Github Top Repositories
前往频道在 Telegram
Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.
显示更多📈 Telegram 频道 Github Top Repositories 的分析概览
频道 Github Top Repositories (@githubre) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 15 260 名订阅者,在 教育 类别中位列第 13 381,并在 印度 地区排名第 25 928 位。
📊 受众指标与增长动态
自 невідомо 创建以来,项目保持高速增长,吸引了 15 260 名订阅者。
根据 10 十一月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 349,过去 24 小时变化为 6,整体触达仍然可观。
- 认证状态: 未认证
- 互动率 (ER): 平均受众互动率为 0.88%。内容发布后 24 小时内通常能获得 0.60% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 134 次浏览,首日通常累积 92 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 1。
- 主题关注点: 内容集中在 repository, fork, programming, statistic, description 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台:
“Top GitHub repositories in one place 🚀
Explore the best projects in programming, AI, data science, and more.”
凭借高频更新(最新数据采集于 11 十一月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 教育 类别中的关键影响点。
15 260
订阅者
+624 小时
+767 天
+34930 天
帖子存档
15 260
🔥 Trending Repository: anet
📝 Description: Simple Rust VPN Client / Server
🔗 Repository URL: https://github.com/ZeroTworu/anet
📖 Readme: https://github.com/ZeroTworu/anet#readme
📊 Statistics:
🌟 Stars: 268 stars
👀 Watchers: 15
🍴 Forks: 20 forks
💻 Programming Languages: Rust - Inno Setup - Shell - Makefile
🏷️ Related Topics:
#rust #vpn================================== 🧠 By: https://t.me/DataScienceM
15 260
🔥 Trending Repository: prompt-optimizer
📝 Description: 一款提示词优化器,助力于编写高质量的提示词
🔗 Repository URL: https://github.com/linshenkx/prompt-optimizer
🌐 Website: https://prompt.always200.com
📖 Readme: https://github.com/linshenkx/prompt-optimizer#readme
📊 Statistics:
🌟 Stars: 19.2K stars
👀 Watchers: 77
🍴 Forks: 2.4K forks
💻 Programming Languages: TypeScript - Vue - JavaScript - Shell - CSS - Dockerfile
🏷️ Related Topics:
#prompt #prompt_toolkit #prompt_tuning #llm #prompt_engineering #prompt_optimization================================== 🧠 By: https://t.me/DataScienceM
15 260
🔥 Trending Repository: fish-shell
📝 Description: The user-friendly command line shell.
🔗 Repository URL: https://github.com/fish-shell/fish-shell
🌐 Website: https://fishshell.com
📖 Readme: https://github.com/fish-shell/fish-shell#readme
📊 Statistics:
🌟 Stars: 32.3K stars
👀 Watchers: 279
🍴 Forks: 2.2K forks
💻 Programming Languages: Rust - Shell - Python - HTML - JavaScript - CMake
🏷️ Related Topics:
#shell #rust #fish #terminal================================== 🧠 By: https://t.me/DataScienceM
15 260
🔥 Trending Repository: cognee
📝 Description: Memory for AI Agents in 6 lines of code
🔗 Repository URL: https://github.com/topoteretes/cognee
🌐 Website: https://www.cognee.ai
📖 Readme: https://github.com/topoteretes/cognee#readme
📊 Statistics:
🌟 Stars: 11.7K stars
👀 Watchers: 59
🍴 Forks: 1.2K forks
💻 Programming Languages: Python - TypeScript - Shell - Dockerfile - CSS - Mako
🏷️ Related Topics:
#open_source #ai #knowledge #neo4j #knowledge_graph #openai #help_wanted #graph_database #ai_agents #contributions_welcome #cognitive_architecture #good_first_issue #rag #good_first_pr #vector_database #graph_rag #ai_memory #cognitive_memory #graphrag #context_engineering================================== 🧠 By: https://t.me/DataScienceM
15 260
✅ Data Science Project Ideas
1️⃣ Beginner Friendly Projects
• Exploratory Data Analysis (EDA) on CSV datasets
• Student Marks Analysis
• COVID / Weather Data Analysis
• Simple Data Visualization Dashboard
• Basic Recommendation System (rule-based)
2️⃣ Python for Data Science
• Sales Data Analysis using Pandas
• Web Scraping + Analysis (BeautifulSoup)
