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Data Analytics

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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho

Network:Machine Learning with Python Russia20 799 Technologies & Applications4 306...

📈 Analytical overview of Telegram channel Data Analytics

Channel Data Analytics (@dataanalyticsx) in the English language segment is an active participant. Currently, the community unites 30 854 subscribers, ranking 4 306 in the Technologies & Applications category and 20 799 in the Russia region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 30 854 subscribers.

According to the latest data from 26 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 553 over the last 30 days and by 11 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 5.22%. Within the first 24 hours after publication, content typically collects 1.55% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 610 views. Within the first day, a publication typically gains 479 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
Thematic interests: Content is focused on key topics such as sellerflash, buybox, buyer, chaos, effortless.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 28 October, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.

30 854

Subscribers

+1124 hours

+737 days

+55330 days

1 610

Post views

~ 47924 hours

~ 83748 hours

5.22%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

30 848

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Repost from Machine Learning

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🤔 Where do I learn Claude? 🤖 You could've just asked! 🎓 13 free courses. ✅ With certificates. 📡 Straight from the source. 🧠 Claude teaches Claude. Who knew? Here's the stash: 📚 1️⃣ Claude 101 https://lnkd.in/gCPUQsRg 2️⃣ AI Fluency: Frameworks & Foundations https://lnkd.in/gS6ceZ_M 3️⃣ Introduction to Agent Skills https://lnkd.in/g_wWNiEb 4️⃣ Building with the Claude API https://lnkd.in/gDr5K_B4 5️⃣ Claude Code in Action https://lnkd.in/g9wWZbK9 6️⃣ Model Context Protocol https://lnkd.in/gAj5HqMY 7️⃣ MCP: Advanced Topics https://lnkd.in/g3eDwBFY 8️⃣ AI Fluency for Students https://lnkd.in/gKKujHGG 9️⃣ AI Fluency for Educators https://lnkd.in/gVcKnuhA 🔟 Teaching AI Fluency https://lnkd.in/g9P4gJFM 1️⃣1️⃣ AI Fluency for Nonprofits https://lnkd.in/gpsm_BVf 1️⃣2️⃣ Claude with Amazon Bedrock https://lnkd.in/gbfPjSFt 1️⃣3️⃣ Claude with Google Vertex AI https://lnkd.in/gvVgB4Ub 🚫 No waitlists. ⏳ No countdown timers. 🚫 No "enrollment closes at midnight." Just click and learn. 🚀 ♻️ Repost for everyone still searching for Claude courses! https://t.me/Python53 🔗

30 848

Polars Cheat Sheet — The Ultimate Fast DataFrame Guide ⚡🐻‍❄️ Master the most important Polars methods used in real-world data analytics and data science workflows. 📊🚀 This visual cheat sheet covers DataFrame creation, filtering, aggregations, joins, lazy execution, reshaping, sorting, and more — with practical examples and simulated outputs for faster learning. 🧠💻 Perfect for: • Data Analysts 👩‍💼 • Data Scientists 🧪 • Python Developers 🐍 • Big Data Enthusiasts 🌐 🚀 Built for speed with Rust-powered performance. ⚙️ 📌 Save this post for your next data project. Source: DataAnalyticsX

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Here’s a NumPy cheat sheet that depicts the 40 most commonly used methods from NumPy 📝🐍 #NumPy #DataAnalytics #AI #math 📊🤖 https://t.me/DataAnalyticsX 🔗📲

30 848

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Repost from Machine Learning with Python

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LLMs are the new operating system for work. 🚀💻 But most people still don’t know the difference between RAG, Embeddings, and Hallucinations. 🤔🧠 Here’s the vocabulary cheat sheet everyone in AI should know 📚✨ These foundational LLM concepts every professional, creator, founder, and tech enthusiast should know 👩‍💼👨‍💻🎨🚀 #LLM #DataScience #AI #ML https://t.me/DataAnalyticsX 📎

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If you want to become a Data Analyst or Data Scientist, these are the Python concepts you should master first: 🧹 Data Cleaning ✔️ dropna() → remove missing values ✔️ fillna() → handle nulls properly ✔️ astype() → fix data types ✔️ unique() → explore categories 📊 Exploratory Data Analysis (EDA) ✔️ describe() → quick statistics ✔️ groupby() → analyze patterns ✔️ corr() → find relationships ✔️ hist() & scatter() → visualize distributions 📈 Data Visualization ✔️ bar() → compare categories ✔️ sns.barplot() → statistical plots ✔️ sns.lineplot() → trends over time ✔️ plotly.express.scatter() → interactive charts

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