Study algorithm and data structure problems interactively using Jupyter Notebooks before a technical interview.
Read through common interview Q&A topics in Chinese to prepare for software engineering or data science roles.
Use the resume guide section to learn how to structure a tech resume for the Chinese job market.
Practice Kaggle-style data science problems alongside LeetCode-style coding challenges in one place.
| apachecn/interview | openbmb/minicpm | graviraja/mlops-basics | |
|---|---|---|---|
| Stars | 8,965 | 8,881 | 8,860 |
| Language | Jupyter Notebook | Jupyter Notebook | Jupyter Notebook |
| Setup difficulty | easy | moderate | moderate |
| Complexity | 1/5 | 4/5 | 3/5 |
| Audience | developer | developer | data |
Figures from each repo's GitHub metadata at analysis time.
Content is available to read online at interview.apachecn.org, no local setup needed. Running notebooks locally requires Python and Jupyter.
This repository is a Chinese-language study collection maintained by ApacheCN, a Chinese open-source community, aimed at people preparing for technical job interviews in the software and data industries. The title translates roughly to an IT industry interview knowledge library, and the description lists four types of content: a resume guide, algorithm and coding problems, a question-and-answer section covering common interview topics, and source code analysis. The topics tagged on the repository point to the kinds of material inside: programming interview questions, Kaggle data science competitions, LeetCode-style coding challenges, machine learning, and Python. The content is written in Jupyter Notebooks, a format commonly used for Python code combined with explanatory text, which suggests the material is interactive and runnable rather than purely written notes. The repository is hosted for reading online at interview.apachecn.org. It is released under a Creative Commons BY-NC-SA 4.0 license, which allows sharing and adaptation for non-commercial purposes with attribution. The README itself is short and points outward to the website rather than describing the content in detail, so the full scope of what is covered is best explored there.
A Chinese-language collection of study material for technical job interviews, covering algorithms, coding challenges, machine learning, Python, and resume writing, with interactive Jupyter Notebook examples.
Mainly Jupyter Notebook. The stack also includes Python, Jupyter Notebook.
Creative Commons BY-NC-SA 4.0, you can share and adapt the material with attribution, but not for commercial purposes, and derivatives must use the same license.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.