• Data Cleaning Preprocessing Project
• Movie Rating Analysis
• Stock Price Analysis (historical data)
3️⃣ Machine Learning Projects
• House Price Prediction
• Spam Email Classifier
• Loan Approval Prediction
• Customer Churn Prediction
• Iris / Titanic Dataset Classification
4️⃣ Data Visualization Projects
• Interactive Dashboard using Matplotlib/Seaborn
• Sales Performance Dashboard
• Social Media Analytics Dashboard
• COVID Trends Visualization
• Country-wise GDP Analysis
5️⃣ NLP (Text Language) Projects
• Sentiment Analysis on Reviews
• Resume Screening System
• Fake News Detection
• Chatbot (Rule-based → ML-based)
• Topic Modeling on Articles
6️⃣ Advanced ML / AI Projects
• Recommendation System (Collaborative Filtering)
• Credit Card Fraud Detection
• Image Classification (CNN basics)
• Face Mask Detection
• Speech-to-Text Analysis
7️⃣ Data Engineering / Big Data
• ETL Pipeline using Python
• Data Warehouse Design (Star Schema)
• Log File Analysis
• API Data Ingestion Project
• Batch Processing with Large Datasets
8️⃣ Real-World / Portfolio Projects
• End-to-End Data Science Project
• Business Problem → Data → Model → Insights
• Kaggle Competition Project
• Open Dataset Case Study
• Automated Data Reporting Tool
15 260
🔥 Trending Repository: likec4
📝 Description: Visualize, collaborate, and evolve the software architecture with always actual and live diagrams from your code
🔗 Repository URL: https://github.com/likec4/likec4
🌐 Website: https://likec4.dev
📖 Readme: https://github.com/likec4/likec4#readme
📊 Statistics:
🌟 Stars: 1.4K stars
👀 Watchers: 17
🍴 Forks: 117 forks
💻 Programming Languages: TypeScript - MDX - Astro - JavaScript - CSS - Langium
🏷️ Related Topics:
#architecture #diagrams #c4 #architecture_as_code================================== 🧠 By: https://t.me/DataScienceM
15 260
🔥 Trending Repository: opentelemetry-collector-contrib
📝 Description: Contrib repository for the OpenTelemetry Collector
🔗 Repository URL: https://github.com/open-telemetry/opentelemetry-collector-contrib
🌐 Website: https://opentelemetry.io
📖 Readme: https://github.com/open-telemetry/opentelemetry-collector-contrib#readme
📊 Statistics:
🌟 Stars: 4.3K stars
👀 Watchers: 62
🍴 Forks: 3.3K forks
💻 Programming Languages: Go - Makefile - Go Template - Shell - Dockerfile - Jinja
🏷️ Related Topics:
#opentelemetry #open_telemetry================================== 🧠 By: https://t.me/DataScienceM
15 260
🔥 Trending Repository: anki
📝 Description: Anki is a smart spaced repetition flashcard program
🔗 Repository URL: https://github.com/ankitects/anki
🌐 Website: https://apps.ankiweb.net
📖 Readme: https://github.com/ankitects/anki#readme
📊 Statistics:
🌟 Stars: 26.1K stars
👀 Watchers: 349
🍴 Forks: 2.8K forks
💻 Programming Languages: Rust - Python - Svelte - TypeScript - SCSS - Shell
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
15 260
🔥 Trending Repository: claude-code-hooks-mastery
📝 Description: Master Claude Code Hooks
🔗 Repository URL: https://github.com/disler/claude-code-hooks-mastery
📖 Readme: https://github.com/disler/claude-code-hooks-mastery#readme
📊 Statistics:
🌟 Stars: 2.3K stars
👀 Watchers: 52
🍴 Forks: 498 forks
💻 Programming Languages: Python - TypeScript
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
15 260
🎯 Want to Upskill in IT? Try Our FREE 2026 Learning Kits!
SPOTO gives you free, instant access to high-quality, updated resources that help you study smarter and pass exams faster.
✅ Latest Exam Materials:
Covering #Python, #Cisco, #PMI, #Fortinet, #AWS, #Azure, #AI, #Excel, #comptia, #ITIL, #cloud & more!
