Math for Data Science Repository Overview Welcome to the Math for Data Science repository! This comprehensive collection of mathematical concepts is curated to empower data scientists with a strong foundation in mathematical principles essential for effective data analysis and interpretation.

Table of Contents Descriptive Statistics

Mean, Median, Mode Percentile Standard Deviation Mean Absolute Deviation Probability and Distribution

Normal Distribution Z Score Logarithm and Log-Normal Distribution Trigonometry

Basic Trigonometric Functions Angles and Degrees Trigonometric Identities Hypothesis Testing

Introduction to Hypothesis Testing Statistical Significance p-values Advanced Statistics

Modified Z Score Advanced Probability Concepts Central Limit Theorem Additional Topics

Matrix Operations Calculus Basics for Data Science Linear Algebra for Machine Learning How to Use This Repository Each topic is organized into its respective directory, containing Jupyter Notebooks, Python scripts, or R code, along with explanatory documentation. Explore the directories to find detailed explanations, code examples, and practical applications.

Getting Started To get started with this repository, follow these steps:

Clone the Repository:

bash Copy code git clone https://github.com/your-username/math-for-data-science.git Navigate to a Topic: Browse through the directories to find the specific mathematical concept you want to explore.

Open Jupyter Notebooks: Open the Jupyter Notebooks or Python scripts to interact with the code and learn through practical examples.

Contributions Contributions to this repository are highly encouraged. If you have additional topics, improvements, or corrections, please feel free to submit a pull request. Let's collaborate and make this resource even more valuable for the data science community!

Support If you have any questions, issues, or suggestions, please open an issue on the repository. We welcome feedback and are here to assist you.

Happy learning! 📊✨