现已上线！2025 年 Telegram 研究 — 年度关键洞察 
Data science/ML/AI
前往频道在 Telegram
Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
显示更多📈 Telegram 频道 Data science/ML/AI 的分析概览
频道 Data science/ML/AI (@datascience_bds) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 13 667 名订阅者，在 技术与应用 类别中位列第 9 381，并在 印度 地区排名第 31 693 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 13 667 名订阅者。
根据 08 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 150，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 7.97%。内容发布后 24 小时内通常能获得 2.27% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 1 089 次浏览，首日通常累积 310 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 5。
- 主题关注点： 内容集中在 panda, learning, row, api, ethic 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：
“Data science and machine learning hub
Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources.
For beginners, data scientists and ML engineers
👉 https://rebrand.ly/bigdatachannels
DMCA: @disclosure_bds
Contact: @mldatasci...”
凭借高频更新（最新数据采集于 09 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。
13 667
订阅者
+424 小时
+437 天
+15030 天
帖子存档
13 667
+1
Why is Kafka Called Kafka❔
Here’s a fun fact that surprises a lot of people.
The “Kafka” you use for real-time data pipelines is… named after the novelist Franz Kafka.
Why? Jay Kreps (the creator) once explained it simply:
- He liked the name.
- It sounded mysterious.
- And Kafka (the author) wrote a lot.
That last part is key.
Because Apache Kafka is all about writing: streams of events, logs, and data in motion.
So the name stuck.
Today, Millions of engineers across the globe talk about “Kafka” every single day… and most don’t realize they’re also invoking a 20th-century novelist.
It's funny how small choices like naming your project can shape how the world remembers it.
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Important LLM Terms
🔹 Transformer Architecture
🔹 Attention Mechanism
🔹 Pre-training
🔹 Fine-tuning
🔹 Parameters
🔹 Self-Attention
🔹 Embeddings
🔹 Context Window
🔹 Masked Language Modeling (MLM)
🔹 Causal Language Modeling (CLM)
🔹 Multi-Head Attention
🔹 Tokenization
🔹 Zero-Shot Learning
🔹 Few-Shot Learning
🔹 Transfer Learning
🔹 Overfitting
🔹 Inference
🔹 Language Model Decoding
🔹 Hallucination
🔹 Latency
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📚 Data Science Riddle
In a medical diagnosis project, what's more important?
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13 667
+2
ML models don’t all think alike 🤖
❇️ Naive Bayes = probability
❇️ KNN = proximity
❇️ Discriminant Analysis = decision boundaries
Different paths, same goal: accurate classification.
Which one do you reach for first?
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📚 Data Science Riddle
A dataset has 20% missing values in a critical column. What's the most practical choice?
13 667
📚 Data Science Riddle
Which Metric is best for imbalanced classification?
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📊 Infographic Elements That Every Data Person Should Master 🚀
After years of working with data, I can tell you one thing:
👉 The
chart ou choose is as important as the data itself.
Here’s your quick visual toolkit 👇
🔹 Timelines
* Sequential ⏩ great for processes
* Scaled ⏳ best for real dates/events
🔹 Circular Charts
* Donut 🍩 & Pie 🥧 for proportions
* Radial 🌌 for progress or cycles
* Venn 🎯 when you want to show overlaps
🔹 Creative Comparisons
* Bubble 🫧 & Area 🔵 for impact by size
* Dot Matrix 🔴 for colorful distributions
* Pictogram 👥 when storytelling matters most
🔹 Classic Must-Haves
* Bar 📊 & Histogram 📏 (clear, reliable)
* Line 📈 for trends
* Area 🌊 & Stacked Area for the “big picture”
🔹 Advanced Tricks
* Stacked Bar 🏗 when categories add up
* Span 📐 for ranges
* Arc 🌈 for relationships
💡 Pro tip from experience:
If your audience doesn’t “get it” in 3 seconds, change the chart. The best visualizations speak louder than numbers
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📚 Data Science Riddle
Which algorithm is most sensitive to feature scaling?
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The RAG Developer Stack 2025 - Build Intelligent Al That Thinks, Remembers & Acts
