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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
Показати більше📈 Аналітичний огляд Telegram-каналу Data Analytics
Канал Data Analytics (@dataanalyticsx) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 28 918 підписників, посідаючи 4 741 місце в категорії Технології та додатки та 22 829 місце у регіоні Росія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 28 918 підписників.
За останніми даними від 10 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 490, а за останні 24 години на 16, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 4.41%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.27% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 1 275 переглядів. Протягом першої доби публікація в середньому набирає 368 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 2.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як sellerflash, buybox, buyer, chaos, effortless.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Завдяки високій частоті оновлень (останні дані отримано 11 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
28 918
Підписники
+1624 години
+677 днів
+49030 день
Архів дописів
28 920
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 charts28 920
🧮 $40/day × 30 days = $1,200/month.
That's what my students average.
From their phone. In 10 minutes a day.
No degree needed.
No investment knowledge required.
Just Copy & Paste my moves.
I'm Tania, and this is real.
👉 Join for Free, Click here
#ad 📢 InsideAd
28 920
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28 920
🧮 $40/day × 30 days = $1,200/month.
That's what my students average.
From their phone. In 10 minutes a day.
No degree needed.
No investment knowledge required.
Just Copy & Paste my moves.
I'm Tania, and this is real.
👉 Join for Free, Click here
#ad 📢 InsideAd
28 920
Language Models Interview Handbook
151 Interview Questions, Foundation Roadmaps, Python Examples,
Architecture Diagrams and Production Playbooks for Modern LLM
28 920
Repost from Machine Learning
🔖 10 Stanford courses on AI and ML — with official pages and all materials
▶️ CS221: Artificial Intelligence
▶️ CS229: Machine Learning
▶️ CS229M: Theory of Machine Learning
▶️ CS230: Deep Learning
▶️ CS234: Reinforcement Learning
▶️ CS224N: Natural Language Processing
▶️ CS231N: Deep Learning for Computer Vision
▶️ CME295: Large Language Models
▶️ CS236: Deep Generative Models
▶️ CS336: Modeling Language from Scratch
They cover the entire spectrum: classic ML, LLM, and generative models — with theory and practice.
tags: #python #ML #LLM #AI
➡ https://t.me/MachineLearning9
28 920
🚨 26 Claude Code slash commands you should know already. Save this post !
Most Claude Code users are only using 10% of what it can do
Here are the shortcuts that change how you work :
📌 Session control:
→ /compact [instructions]: compresses your conversation, you control what stays
→ /resume [session]: pick up exactly where you left off
→ /branch [name]: explore an alternative path without touching your main session
→ /rewind: roll back to an earlier point in code or conversation
📌 Cost and usage:
→ /cost: see token usage for the session
→ /usage: check your plan limits and rate limit status
→ /extra-usage: keep working even when rate limits hit
📌 Project setup:
→ /init: creates a CLAUDE.md guide for your project
→ /memory: edits CLAUDE.md files, enables or disables auto-memory
→ /add-dir <path>: adds a working directory for file access
📌 Code quality:
→ /diff: interactive diff viewer, uncommitted changes and per-turn diffs
→ /security-review: scans pending changes for vulnerabilities
→ /plan [description]: enters plan mode before Claude writes a single line
📌 Advanced control:
→ /permissions: manages allow, ask, and deny rules for tool use
→ /agents: configures sub-agent behavior
→ /mcp: manages MCP server connections and OAuth
→ /skills: lists all available skills, built-in and custom
Most people open Claude Code and just start typing
The ones shipping faster are using these before they write the first prompt
Which one did you not know about?
♻️ Repost if this helped someone on your team
28 920
28 920
Data is everywhere. Clarity is rare.
Behind every dashboard, SQL query, or machine learning model lies a common challenge — understanding the language of data.
The 𝐀–𝐙 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 & 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 brings together 500+ essential terms across SQL, Python, Power BI, Excel, Statistics, and Machine Learning in one structured reference.
This is the layer many professionals underestimate.
Not tools. Not dashboards.
But the ability to understand, interpret, and communicate concepts with precision.
𝐖𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐢𝐬 𝐯𝐚𝐥𝐮𝐚𝐛𝐥𝐞:
- Clear definitions without unnecessary complexity
- Concepts connected across tools and domains
- Coverage from foundational terms to advanced analytics concepts
- Useful for both technical execution and business communication
𝐖𝐡𝐞𝐫𝐞 𝐭𝐡𝐢𝐬 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐢𝐦𝐩𝐚𝐜𝐭𝐟𝐮𝐥:
- During interviews, when explaining concepts matters more than just knowing them
- In projects, where misinterpreting a term can lead to incorrect insights
- In stakeholder discussions, where clarity builds credibility
- In learning journeys, where structured understanding accelerates growth
𝐒𝐭𝐫𝐨𝐧𝐠 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬 𝐝𝐨𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐝𝐚𝐭𝐚. 𝐓𝐡𝐞𝐲 𝐬𝐩𝐞𝐚𝐤 𝐢𝐭𝐬 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐰𝐢𝐭𝐡 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞.
#DataAnalytics #BusinessIntelligence #DataScience #SQL #Python #PowerBI #Excel #MachineLearning #Statistics #DataEngineering #AnalyticsCareer #DataLearning #DataProfessionals #CareerGrowth #InterviewPreparation
https://t.me/DataAnalyticsX
28 920
🧮 $40/day × 30 days = $1,200/month.
