DataSpoof
前往频道在 Telegram
Learn Data Science https://dataspoof4081.graphy.com/membership Artificial Intelligence Machine Learning Data Science Deep learning Computer vision NLP Big data
显示更多📈 Telegram 频道 DataSpoof 的分析概览
频道 DataSpoof (@dataspoof) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 16 139 名订阅者,在 教育 类别中位列第 12 546,并在 印度 地区排名第 26 595 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 16 139 名订阅者。
根据 21 六月, 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 -143,过去 24 小时变化为 -2,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 7.89%。内容发布后 24 小时内通常能获得 N/A% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 0 次浏览,首日通常累积 0 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 0。
- 主题关注点: 内容集中在 api, llm, pipeline, +9183182, 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:
“Learn Data Science
https://dataspoof4081.graphy.com/membership
Artificial Intelligence
Machine Learning
Data Science
Deep learning
Computer vision
NLP
Big data”
凭借高频更新(最新数据采集于 22 六月, 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。
16 139
订阅者
-224 小时
-327 天
-14330 天
帖子存档
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𝐋𝐞𝐚𝐫𝐧 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 & 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐟𝐨𝐫 𝐅𝐫𝐞𝐞
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Get complete post here
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𝐅𝐫𝐞𝐬𝐡𝐞𝐫 𝐋𝐞𝐯𝐞𝐥 𝐒𝐲𝐬𝐭𝐞𝐦 𝐃𝐞𝐬𝐢𝐠𝐧 interview questions
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1. Design a simple URL shortening service.
2. Design a basic chat application.
3. Design a file storage system.
4. Design a simple social media platform.
5. Design a simple search engine.
6. Design a simple e-commerce website.
7. Design a basic ride-sharing system.
8. Design a basic video streaming service.
9. Design a simple recommendation system.
10. Design a basic food delivery app.
11. Design a parking lot management system.
12. Design a simple music streaming service.
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14. Design a simple note-taking application.
15. Design a weather forecasting system.
16. Design a basic email service.
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18. Design a simple calendar application.
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20. Design a user authentication system.
Credit- Himanshu
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FREE certification course from Google to try in 2023
1. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗿𝗮𝘀𝗵 𝗖𝗼𝘂𝗿𝘀𝗲
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Thread from Instagram is launched
You can join us on thread
https://www.threads.net/@dataspoof
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Most common questions asked in any Data Science interview
Q1: Discuss a challenging problem you faced while working on a data science project and how did you solve it?
Q2: Question: Explain a data science project that you are most proud to work on?
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Easiest explanation on Docker
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As a financial data scientist, you’re no stranger to the complexities of navigating vast financial datasets. However, Python’s versatile libraries have revolutionized this process, making it more streamlined and efficient. Python stands out among other programming languages like R, thanks to its extensive offerings for data analysis, manipulation, visualization, and modeling.
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