Data Science & Machine Learning

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Tarmoq:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert Hindiston4 204 Taʼlim2 103...

📈 Telegram kanali Data Science & Machine Learning analitikasi

Data Science & Machine Learning (@datasciencefun) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 75 933 obunachidan iborat bo'lib, Taʼlim toifasida 2 103-o'rinni va Hindiston mintaqasida 4 204-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 75 933 obunachiga ega bo‘ldi.

23 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 731 ga, so‘nggi 24 soatda esa 33 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

Tasdiqlash holati: Tasdiqlanmagan
Jalb etish (ER): Auditoriya o‘rtacha 2.95% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.86% ini tashkil etuvchi reaksiyalarni to‘playdi.
Post qamrovi: Har bir post o‘rtacha 2 239 marta ko‘riladi; birinchi sutkada odatda 650 ta ko‘rish yig‘iladi.
Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
Tematik yo‘nalishlar: Kontent learning, accuracy, distribution, panda, dataset kabi asosiy mavzularga jamlangan.

📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data”

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

What is a major benefit of using LOD expressions?

Anonymous voting

2	Which LOD expression removes dimensions from the current level of detail?	2
3	Which LOD expression adds dimensions to the current level of detail?	1
4	Which LOD expression calculates values at a specific level regardless of the current view?	2
5	What does LOD stand for in Tableau?	1
6	🎓 𝗚𝗼𝗼𝗴𝗹𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟲 🚀 Learn job-ready skills from Google and boost your resume?🌟 ✔️ Learn from Google Experts ✔️ Industry-Recognized Certificates ✔️ Beginner-Friendly Learning Paths ✔️ Self-Paced Courses ✔️ Enhance Resume & LinkedIn Profile ✔️ Build Job-Ready Skills 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/4vjLGVq ⏳ Start Learning Today & Upgrade Your Career!	549
7	💻 Popular Coding Languages & Their Uses 🚀 There are many programming languages, each serving different purposes. Here are some key ones you should know: 🔹 1. Python – Beginner-friendly, versatile, and widely used in data science, AI, web development, and automation. 🔹 2. JavaScript – Essential for frontend and backend web development, powering interactive websites and applications. 🔹 3. Java – Used for enterprise applications, Android development, and large-scale systems due to its stability. 🔹 4. C++ – High-performance language ideal for game development, operating systems, and embedded systems. 🔹 5. C# – Commonly used in game development (Unity), Windows applications, and enterprise software. 🔹 6. Swift – The go-to language for iOS and macOS development, known for its efficiency. 🔹 7. Go (Golang) – Designed for high-performance applications, cloud computing, and network programming. 🔹 8. Rust – Focuses on memory safety and performance, making it great for system-level programming. 🔹 9. SQL – Essential for database management, allowing efficient data retrieval and manipulation. 🔹 10. Kotlin – Popular for Android app development, offering modern features compared to Java. 🔥 React ❤️ for more 😊🚀	938
8	📊 𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 Here's an amazing opportunity from TCS to learn essential data analytics skills completely FREE and earn a certificate 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/4waJYWJ 🔥 Data Analytics continues to be one of the most in-demand career paths, and this free course is a great first step toward building job-ready skills. ⏳ Don't miss this opportunity to upskill and boost your career!	1 100
9	𝟳 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟲😍 ✅ 100% FREE & Beginner-Friendly ✅ Learn AI, ML, Data Science, Ethical Hacking & More ✅ Taught by Industry Experts ✅ Practical & Hands-on Learning 📢 Start learning today and take your tech career to the next level! 🚀 𝐋𝐢𝐧𝐤 👇:- https://pdlink.in/4bQ6FpS Enroll For FREE & Get Certified 🎓	1 748
10	🔰 Important Pandas Methods for Data Science	1 751
11	🔰 Important Pandas Methods for Data Science 🔗 LearnPython	1
12	𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 - 𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸𝗗𝗲𝘃 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗪𝗶𝘁𝗵 𝗚𝗲𝗻𝗔𝗜 😍 Curriculum designed and taught by alumni from IITs & leading tech companies. Learn Coding & Get Placed In Top Tech Companies 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:- 💼 Avg. Package: ₹7.2 LPA \| Highest: ₹41 LPA 𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰 👇:- https://pdlink.in/42WOE5H Hurry! Limited seats are available.🏃‍♂️	1 446