That's what my students average.
From their phone. In 10 minutes a day.
No degree needed.
No investment knowledge required.
Just Copy & Paste my moves.
I'm Tania, and this is real.
👉 Join for Free, Click here
#ad 📢 InsideAd
28 920
28 920
🚀 Python isn’t just a programming language… it’s a career accelerator.
From data science to web development, automation to AI — Python is powering it all.
Here’s the reality 👇
When you combine Python with the right tools, you unlock entire industries:
▪️ Python + Pandas → Data manipulation
▪️ Python + Scikit-learn → Machine learning
▪️ Python + TensorFlow → Deep learning
▪️ Python + Matplotlib / Seaborn → Data visualization
▪️ Python + BeautifulSoup → Web scraping
▪️ Python + Selenium → Browser automation
▪️ Python + FastAPI → High-performance APIs
▪️ Python + SQLAlchemy → Database access
▪️ Python + Flask / Django → Web development
▪️ Python + OpenCV → Computer vision
▪️ Python + Pygame → Game development
💡 One language. Endless possibilities.
28 920
𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗰𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗰𝗿𝗮𝗰𝗸 𝗔𝗜/𝗠𝗟 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀. 𝗬𝗼𝘂 𝗻𝗲𝗲𝗱 𝗮 𝘀𝗵𝗮𝗿𝗽 𝗽𝗿𝗲𝗽 𝘀𝘁𝗮𝗰𝗸.
Most people consume content.
Top candidates train with the right repos.
Here’s a tight 9-repo stack that actually moves the needle:
𝟭 ⤷ 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗣𝗿𝗶𝗺𝗲𝗿 (344k ⭐️)
https://lnkd.in/grVdK9X5
↳ Scale, trade-offs, real architecture thinking
↳ What you say in design rounds starts here
𝟮 ⤷ 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 (220k ⭐️)
https://lnkd.in/gdeUgjsi
↳ Clean implementations
↳ Sharpens fundamentals for coding + ML logic
𝟯 ⤷ 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗛𝗮𝗻𝗱𝗯𝗼𝗼𝗸 (139k ⭐️)
https://lnkd.in/gmwpV865
↳ End-to-end prep: DSA + design + behavioral
↳ Use it to structure your weeks
𝟰 ⤷ 𝗔𝘄𝗲𝘀𝗼𝗺𝗲 𝗠𝗟 (72.3k ⭐️)
https://lnkd.in/gSGVvmBB
↳ Curated ML resources, papers, tools
↳ Fill gaps fast without noise
𝟱 ⤷ 𝗔𝘄𝗲𝘀𝗼𝗺𝗲 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 (76.6k ⭐️)
https://lnkd.in/gt3R-nhm
↳ Real systems, real trade-offs
↳ Turns theory into production thinking
𝟲 ⤷ 𝟭𝟬𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗠𝗟 (50.7k ⭐️)
https://lnkd.in/gyBQF3dv
↳ Daily reps → consistency
↳ Concepts + code together
𝟳 ⤷ 𝗠𝗮𝘁𝗵 𝗳𝗼𝗿 𝗠𝗟 (15.4k ⭐️)
https://lnkd.in/g_TYTu5J
↳ Linear algebra, stats, optimization
↳ Clears “I kinda get it” confusion
𝟴 ⤷ 𝗠𝗟 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 (8.1k ⭐️)
https://lnkd.in/dTt3syu2
↳ Focused ML interview questions
↳ Practice how you’ll actually be evaluated
𝟵 ⤷ 𝟲𝟱 𝗠𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘀 (4.6k ⭐️)
https://lnkd.in/dcT9EksV
↳ Fast revision set
↳ Perfect before interviews
𝟭𝟬 ⤷ 𝗛𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗽𝗲𝗿𝗹𝘆
Fundamentals → Algorithms + Math
▼
System Design → Scalability
▼
ML Concepts → Practice Questions
▼
Daily Coding → Consistency
No jumping. No chaos. Just progression.
𝟭𝟭 ⤷ 𝗪𝗵𝗮𝘁 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗲𝘀 𝘁𝗼𝗽 𝗰𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲𝘀
↳ They don’t collect resources
↳ They reuse a small, high-signal stack
↳ They practice like it’s a system, not a mood
That’s the edge.
𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝟱𝟬 𝗿𝗲𝗽𝗼𝘀. 𝗬𝗼𝘂 𝗻𝗲𝗲𝗱 𝟵 𝗮𝗻𝗱 𝗮 𝗽𝗹𝗮𝗻.
28 920
🧮 $40/day × 30 days = $1,200/month.
That's what my students average.
From their phone. In 10 minutes a day.
No degree needed.
No investment knowledge required.
Just Copy & Paste my moves.
I'm Tania, and this is real.
👉 Join for Free, Click here
#ad 📢 InsideAd
28 920
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/Codeprogrammer
28 920
Repost from Machine Learning with Python
🔖 3 websites with tasks for improving ML skills
A good selection for those who want to improve their skills in practice, rather than just reading theory:
▶️ Deep-ML — a complete stack from matrices to neural networks;
▶️ Tensorgym — practical exercises in ML;
▶️ NeetCode ML — the ML section from the authors of a well-known platform for preparing for interviews.
tags: #ML #DataScience #DataAnalysis
➡ https://t.me/CodeProgrammer
