Data Analytics
前往频道在 Telegram
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho
显示更多📈 Telegram 频道 Data Analytics 的分析概览
频道 Data Analytics (@dataanalyticsx) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 28 918 名订阅者,在 技术与应用 类别中位列第 4 741,并在 俄罗斯 地区排名第 22 829 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 28 918 名订阅者。
根据 10 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 490,过去 24 小时变化为 16,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 4.41%。内容发布后 24 小时内通常能获得 1.27% 的反应,占订阅者总量。
- 帖子覆盖: 每篇帖子平均可获得 1 275 次浏览,首日通常累积 368 次浏览。
- 互动与反馈: 受众积极参与,单帖平均反应数为 2。
- 主题关注点: 内容集中在 sellerflash, buybox, buyer, chaos, effortless 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:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
凭借高频更新(最新数据采集于 11 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。
28 918
订阅者
+1624 小时
+677 天
+49030 天
帖子存档
28 920
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Repost from Machine Learning with Python
Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚
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28 920
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Pandas vs Polars vs DuckDB: Which Library Should You Choose? 🤔📊
pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows 📝📈. Polars focus on fast, memory-efficient DataFrame processing ⚡💾, while DuckDB brings a SQL-first approach for querying local files and embedded analytics 🗄️🔍.
Each tool fits a different kind of local data workflow 🛠️. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases 🏆🔗.
More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ 🔗
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28 920
Repost from Machine Learning
🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨
We found this interactive website that shows you visually how transformer models work. 🌐📊
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
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28 920
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⚡️ Machine Learning Roadmap 2026: a large map for entering ML without fairy tales about "neural networks in a month" 🤖
A large Russian-language roadmap for machine learning: from the first import of numpy to LLM, RAG, fine-tuning, AI agents, and MLOps, and even Vue coding. 🚀
Inside, there's a normal structure: what to learn, in what order, why it's needed, and what should be achieved in practice after each stage. 🧠
The roadmap is divided into 7 tracks: 📊
1. Foundation: Python, mathematics, statistics, tools 🏗️
2. Classic ML: scikit-learn, tabular data, metrics, validation 📈
3. Deep Learning: PyTorch, CNN, RNN, training loop 🧠
4. LLM and transformers: attention, KV-cache, RAG, LoRA, agents 🤖
5. Generative AI: images, videos, audio, multimodality 🎨
6. MLOps and production: Docker, Kubernetes, CI/CD, monitoring, serving ⚙️
7. Specialization: CV, NLP, RecSys, RL, Safety 🎯
The roadmap doesn't sell the illusion of "training a model - becoming an ML engineer". 🚫
In real work, a lot of time is spent on data, metrics, deployment, monitoring, reproducibility, and error analysis. Model is just part of the system. 🛠️
A good idea from the roadmap: LLM doesn't make a junior a senior. It accelerates someone who already understands the basics. Without the basics, a person just becomes an operator of Copilot, who can't explain why everything broke down. 🛑
In terms of time, it's no fairy tale either: ⏳
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28 920
does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀
``#DataStructure #Efficiency #CyclicalOps #Coding #TechTips #Programming
28 920
Do you know that Python can shift sequences without slicing and creating new lists? 🤔
When you need to cyclically shift data, many use slicing:
data = data[-1:] + data[:-1]
But `deque.rotate() does this at the level of the data structure and usually works more efficiently for cyclical operations. 🚀
``python
q.rotate(1)
A negative value rotates the queue in the other direction. 🔄python q.rotate(-2)
This is useful for ring buffers, task schedulers, cyclical queues, and round-robin algorithms. ⚙️python workers.rotate(-1)
`
🔥 `deque.rotate()` allows you to implement cyclical data structures without manual index logic and without creating new lists.
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Repost from Machine Learning with Python
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Repost from Machine Learning with Python
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