Which Python libraries does this skill support?

This skill provides optimized patterns for the core Python data stack, including Pandas, NumPy, Scikit-learn, Statsmodels, SciPy, and visualization tools like Matplotlib and Seaborn.

Can this skill help with A/B testing?

Yes, it includes specialized functions for calculating required sample sizes based on power/alpha and tools for analyzing results to determine statistical significance.

Is causal inference supported?

Yes, the skill includes specific implementations for causal analysis, such as using propensity score matching to compare treatment and control groups.

Does it support advanced machine learning workflows?

Absolutely. It covers feature engineering, mutual information-based feature selection, and robust model evaluation using Stratified K-Fold cross-validation.

Data Science & Statistical Analysis

Name: Data Science & Statistical Analysis
Author: eyadsibai

byeyadsibai

数据科学与机器学习

Empowers Claude to perform statistical modeling, design experiments, and build predictive machine learning pipelines using Python.

关于

The Data Science skill for Claude Code provides specialized guidance for complex analytical tasks including A/B test design, predictive modeling, and causal inference. It streamlines the implementation of industry-standard Python libraries like Pandas, Scikit-learn, and Statsmodels by providing production-ready code patterns for feature engineering, model evaluation, and hypothesis testing. This skill is essential for developers and data scientists looking to automate rigorous statistical analysis and build robust machine learning workflows directly within their coding environment.

主要功能

Production-grade feature engineering and selection pipelines
A/B test framework for sample size and significance calculations
Robust model evaluation using stratified cross-validation
Causal inference via propensity score matching
Standardized statistical hypothesis testing workflows
0 GitHub stars

使用场景

Developing complex data processing and feature transformation scripts
Building and evaluating predictive models for business forecasting
Designing and analyzing experiment results for product features

关于

主要功能

Production-grade feature engineering and selection pipelines
A/B test framework for sample size and significance calculations
Robust model evaluation using stratified cross-validation
Causal inference via propensity score matching
Standardized statistical hypothesis testing workflows
0 GitHub stars

使用场景

Developing complex data processing and feature transformation scripts
Building and evaluating predictive models for business forecasting
Designing and analyzing experiment results for product features