# 📊 Fundamental Mathematics for Data Science

A comprehensive collection of Python implementations exploring core mathematical concepts essential for data science, including descriptive statistics, probability, inferential statistics, linear algebra, and calculus.

## 🎯 Project Overview

This repository demonstrates practical applications of fundamental mathematical concepts through real-world data analysis. Each section builds intuition through hands-on coding with authentic datasets.

## 📚 Topics Covered

- **Descriptive Statistics**: Central tendency, dispersion, and data summarization
- **Probability**: Poisson distributions, sampling distributions, and random processes
- **Inferential Statistics**: Hypothesis testing and statistical inference
- **Linear Algebra**: Matrix operations and image transformations
- **Calculus**: Numerical differentiation and limit approximations

## 🛠️ Technologies

- Python 3.x
- NumPy, Pandas, SciPy
- Matplotlib, Seaborn
- Jupyter Notebooks