✅ 100% Free, No Sign-up:
All materials are instantly downloadable
✅ What’s Inside:
・📘IT Certs E-book: https://bit.ly/3Mlu5ez
・📝IT Exams Skill Test: https://bit.ly/3NVrgRU
・🎓Free IT courses: https://bit.ly/3M9h5su
・🤖Free PMP Study Guide: https://bit.ly/4te3EIn
・☁️Free Cloud Study Guide: https://bit.ly/4kgFVDs
👉 Become Part of Our IT Learning Circle! resources and support:
https://chat.whatsapp.com/FlG2rOYVySLEHLKXF3nKGB
💬 Want exam help? Chat with an admin now!
wa.link/8fy3x4
15 260
Leaked TonHub Checklist You Didn’t Know You Needed
Discover how to snag easy cash with zero fuss.
Step 1: Find simple tasks that pay without selling your soul.
Step 2: Use the bot that’s literally a genie for ads & tasks.
Step 3: Watch your wallet fill while you chill-no cap.
Ready to cash in? Check this out 👉 TonHub - Ads & Tasks
#ad InsideAds.
15 260
🔥 Trending Repository: vm0
📝 Description: the easiest way to run natural language-described workflows automatically
🔗 Repository URL: https://github.com/vm0-ai/vm0
🌐 Website: https://vm0.ai
📖 Readme: https://github.com/vm0-ai/vm0#readme
📊 Statistics:
🌟 Stars: 522 stars
👀 Watchers: 1
🍴 Forks: 20 forks
💻 Programming Languages: TypeScript - MDX - Shell - CSS - Rust - JavaScript
🏷️ Related Topics:
#react #cli #typescript #containers #sandbox #cloudflare #codex #dev_tools #ai_agent #ai_runtime #gemini_cli #agentic_workflow #claude_code #context_engineer #ai_sandbox================================== 🧠 By: https://t.me/DataScienceM
15 260
🔥 Trending Repository: ccpm
📝 Description: Project management system for Claude Code using GitHub Issues and Git worktrees for parallel agent execution.
🔗 Repository URL: https://github.com/automazeio/ccpm
🌐 Website: https://automaze.io/ccpm
📖 Readme: https://github.com/automazeio/ccpm#readme
📊 Statistics:
🌟 Stars: 6.5K stars
👀 Watchers: 39
🍴 Forks: 684 forks
💻 Programming Languages: Shell - Batchfile
🏷️ Related Topics:
#project_management #ai_agents #claude #ai_coding #vibe_coding #claude_code================================== 🧠 By: https://t.me/DataScienceM
15 260
🔥 Trending Repository: skills
📝 Description: Skills Catalog for Codex
🔗 Repository URL: https://github.com/openai/skills
📖 Readme: https://github.com/openai/skills#readme
📊 Statistics:
🌟 Stars: 2.6K stars
👀 Watchers: 26
🍴 Forks: 166 forks
💻 Programming Languages: Python - Shell - JavaScript
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
15 260
🔥 Trending Repository: review-prompts
📝 Description: AI review prompts
🔗 Repository URL: https://github.com/masoncl/review-prompts
📖 Readme: https://github.com/masoncl/review-prompts#readme
📊 Statistics:
🌟 Stars: 192 stars
👀 Watchers: 9
🍴 Forks: 29 forks
💻 Programming Languages: Python - Shell
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
15 260
🔥 Trending Repository: rag-from-scratch
📝 Description: No description available
🔗 Repository URL: https://github.com/langchain-ai/rag-from-scratch
📖 Readme: https://github.com/langchain-ai/rag-from-scratch#readme
📊 Statistics:
🌟 Stars: 6.8K stars
👀 Watchers: 60
🍴 Forks: 1.8K forks
💻 Programming Languages: Jupyter Notebook
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
15 260
🔥 Trending Repository: nanochat
📝 Description: The best ChatGPT that $100 can buy.
