How does this help with machine learning development?

It provides the underlying linear algebra and statistical validation techniques necessary for building, dimensionality reduction, and auditing the performance of ML models.

Which Python libraries are integrated into this skill?

The skill provides optimized implementation patterns for NumPy, SciPy, pandas, statsmodels, and scikit-learn.

Does it include support for statistical assumptions?

Yes, it includes best practices and code for checking assumptions like normality (Shapiro-Wilk), homoscedasticity, and multicollinearity.

Can I use this skill for A/B testing analysis?

Yes, it includes specialized frameworks for two-proportion z-tests, calculating lift, and determining p-values for experimental comparisons.

What mathematical concepts does this skill cover?

It covers descriptive statistics, probability distributions, linear algebra (including SVD and Eigenvalues), and various regression models like OLS, Ridge, and Lasso.

Statistics & Mathematics for Data Science

Name: Statistics & Mathematics for Data Science
Author: pluginagentmarketplace

bypluginagentmarketplace

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数据科学与机器学习

Provides foundational mathematical tools and statistical methods for data analysis, hypothesis testing, and machine learning architecture.

This skill equips Claude with the rigorous mathematical foundations necessary for advanced data science and engineering tasks. It enables precise execution of probability modeling, linear algebra operations, and statistical inference, allowing for the creation of robust analytical pipelines. From implementing A/B testing frameworks to performing singular value decomposition for dimensionality reduction, this skill ensures that data-driven decisions and machine learning models are built on sound mathematical principles.

主要功能

01Comprehensive hypothesis testing including t-tests, chi-square, and A/B test proportions

02Core linear algebra operations for ML including SVD, Eigenvalues, and matrix multiplication

032 GitHub stars

04Implementation of Central Limit Theorem and descriptive statistical analysis

05Probability distribution modeling for Normal, Binomial, and Poisson datasets

06Advanced regression analysis using OLS, Ridge, and Lasso with statsmodels and scikit-learn

使用场景

01Performing exploratory data analysis to validate assumptions through normality tests and correlation checks

02Designing and analyzing experimental A/B testing results to determine statistical significance

03Building custom machine learning algorithms from scratch using matrix calculus and linear algebra

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pluginagentmarketplace/custom-plugin-data-engineer statistics-math

For use in Claude.ai and ChatGPT

Download Skill