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Data Analytics & AI | SQL Interviews | Power BI Resources

Data Analytics & AI | SQL Interviews | Power BI Resources

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πŸ”“Explore the fascinating world of Data Analytics & Artificial Intelligence πŸ’» Best AI tools, free resources, and expert advice to land your dream tech job. Admin: @coderfun Buy ads: https://telega.io/c/Data_Visual

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πŸ“ˆ Analytical overview of Telegram channel Data Analytics & AI | SQL Interviews | Power BI Resources

Channel Data Analytics & AI | SQL Interviews | Power BI Resources (@data_visual) in the English language segment is an active participant. Currently, the community unites 27 196 subscribers, ranking 7 190 in the Education category and 15 555 in the India region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 27 196 subscribers.

According to the latest data from 24 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 139 over the last 30 days and by 8 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 1.92%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 522 views. Within the first day, a publication typically gains 0 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
  • Thematic interests: Content is focused on key topics such as |--, sql, learning, analytic, visualization.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œπŸ”“Explore the fascinating world of Data Analytics & Artificial Intelligence πŸ’» Best AI tools, free resources, and expert advice to land your dream tech job. Admin: @coderfun Buy ads: https://telega.io/c/Data_Visual”

Thanks to the high frequency of updates (latest data received on 25 June, 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 Education category.

27 196
Subscribers
+824 hours
-107 days
+13930 days

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Channel Posts
Data Analytics & AI | SQL Interviews | Power BI Resourcesverified-telegram-icon
Quick Excel Cheatsheet! πŸ“Š 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

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βœ… Data Analytics Roadmap for Freshers πŸš€πŸ“Š 1️⃣ Understand What a Data Analyst Does πŸ” Analyze data, find insights, create dashboards, support business decisions. 2️⃣ Start with Excel πŸ“ˆ Learn: – Basic formulas – Charts & Pivot Tables – Data cleaning πŸ’‘ 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 πŸ› οΈ 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 πŸ“Š Learn: – Power BI or Tableau – Build simple dashboards πŸ’‘ Start with free versions or YouTube tutorials. 6️⃣ Practice with Real Data πŸ” Use sites like Kaggle or Data.gov – Clean, analyze, visualize – Try small case studies (sales report, customer trends) 7️⃣ Create a Portfolio πŸ’» Share projects on: – GitHub – Notion or a simple website πŸ“Œ Add visuals + brief explanations of your insights. 8️⃣ Improve Soft Skills πŸ—£οΈ Focus on: – Presenting data in simple words – Asking good questions – Thinking critically about patterns 9️⃣ Certifications to Stand Out πŸŽ“ Try: – Google Data Analytics (Coursera) – IBM Data Analyst – LinkedIn Learning basics πŸ”Ÿ Apply for Internships & Entry Jobs 🎯 Titles to look for: – Data Analyst (Intern) – Junior Analyst – Business Analyst πŸ’¬ React ❀️ for more!
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πŸ€– 𝗛𝗒π—ͺ 𝗧𝗒 π—™π—œπ—« 𝗣π—₯𝗒𝗠𝗣𝗧 π—ͺπ—œπ—§π—› π— π—˜π—§π—” 𝗣π—₯π—’π— π—£π—§π—œπ—‘π—š: ( Bookmark πŸ”– This )
πŸ€– 𝗛𝗒π—ͺ 𝗧𝗒 π—™π—œπ—« 𝗣π—₯𝗒𝗠𝗣𝗧 π—ͺπ—œπ—§π—› π— π—˜π—§π—” 𝗣π—₯π—’π— π—£π—§π—œπ—‘π—š: ( Bookmark πŸ”– This )
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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 β€” 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 β€˜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 πŸ‘πŸ‘
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Resonant is a mini-app that connects your decision patterns to your AI Agents. Generate your personal Agentic Memory Card now
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
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πŸ“ 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 🌟
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