Modeling interfaces
scikit-learn estimator API: fit/predict/transform
Coming soon
Pricing calculator
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$1,162 · Python for data science · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.