LearnPyPython Learning Platform

From Python basics to machine learning, from statistical analysis to data science - Build full-stack capabilities for the AI era

Python Fundamentals

Python & Statistics

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Python Fundamentals for Research

Zero-to-hero Python learning with AI tools assistance. From basic syntax to object-oriented programming, from file operations to popular libraries - a learning path designed for the AI era.

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Python & Statistics

From descriptive statistics to econometrics, from hypothesis testing to panel data analysis. Data analysis and statistical modeling using Python (pandas, statsmodels, scipy).

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Machine Learning Fast Track

Full coverage of supervised, unsupervised, and deep learning. Theory meets practice - from scikit-learn to PyTorch, master algorithms and industrial applications.

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AI for Research

Using generative AI and batch API calls for research text processing. Document information extraction, classification, batch data processing - practical skills designed for research.

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Online Code Execution

Built-in Pyodide Python environment, no local setup required. Supports most mainstream Python libraries - learn and practice instantly, see results immediately.

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Example-driven, Easy to Understand

Clear definitions, principle analysis, and code examples for every concept. Professional terminology explained in plain language - accessible even for beginners.

📚 Book Series

Build a complete data science knowledge system, from programming basics to cutting-edge technologies, from statistical theory to industrial practice. Each book focuses on case-driven, easy-to-understand content to help you quickly build core competitiveness in the AI era.

Python Fundamentals for Research

Zero-to-hero Python learning designed for social science researchers. From basic syntax to data analysis, master programming skills through practical projects.

StatsPai - Python & Statistics

From descriptive statistics to econometrics, professional data analysis and modeling using Python. Covers probability, hypothesis testing, regression, time series, and more.

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Machine Learning & Causal Inference

📝 In Preparation

From machine learning to causal inference, from theory to practice. Learn supervised/unsupervised learning, causal diagrams, A/B testing, applied to recommendation systems and policy evaluation.

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AI for Research

📝 In Planning

Using generative AI and batch API calls to process text files. Learn document information extraction, classification, batch data processing, and make AI your research assistant.

🎯 Why Choose LearnPy.online?

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Systematic Learning Path

From programming basics to cutting-edge AI, build complete knowledge system

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Online Code Execution

Built-in Python environment, learn and practice without setup

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Case-Driven Teaching

Practical cases for every concept, theory meets practice

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Easy to Understand

Plain language explanations, accessible for beginners

Website Instructions

This website allows you to run Python code without logging in. For any errors, please visit the Feedback page
Python code can be edited and temporarily saved, but won't be permanently saved - will reset after page refresh
For any issues or suggestions, visit the Feedback page or email: brycew6m@gmail.com
All core features of this website are permanently free

Quick Start

Choose a course that interests you and start your learning journey:

🐍 New to Python? → Python Fundamentals for Research
📊 Want to learn data analysis? → Python & Statistics
🧠 Want to learn machine learning? → Machine Learning Fast Track
🤖 Want AI to help with research? → AI for Research

Learning Philosophy

In the AI Era, Understanding is King

We believe that in the age of AI assistance, understanding core concepts is more important than memorizing technical details. LearnPy.online is committed to:

Eliminating non-essential details, going straight to the core ideas
Plain language explanations, making professional knowledge accessible
Practice-oriented, from demo code to production-level implementation
Systematic learning, building a complete knowledge graph

Update Roadmap

Project Introduction

LearnPy.online is a continuously updated open-source learning platform dedicated to providing Chinese developers with a complete learning path from Python basics to AI applications. Currently offers four core courses covering Python programming, statistical analysis, machine learning, and AI-assisted research.

Version Planning

v1.0: Platform Architecture (2024.6.1 - 2025.10.31) ✅

Build complete learning platform architecture supporting four core courses
Deploy to Vercel + dedicated domain (Completed: learnpy.online)
Implement online Python code execution environment (Completed)

v2.0: Course Content Development (2025.11.1 - 2026.3.31)

Complete Python Fundamentals for Research course content (✅ Published)
Complete Python & Statistics course content (✅ Published)
Complete Machine Learning Fast Track course content (In preparation)
Complete AI for Research course content (In planning)
Add more practical cases and exercises

v3.0: Platform Feature Enhancement (2026.4.1 - 2026.6.30)

Add AI-assisted learning features: automatic concept explanations, error explanations, code generation/correction
Add learning progress tracking and certificate system
Add community interaction features
Optimize mobile experience

Current Version: v1.0 | Next Milestone: Four courses content completion (Expected end of March 2026)

Feedback & Suggestions

You can participate through:

GitHub Issues: Report bugs or suggestions: Project Issues
Feedback Button: Floating button at bottom right of website
Email Contact: Directly contact author: brycew6m@gmail.com
Pull Request: Contribute code or documentation directly - all improvements welcome!

StatsPai's Core Product: CoPaper.AI

StatsPai Flagship Product

AI-Powered Research Assistant — From data to paper, human-AI collaboration for the entire empirical research workflow.

Powered by Claude 4.5 and GPT-5, CoPaper.AI helps researchers complete empirical research that traditionally takes weeks — in just minutes. Whether it's economics papers, social science research, or public policy analysis, CoPaper.AI provides end-to-end support from data cleaning to final reports.

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Paper Outline Planning

Structured templates for quick research framing

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Literature Review

Upload PDFs, get 100% reliable synthesis

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Empirical Data Analysis

Descriptive stats, baseline models, robustness checks

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Causal Inference

RCT, DID, IV, RDD and more

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Publication-Ready Papers

Complete DOCX reports with methodology & discussion

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Reproducible Code

Python / R / Stata code, fully reproducible

Who It's For

Academic ResearchersGraduate StudentsEconomics / Social SciencePublic Policy AnalysisData-Driven Research

Team Background

StatsPai, Inc. was founded at Stanford REAP (Rural Education Action Program). Co-founder Prof. Scott Rozelle is a Stanford expert in development economics and econometrics with over 30 years of empirical research experience.

Visit CoPaper.AI → About StatsPai →

StatsPai Ecosystem: CoPaper.AI (Professional Research Assistant) + LearnPy.online (Free Learning Platform) — From learning to research, every step of the way

About the Author

Bryce Wang (王几行XING)

📖 Zhihu: @王几行XING
📧 Email: brycew6m@gmail.com
💻 GitHub: @brycewang-stanford

Acknowledgements

Thanks to all friends who support this project!

Star Support: Every star on GitHub is motivation
Feedback & Suggestions: Every piece of feedback is taken seriously
Community Contributions: Welcome to join and build together

Let's build full-stack data science capabilities through understanding, not technical details, in the AI era! 🚀