Statistics Math

pluginagentmarketplace/custom-plugin-data-engineer

This is your foundational math toolkit for data science work. You get descriptive statistics, hypothesis testing (t-tests, chi-square, A/B tests), probability distributions, and linear algebra operations through NumPy, SciPy, and statsmodels. The code examples are practical: calculating p-values, checking for statistical significance, running regression with regularization, and computing effect sizes beyond just p-values. It covers 120 estimated hours to master and unlocks machine learning and deep learning skills. The best practice warnings are honest about common pitfalls like p-hacking and confusing correlation with causation. If you're moving from basic Python into actual data analysis, this bridges that gap with the statistical reasoning you need before jumping into ML frameworks.

skills.sh installs: 433
GitHub stars: 7
last updated: Aug 21, 2026

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Install

$ npx -y skills add pluginagentmarketplace/custom-plugin-data-engineer --skill statistics-math --agent claude-code

Installs into .claude/skills of the current project.

Files

Statistics & Mathematics

Mathematical foundations for data science, machine learning, and statistical analysis.

Quick Start

import numpy as np
import scipy.stats as stats
from sklearn.linear_model import LinearRegression

# Descriptive Statistics
data = np.array([23, 45, 67, 32, 45, 67, 89, 12, 34, 56])
print(f"Mean: {np.mean(data):.2f}")
print(f"Median: {np.median(data):.2f}")
print(f"Std Dev: {np.std(data, ddof=1):.2f}")
print(f"IQR: {np.percentile(data, 75) - np.percentile(data, 25):.2f}")

# Hypothesis Testing
sample_a = [23, 45, 67, 32, 45]
sample_b = [56, 78, 45, 67, 89]
t_stat, p_value = stats.ttest_ind(sample_a, sample_b)
print(f"T-statistic: {t_stat:.4f}, p-value: {p_value:.4f}")

if p_value < 0.05:
    print("Reject null hypothesis: significant difference")
else:
    print("Fail to reject null hypothesis")

Core Concepts

1. Probability Distributions

import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt

# Normal Distribution
mu, sigma = 100, 15
normal_dist = stats.norm(loc=mu, scale=sigma)
x = np.linspace(50, 150, 100)

SKILL.md on GitHub