Data Analytics & AI | SQL Interviews | Power BI Resources

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🔓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

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网络:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert 印度15 573 教育7 206...

📈 Telegram 频道 Data Analytics & AI | SQL Interviews | Power BI Resources 的分析概览

频道 Data Analytics & AI | SQL Interviews | Power BI Resources (@data_visual) 英语语言赛道中的是活跃参与者。目前社区聚集了 27 200 名订阅者，在教育类别中位列第 7 206，并在印度地区排名第 15 573 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 27 200 名订阅者。

根据 23 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 137，过去 24 小时变化为 -7，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 1.74%。内容发布后 24 小时内通常能获得 N/A% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 472 次浏览，首日通常累积 0 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 4。
主题关注点： 内容集中在 |--, sql, learning, analytic, visualization 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：
“🔓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”

凭借高频更新（最新数据采集于 24 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，使其成为教育类别中的关键影响点。

27 200

订阅者

-724 小时

-237 天

+13730 天

472

帖子浏览量

无数据24 小时

无数据48 小时

1.74%

参与率

无数据

每日帖子数

Ads index

beta

数据加载中...

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吸引订阅者

六月 '26

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+208

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+212

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+462

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+484

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+585

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+581

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+543

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+708

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+847

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+2 679

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+3 616

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+798

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+512

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+600

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+730

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+1 351

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+1 170

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+1 476

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+1 890

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+1 173

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+2 953

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日期	订阅者增长	提及	频道
24 六月	+3
23 六月	+2
22 六月	0
21 六月	+1
20 六月	0
19 六月	+2
18 六月	+7
17 六月	0
16 六月	+5
15 六月	0
14 六月	+26
13 六月	+6
12 六月	+12
11 六月	+4
10 六月	+8
09 六月	+9
08 六月	0
07 六月	+5
06 六月	+6
05 六月	+10
04 六月	+14
03 六月	+15
02 六月	+8
01 六月	+3

频道帖子

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

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