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Data Science

前往频道在 Telegram

DS По всем вопросам- @haarrp @ai_machinelearning_big_data - machine learning @pythonl - Python @itchannels_telegram - 🔥 best it channels @ArtificialIntelligencedl - AI @pythonlbooks-📚 @programming_books_it -📚 Реестр РКН: https://clck.ru/3Fk3zS

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📈 Telegram 频道 Data Science 的分析概览

频道 Data Science (@datascienceiot) 是活跃参与者。目前社区聚集了 41 817 名订阅者,在 技术与应用 类别中位列第 3 211,并在 俄罗斯 地区排名第 15 203

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 41 817 名订阅者。

根据 27 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 -102,过去 24 小时变化为 4,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 5.68%。内容发布后 24 小时内通常能获得 2.42% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 2 374 次浏览,首日通常累积 1 011 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 llm, агентов, api, октября, разработчиков 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:
DS По всем вопросам- @haarrp @ai_machinelearning_big_data - machine learning @pythonl - Python @itchannels_telegram - 🔥 best it channels @ArtificialIntelligencedl - AI @pythonlbooks-📚 @programming_books_it -📚 Реестр РКН: https://clck.ru/3...

凭借高频更新(最新数据采集于 28 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 技术与应用 类别中的关键影响点。

41 817
订阅者
+424 小时
-627
-10230
帖子存档
Artificial Intelligence for Marketing Github @datascienceiot
Artificial Intelligence for Marketing Github @datascienceiot

Classic Computer Science Problems in Python Github @datascienceiot
Classic Computer Science Problems in Python Github @datascienceiot

Deep Learning: State of the Art (2020) Book @datascienceiot

Data Analysis with Pandas @datascienceiot

An Introduction to Machine Learning Interpretability Github @datascienceiot
An Introduction to Machine Learning Interpretability Github @datascienceiot

Machine Learning Pocket Reference: Working with Structured Data in Python Github @datascienceiot
Machine Learning Pocket Reference: Working with Structured Data in Python Github @datascienceiot

Learning Pandas Github @datascienceiot
Learning Pandas Github @datascienceiot

Numpy tutorial Github @datascienceiot
Numpy tutorial Github @datascienceiot

Scipy Linear Algebra @datascienceiot

Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto (2019) @datascienceiot

Big Data, Data Mining, and Machine Learning @datascienceiot

Flask Web Development @pythonlbooks

Beginning Apache Spark 2 @datascienceiot

Data Scientists at Work

Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python @pythonlbooks

Strategic Engineering for Cloud Computing and Big Data Analytics @datascienceiot

Veracity of Big Data @datascienceiot

📚Fresh book by Nassim Taleb Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications https://arxiv.org/abs/2001.10488 @ai_machinelearning_big_data

📚Fresh book by Nassim Taleb Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications https://arxiv.org/abs/2001.10488