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Github is a free university, but 99% don't know!

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By Fodev JEO 4 min read

GitHub: The Free University for Data Science

Curiosity: How can we leverage GitHub as a learning platform? What knowledge can we retrieve from the vast repository of open-source data science resources?

GitHub is indeed a free university—a treasure trove of knowledge, code, and learning resources. Yet, 99% of people don’t realize its full potential as an educational platform. This curated list of 20 best GitHub repositories will help you retrieve knowledge systematically and innovate in your data science journey.

Learning Path Architecture

graph TB
    A[Data Science Learning] --> B[Foundations]
    A --> C[Specializations]
    A --> D[Practice]
    A --> E[Career]
    
    B --> B1[Roadmaps]
    B --> B2[Python Basics]
    B --> B3[Core Concepts]
    
    C --> C1[Time Series]
    C --> C2[Deep Learning]
    C --> C3[Data Engineering]
    
    D --> D1[Notebooks]
    D --> D2[Exercises]
    D --> D3[Projects]
    
    E --> E1[Interviews]
    E --> E2[Q&A]
    E --> E3[Cheatsheets]
    
    style A fill:#e1f5ff
    style B fill:#fff3cd
    style C fill:#d4edda
    style D fill:#f8d7da
    style E fill:#e7d4f8

20 Best GitHub Repositories for Data Science

#RepositoryCategoryDescriptionStarsLink
1Data Scientist Roadmap🗺️ RoadmapComprehensive learning path for data scientists⭐Link
2Learn Data Science📚 LearningInteractive tutorials and notebooks⭐Link
3Awesome Python🐍 ResourcesCurated list of Python resources⭐⭐⭐Link
4Data Science in Python📊 TutorialsPython-based data science tutorials⭐Link
5DS Python Notebooks📓 NotebooksCollection of data science notebooks⭐⭐⭐Link
6Awesome Data Science📚 ResourcesComprehensive data science resource list⭐⭐⭐Link
7Self Taught DS🎓 CurriculumOpen-source data science curriculum⭐⭐⭐Link
8Time Series Forecasting📈 SpecializationMicrosoft’s time series forecasting guide⭐Link
9Master Data Science🎓 CurriculumComplete data science master’s program⭐Link
10Keras Resources🤖 Deep LearningBest practices and resources for Keras⭐Link
11Pandas Exercises💪 PracticeHands-on pandas exercises⭐⭐⭐Link
12Best DS Resources📚 ResourcesCurated collection of data science resources⭐Link
13Data Engineering HowTo🔧 EngineeringGuide to becoming a data engineer⭐Link
14Awesome Data Engineering🔧 EngineeringData engineering tools and resources⭐⭐⭐Link
15DS Cheatsheets📝 ReferenceQuick reference cheatsheets⭐⭐⭐Link
161000+ DS Blogs📰 BlogsComprehensive list of data science blogs⭐Link
17Free DS Books📖 BooksCollection of free data science books⭐Link
18Data Science Q&A❓ Q&AQuestion and answer repository⭐Link
19DS Interviews💼 CareerInterview preparation resources⭐⭐⭐Link

Repository Categories Breakdown

🗺️ Roadmaps & Learning Paths

1. Data Scientist Roadmap

2. Self Taught Data Science

📚 Comprehensive Resources

3. Awesome Python

4. Awesome Data Science

📓 Hands-on Practice

5. Data Science Python Notebooks

6. Pandas Exercises

How to Use These Repositories

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# Example: Cloning and exploring a repository
import subprocess
import os

def explore_repo(repo_url, local_path):
    """Clone and explore a GitHub repository"""
    # Clone the repository
    subprocess.run(['git', 'clone', repo_url, local_path])
    
    # List contents
    contents = os.listdir(local_path)
    print(f"Repository contents: {contents}")
    
    # Find notebooks
    notebooks = [f for f in contents if f.endswith('.ipynb')]
    print(f"Found {len(notebooks)} notebooks")
    
    return notebooks

# Example usage
repo_url = "https://github.com/donnemartin/data-science-ipython-notebooks"
local_path = "./data-science-notebooks"
notebooks = explore_repo(repo_url, local_path)

Learning Workflow

graph LR
    A[Choose Repository] --> B[Clone & Explore]
    B --> C[Read Documentation]
    C --> D[Run Examples]
    D --> E[Modify & Experiment]
    E --> F[Build Projects]
    F --> G[Contribute Back]
    
    style A fill:#e1f5ff
    style D fill:#fff3cd
    style F fill:#d4edda
    style G fill:#f8d7da

Specialized Learning Paths

Time Series Analysis

Deep Learning

Data Engineering

Career Preparation

Interview Resources

Q&A Repository

Key Takeaways

Retrieve: GitHub hosts an incredible wealth of free educational resources—from roadmaps to hands-on exercises, covering every aspect of data science.

Innovate: By systematically exploring these repositories, you can build a personalized learning path that matches your career goals and interests.

Curiosity → Retrieve → Innovation: Start with curiosity about a topic, retrieve knowledge from these repositories, and innovate by applying what you learn to real-world problems.

Multi-GPU Training Diagram by Avi Chawla

Curiosity: Includes:

  • ▶ Model Parallelism
  • ▶ Tensor Parallelism
  • ▶ Data Parallelism
  • ▶ Pipeline Parallelism

4 Strategies Multi-GPU

Study, Datascience
Github Datascience
This post is licensed under CC BY 4.0 by the author.