Data Careers Resources & Job Updates | iamrupnath
👉 Connect LinkedIn : https://www.linkedin.com/in/rupnath-shaw Google Search => Techcompreviews IG: @iamrupnath Perfect channel for Data Careers, Job Updates Learn Excel, SQL, Python, Tableau, Power BI, AI tools, AI tips & tricks and many more
显示更多📈 Telegram 频道 Data Careers Resources & Job Updates | iamrupnath 的分析概览
频道 Data Careers Resources & Job Updates | iamrupnath (@codewithrup) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 21 400 名订阅者,在 技术与应用 类别中位列第 6 134,并在 印度 地区排名第 19 532 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 21 400 名订阅者。
根据 28 七月, 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 -413,过去 24 小时变化为 -15,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 4.36%。内容发布后 24 小时内通常能获得 1.29% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 933 次浏览,首日通常累积 277 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 apply, qualification, bachelor, degree, engineer 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:
“👉 Connect LinkedIn :
https://www.linkedin.com/in/rupnath-shaw
Google Search => Techcompreviews
IG: @iamrupnath
Perfect channel for Data Careers, Job Updates
Learn Excel, SQL, Python, Tableau, Power BI, AI tools, AI tips & tricks and many more”
凭借高频更新(最新数据采集于 29 七月, 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 技术与应用 类别中的关键影响点。
A beginner-friendly 21-lesson course by Microsoft that teaches how to build real generative AI apps—from prompts to RAG, agents, and deployment.2️⃣ rasbt/LLMs-from-scratch
Learn how LLMs actually work by building a GPT-style model step by step in pure PyTorch—ideal for deeply understanding LLM internals.3️⃣ DataTalksClub/llm-zoomcamp
A free 10-week, hands-on course focused on production-ready LLM applications, especially RAG systems built over your own data.4️⃣ Shubhamsaboo/awesome-llm-apps
A curated collection of real, runnable LLM applications showcasing agents, RAG pipelines, voice AI, and modern agentic patterns.5️⃣ panaversity/learn-agentic-ai
A practical program for designing and scaling cloud-native, production-grade agentic AI systems using Kubernetes, Dapr, and multi-agent workflows.6️⃣ dair-ai/Mathematics-for-ML
A carefully curated library of books, lectures, and papers to master the mathematical foundations behind machine learning and deep learning.7️⃣ ashishpatel26/500-AI-ML-DL-Projects-with-code
A massive collection of 500+ AI project ideas with code across computer vision, NLP, healthcare, recommender systems, and real-world ML use cases.8️⃣ armankhondker/awesome-ai-ml-resources
A clear 2025 roadmap that guides learners from beginner to advanced AI with curated resources and career-focused direction.9️⃣ spmallick/learnopencv
One of the best hands-on repositories for computer vision, covering OpenCV, YOLO, diffusion models, robotics, and edge AI.🔟 x1xhlol/system-prompts-and-models-of-ai-tools
A deep dive into how real AI tools are built, featuring 30K+ lines of system prompts, agent designs, and production-level AI patterns.🤖 AI for the Future || Double Tap ❤️ for More