🔗 Repository URL: https://github.com/karpathy/nanochat
📖 Readme: https://github.com/karpathy/nanochat#readme
📊 Statistics:
🌟 Stars: 41.4K stars
👀 Watchers: 289
🍴 Forks: 5.4K forks
💻 Programming Languages: Python - Jupyter Notebook - HTML - Shell
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.me/DataScienceM
15 260
🔥 Trending Repository: qui
📝 Description: A fast, single-binary qBittorrent web UI: manage multiple instances, automate torrent workflows, and cross-seed across trackers.
🔗 Repository URL: https://github.com/autobrr/qui
🌐 Website: https://getqui.com
📖 Readme: https://github.com/autobrr/qui#readme
📊 Statistics:
🌟 Stars: 2.6K stars
👀 Watchers: 8
🍴 Forks: 74 forks
💻 Programming Languages: Go - TypeScript - CSS - Python - Makefile - HTML
🏷️ Related Topics:
#go #golang #qbittorrent #libtorrent #workflows #qbit #cross_seed #cross_seeding================================== 🧠 By: https://t.me/DataScienceM
15 260
Top 100 Data Science Interview Questions ✅
Data Science Basics
1. What is data science and how is it different from data analytics?
2. What are the key steps in a data science lifecycle?
3. What types of problems does data science solve?
4. What skills does a data scientist need in real projects?
5. What is the difference between structured and unstructured data?
6. What is exploratory data analysis and why do you do it first?
7. What are common data sources in real companies?
8. What is feature engineering?
9. What is the difference between supervised and unsupervised learning?
10. What is bias in data and how does it affect models?
Statistics and Probability
11. What is the difference between mean, median, and mode?
12. What is standard deviation and variance?
13. What is probability distribution?
14. What is normal distribution and where is it used?
15. What is skewness and kurtosis?
16. What is correlation vs causation?
17. What is hypothesis testing?
18. What are Type I and Type II errors?
19. What is p-value?
20. What is confidence interval?
Data Cleaning and Preprocessing
21. How do you handle missing values?
22. How do you treat outliers?
23. What is data normalization and standardization?
24. When do you use Min-Max scaling vs Z-score?
25. How do you handle imbalanced datasets?
26. What is one-hot encoding?
27. What is label encoding?
28. How do you detect data leakage?
29. What is duplicate data and how do you handle it?
30. How do you validate data quality?
Python for Data Science
31. Why is Python popular in data science?
32. Difference between list, tuple, set, and dictionary?
33. What is NumPy and why is it fast?
34. What is Pandas and where do you use it?
35. Difference between loc and iloc?
36. What are vectorized operations?
37. What is lambda function?
38. What is list comprehension?
39. How do you handle large datasets in Python?
40. What are common Python libraries used in data science?
Data Visualization
41. Why is data visualization important?
42. Difference between bar chart and histogram?
43. When do you use box plots?
44. What does a scatter plot show?
45. What are common mistakes in data visualization?
46. Difference between Seaborn and Matplotlib?
47. What is a heatmap used for?
48. How do you visualize distributions?
49. What is dashboarding?
50. How do you choose the right chart?
Machine Learning Basics
51. What is machine learning?
52. Difference between regression and classification?
53. What is overfitting and underfitting?
54. What is train-test split?
55. What is cross-validation?
56. What is bias-variance tradeoff?
57. What is feature selection?
58. What is model evaluation?
59. What is baseline model?
60. How do you choose a model?
Supervised Learning
61. How does linear regression work?
62. Assumptions of linear regression?
63. What is logistic regression?
64. What is decision tree?
65. What is random forest?
66. What is KNN and when do you use it?
67. What is SVM?
68. How does Naive Bayes work?
69. What are ensemble methods?
70. How do you tune hyperparameters?
Unsupervised Learning
71. What is clustering?
72. Difference between K-means and hierarchical clustering?
73. How do you choose value of K?
74. What is PCA?
75. Why is dimensionality reduction needed?
76. What is anomaly detection?
77. What is association rule mining?
78. What is DBSCAN?
79. What is cosine similarity?
80. Where is unsupervised learning used?
Model Evaluation Metrics
81. What is accuracy and when is it misleading?
82. What is precision and recall?
83. What is F1 score?
84. What is ROC curve?
85. What is AUC?
86. Difference between confusion matrix metrics?
87. What is log loss?
88. What is RMSE?
89. What metric do you use for imbalanced data?
90. How do business metrics link to ML metrics?
