Claude Code skills tagged Data Science

Skills for data analysis, statistical modeling, visualization, and ML in scientific contexts.

12 skills

Dataframes & graphs (3)

Polars, Dask, NetworkX — the libraries that handle the data shape before modeling starts.

• polars

  Fast pandas replacement with lazy evaluation for large DataFrames

  generic science

• dask

  Scale pandas and NumPy workflows beyond available RAM

  generic science

• networkx

  Analyze and visualize graph and network data structures

  generic science

Statistics & modeling (4)

Bayesian modeling, classical statistics, statsmodels, and exploratory analysis.

• pymc

  Build hierarchical Bayesian models with MCMC and posterior checks

  generic science

• statistical-analysis

  Choose the right statistical test and report results in APA

  generic science

• statsmodels

  Run statistical models with full diagnostics and inference tables

  generic science

• exploratory-data-analysis

  Auto-detect and profile any scientific data file format

  generic science

Visualization (2)

Matplotlib and Seaborn for publication-quality scientific charts.

• matplotlib

  Fine-grained control over every element of a Python plot

  generic science

• seaborn

  Quick statistical plots with attractive defaults from pandas

  generic science

Specialized & symbolic (3)

Time-series forecasting and symbolic mathematics.

• aeon

  scikit-learn-compatible ML toolkit for time series

  generic science

• timesfm-forecasting

  Zero-shot time series forecasting without model training

  generic science

• sympy

  Symbolic algebra, calculus, and exact mathematical computation

  generic science

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