Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Data Analytics & AI | SQL Interviews | Power BI Resources
Kanalga Telegram’da o‘tish
🔓Explore the fascinating world of Data Analytics & Artificial Intelligence 💻 Best AI tools, free resources, and expert advice to land your dream tech job. Admin: @coderfun Buy ads: https://telega.io/c/Data_Visual
Ko'proq ko'rsatish📈 Telegram kanali Data Analytics & AI | SQL Interviews | Power BI Resources analitikasi
Data Analytics & AI | SQL Interviews | Power BI Resources (@data_visual) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 27 200 obunachidan iborat bo'lib, Taʼlim toifasida 7 206-o'rinni va Hindiston mintaqasida 15 573-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 27 200 obunachiga ega bo‘ldi.
23 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 137 ga, so‘nggi 24 soatda esa -7 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 1.74% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 472 marta ko‘riladi; birinchi sutkada odatda 0 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 4 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent |--, sql, learning, analytic, visualization kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“🔓Explore the fascinating world of Data Analytics & Artificial Intelligence
💻 Best AI tools, free resources, and expert advice to land your dream tech job.
Admin: @coderfun
Buy ads: https://telega.io/c/Data_Visual”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 24 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
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Kanal postlari
Quick Excel Cheatsheet! 📊
Basic Formulas
1. Add: =A1+B1
2. Subtract: =A1-B1
3. Multiply: =A1*B1
4. Divide: =A1/B1
5. Average: =AVERAGE(A1:A10)
6. Sum: =SUM(A1:A10)
Logical Functions
1. IF: =IF(A1>10, "Yes", "No")
2. AND: =AND(A1>5, B1<10)
3. OR: =OR(A1=1, B1=2)
4. EXACT (case-sensitive match): =EXACT(A1, B1)
Lookup Functions
1. VLOOKUP: =VLOOKUP(A1, Table, 2, FALSE)
2. HLOOKUP: =HLOOKUP(A1, Table, 2, FALSE)
3. XLOOKUP: =XLOOKUP(A1, Range1, Range2)
Counting Data Types
1. Count numbers: =COUNT(A1:A10)
2. Count non-empty: =COUNTA(A1:A10)
3. Count blanks: =COUNTBLANK(A1:A10)
4. Is number: =ISNUMBER(A1)
5. Is text: =ISTEXT(A1)
React ❤️ for more
| 2 | ✅ Data Analytics Roadmap for Freshers 🚀📊
1️⃣ Understand What a Data Analyst Does
🔍 Analyze data, find insights, create dashboards, support business decisions.
2️⃣ Start with Excel
📈 Learn:
– Basic formulas
– Charts & Pivot Tables
– Data cleaning
💡 Excel is still the #1 tool in many companies.
3️⃣ Learn SQL
🧩 SQL helps you pull and analyze data from databases.
Start with:
– SELECT, WHERE, JOIN, GROUP BY
🛠️ Practice on platforms like W3Schools or Mode Analytics.
4️⃣ Pick a Programming Language
🐍 Start with Python (easier) or R
– Learn pandas, matplotlib, numpy
– Do small projects (e.g. analyze sales data)
5️⃣ Data Visualization Tools
📊 Learn:
– Power BI or Tableau
– Build simple dashboards
💡 Start with free versions or YouTube tutorials.
6️⃣ Practice with Real Data
🔍 Use sites like Kaggle or Data.gov
– Clean, analyze, visualize
– Try small case studies (sales report, customer trends)
7️⃣ Create a Portfolio
💻 Share projects on:
– GitHub
– Notion or a simple website
📌 Add visuals + brief explanations of your insights.
8️⃣ Improve Soft Skills
🗣️ Focus on:
– Presenting data in simple words
– Asking good questions
– Thinking critically about patterns
9️⃣ Certifications to Stand Out
🎓 Try:
– Google Data Analytics (Coursera)
– IBM Data Analyst
– LinkedIn Learning basics
🔟 Apply for Internships & Entry Jobs
🎯 Titles to look for:
– Data Analyst (Intern)
– Junior Analyst
– Business Analyst
💬 React ❤️ for more! | 0 |
| 3 |  🤖 𝗛𝗢𝗪 𝗧𝗢 𝗙𝗜𝗫 𝗣𝗥𝗢𝗠𝗣𝗧 𝗪𝗜𝗧𝗛 𝗠𝗘𝗧𝗔 𝗣𝗥𝗢𝗠𝗣𝗧𝗜𝗡𝗚:
( Bookmark 🔖 This ) | 0 |
| 4 | If you’re just starting out in Data Analytics, it’s super important to build the right habits early.
Here’s a simple plan for beginners to grow both technical and problem-solving skills together:
If You Just Started Learning Data Analytics, Focus on These 5 Baby Steps:
1. Don’t Just Watch Tutorials — Build Small Projects
After learning a new tool (like SQL or Excel), create mini-projects:
- Analyze your expenses
- Explore a free dataset (like Netflix movies, COVID data)
2. Ask Business-Like Questions Early
Whenever you see a dataset, practice asking:
- What problem could this data solve?
- Who would care about this insight?
3. Start a ‘Data Journal’
Every day, note down:
- What you learned
- One business question you could answer with data (Helps you build real-world thinking!)
4. Practice the Basics 100x
Get very comfortable with:
- SELECT, WHERE, GROUP BY (SQL)
- Pivot tables and charts (Excel)
- Basic cleaning (Power Query / Python pandas)
_Mastering basics > learning 50 fancy functions._
5. Learn to Communicate Early
Explain your mini-projects like this:
- What was the business goal?
- What did you find?
- What should someone do based on it?
React with ❤️ if you need a beginner-friendly roadmap to start your data analytics career
Data Analytics Free Resources: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
ENJOY LEARNING 👍👍 | 0 |
| 5 | Resonant is a mini-app that connects your decision patterns to your AI Agents. Generate your personal Agentic Memory Card now!
https://t.me/ResonantAlphaBot/resonant?startapp | 0 |
| 6 | 📝 12 Essential Articles for Data Scientists
🏷 Article: Seq2Seq Learning with NN
https://arxiv.org/pdf/1409.3215
An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning.
🏷 Article: GANs
https://arxiv.org/pdf/1406.2661
An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence.
🏷 Article: Attention is All You Need
https://arxiv.org/pdf/1706.03762
This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models.
🏷 Article: Deep Residual Learning
https://arxiv.org/pdf/1512.03385
This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process.
🏷 Article: Batch Normalization
https://arxiv.org/pdf/1502.03167
This paper introduced a technique that facilitates faster and more stable training of neural networks.
🏷 Article: Dropout
https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
A straightforward method designed to prevent overfitting in neural networks.
🏷 Article: ImageNet Classification with DCNN
https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
The first successful application of a deep neural network for image recognition.
🏷 Article: Support-Vector Machines
https://link.springer.com/content/pdf/10.1007/BF00994018.pdf
This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification.
🏷 Article: A Few Useful Things to Know About ML
https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf
A comprehensive collection of practical and empirical insights regarding machine learning.
🏷 Article: Gradient Boosting Machine
https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf
This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM.
🏷 Article: Latent Dirichlet Allocation
https://jmlr.org/papers/volume3/blei03a/blei03a.pdf
This work introduced a model for text analysis capable of identifying the topics discussed within an article.
🏷 Article: Random Forests
https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy.
https://t.me/CodeProgrammer 🌟 | 0 |
