https://github.com/vbs360/reference_materials

https://github.com/vbs360/reference_materials

Last synced: 9 days ago
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• Host: GitHub
• URL: https://github.com/vbs360/reference_materials
• Owner: VBS360
• Created: 2024-09-21T08:47:34.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-12-28T12:08:41.000Z (10 days ago)
• Last Synced: 2024-12-28T13:19:15.185Z (10 days ago)
• Language: Jupyter Notebook
• Size: 35.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Reference Materials

This repository is a collection of various study materials, projects, and notes related to Data Science, Python, Statistics, and more. The aim of this repository is to serve as a comprehensive reference for my personal use and for anyone interested in learning these subjects.

## Repository Structure

### 1. **Data Science**

Contains study guides, projects, and practical exercises focused on data science workflows, machine learning models, and essential algorithms.

### 2. **Python**

A collection of Python code snippets, tutorials, and best practices, ranging from basic programming concepts to advanced functionalities like object-oriented programming, decorators, and more.

### 3. **Statistics**

Comprehensive notes and study material covering key statistical concepts, including probability, hypothesis testing, regression analysis, and more. It includes practical examples for better understanding.

### 4. **Machine Learning**

Documents on machine learning techniques, model building, and optimization strategies. This section also contains hands-on projects demonstrating machine learning applications.

### 5. **Data Analytics**

This folder contains a 7-day study plan for anyone looking to build a foundation in data analytics. It covers essential concepts and tools for data analysis.

- **PowerBI and Tableau Dashboards**: A video tutorial demonstrating how to create interactive dashboards using PowerBI and Tableau. These tools are essential for visualizing and communicating data insights effectively.

## How to Navigate

- Clone or download the repository to explore each section.

- Dive into the folder of interest to access the notes, scripts, and tutorials provided.

- Check out the **Data Analytics** section if you’re looking for a structured learning path in data analysis.

## Contributing

While this repository is primarily for my reference, contributions are welcome. If you have study materials, tutorials, or resources you'd like to add, feel free to submit a pull request.