Data-Science-Notes
数据科学的笔记以及资料搜集
A structured collection of Jupyter Notebook study notes in Chinese covering data science foundations, from Python and NumPy basics through scikit-learn, machine learning, deep learning, and feature engineering.
Data-Science-Notes is a collection of study notes and gathered materials covering the foundations of data science, compiled and shared by a Chinese developer who goes by fengdu78. The repository is written primarily in Chinese and is organized as a set of Jupyter Notebooks grouped by topic.
The ten sections cover math fundamentals, Python basics, NumPy, Pandas, SciPy, data visualization using matplotlib and seaborn, scikit-learn, machine learning, deep learning, and feature engineering. Each section is its own folder inside the repository. The author describes the collection as still being updated over time, and notes that some content was gathered from other GitHub repositories.
The README lists the references and sources the author drew from, including the book Statistical Learning Methods by Li Hang, Coursera machine learning course materials, and several other open GitHub learning repos. There is no installation or setup step: you open the Jupyter Notebooks directly to read through examples and notes.
This repository is intended as a study reference rather than a software tool. Someone learning data science from scratch in Chinese would find it a structured starting point covering the main technical building blocks, from working with numbers in NumPy to training machine learning models with scikit-learn. The author also runs a WeChat public account and a community group focused on beginners in machine learning, and points to those as additional resources alongside this repository.
Where it fits
- Work through structured data science examples in Chinese, from Python basics up to training machine learning models.
- Use the scikit-learn notebooks as a hands-on reference when building your first classification or regression pipeline.
- Learn data visualization patterns with matplotlib and seaborn by running and modifying the example notebooks.