https://github.com/sureshbeekhani/statistics-for-data-science-learining
This repository is dedicated to providing a comprehensive learning path for mastering statistics in the field of data science
https://github.com/sureshbeekhani/statistics-for-data-science-learining
conditional-probability descriptive descriptive-statistics inferential probability-distributions statistics
Last synced: 6 months ago
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This repository is dedicated to providing a comprehensive learning path for mastering statistics in the field of data science
- Host: GitHub
- URL: https://github.com/sureshbeekhani/statistics-for-data-science-learining
- Owner: SURESHBEEKHANI
- Created: 2024-11-26T15:28:39.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-05T08:37:36.000Z (10 months ago)
- Last Synced: 2025-02-02T08:13:20.130Z (8 months ago)
- Topics: conditional-probability, descriptive, descriptive-statistics, inferential, probability-distributions, statistics
- Language: Jupyter Notebook
- Homepage:
- Size: 94.7 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Statistics for Data Science Learning
Welcome to the **Statistics for Data Science Learning** repository! This repository is designed to help learners understand and apply key statistical concepts in the context of data science. The materials here cover a variety of topics, from basic statistics to intermediate-level techniques used in data analysis and machine learning.
## Table of Contents
- [Repository Overview](#repository-overview)
- [Installation](#installation)
- [Usage](#usage)
- [File Structure](#file-structure)
- [Contributing](#contributing)
- [License](#license)
## Repository Overview
This repository contains Jupyter notebooks with explanations, examples, and exercises on various statistical topics. The focus is on providing hands-on experience with real-world datasets and problem-solving techniques that are essential for data scientists.
### Topics Covered
1. **Basic Statistics**
- Descriptive statistics (mean, median, mode, etc.)
- Probability distributions
- Sampling and hypothesis testing
2. **Intermediate Statistics**
- Regression analysis (linear, logistic)
- Analysis of variance (ANOVA)
- Time series analysis
## Installation
To get started, clone this repository to your local machine:
```bash
git clone https://github.com/SURESHBEEKHANI/Statistics-For-Data-Science-learining.git