13	✅ Tableau LOD Expressions Level of Detail 📊🔥 👉 LOD Level of Detail Expressions are one of the most powerful and frequently asked Tableau interview topics. They allow you to perform calculations at a different level of granularity than what is currently shown in the visualization. 🔹 1. What are LOD Expressions? LOD Expressions let you control how data is aggregated. 👉 Normally, Tableau calculates values based on the current view. 👉 LOD lets you calculate values independently of the visualization. 🔥 2. Why Use LOD Expressions? ✔ Calculate metrics at different levels ✔ Compare individual values to totals ✔ Create advanced KPIs ✔ Improve dashboard flexibility 🔹 3. Types of LOD Expressions ⭐ There are three main types: ✅ FIXED Calculates values at a specific level. { FIXED [Region] : SUM([Sales]) } 👉 Calculates total sales for each region regardless of what's in the view. ✅ INCLUDE Adds dimensions to the current view. { INCLUDE [Customer Name] : SUM([Sales]) } 👉 Includes customer-level calculations. ✅ EXCLUDE Removes dimensions from the current view. { EXCLUDE [Product] : SUM([Sales]) } 👉 Ignores product-level detail. 🔹 4. Example of FIXED LOD Suppose you want: 👉 Total Sales by Region Even when viewing sales by product. { FIXED [Region] : SUM([Sales]) } This value remains constant for the region. 🔹 5. Real-World Example Calculate each customer's contribution to total regional sales: SUM([Sales]) / { FIXED [Region] : SUM([Sales]) } 🔹 6. Difference Between Aggregate & LOD Aggregate: Depends on current view, Simple calculations, Dynamic with visualization LOD: Independent of current view, Advanced calculations, Fixed granularity control 🔹 7. When to Use LOD? ✔ Customer contribution analysis ✔ Regional benchmarking ✔ Advanced KPIs ✔ Performance comparisons 🔹 8. Common Interview Question ⭐ Q: Which LOD expression ignores the dimensions in the current view? ✅ Answer: FIXED 🔹 9. Why LOD is Important? ✔ Advanced Tableau skill ✔ Frequently asked in interviews ✔ Used in enterprise dashboards ✔ Makes complex calculations easier 🎯 Today's Goal ✔ Understand FIXED, INCLUDE, EXCLUDE ✔ Learn granularity concepts ✔ Build advanced Tableau calculations 👉 Double Tap ❤️ For More	1 621
14	𝗔𝗰𝗰𝗲𝗻𝘁𝘂𝗿𝗲 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲 📊 Join the Accenture Virtual Internship Program and learn industry-relevant analytics skills with a free certificate 🌍 ✨ Learn from Accenture Industry Experts ✨ Boost Your Resume & LinkedIn Profile ✨ Gain Practical Analytics Experience ✨ Improve Career Opportunities in 2026 ✨ Great for Students & Freshers 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/42TuhXg 🔥 Start your Data Analytics journey today and gain valuable virtual internship experience from a top global company.	1 097
15	Essential SQL Topics for Data Analysts 👇 - Basic Queries: SELECT, FROM, WHERE clauses. - Sorting and Filtering: ORDER BY, GROUP BY, HAVING. - Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN. - Aggregation Functions: COUNT, SUM, AVG, MIN, MAX. - Subqueries: Embedding queries within queries. - Data Modification: INSERT, UPDATE, DELETE. - Indexes: Optimizing query performance. - Normalization: Ensuring efficient database design. - Views: Creating virtual tables for simplified queries. - Understanding Database Relationships: One-to-One, One-to-Many, Many-to-Many. Window functions are also important for data analysts. They allow for advanced data analysis and manipulation within specified subsets of data. Commonly used window functions include: - ROW_NUMBER(): Assigns a unique number to each row based on a specified order. - RANK() and DENSE_RANK(): Rank data based on a specified order, handling ties differently. - LAG() and LEAD(): Access data from preceding or following rows within a partition. - SUM(), AVG(), MIN(), MAX(): Aggregations over a defined window of rows. Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz Share with credits: https://t.me/sqlspecialist Hope it helps :)	1 328
16	🚀 𝗧𝗼𝗽 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗟𝗲𝗮𝗿𝗻 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘! 💼🔥 These free courses can help you build in-demand tech skills for 2026 👇 ✅ Microsoft Azure Fundamentals ☁️ ✅ Power BI Data Analyst 📊 ✅ Data Analysis Using Excel 📈 ✅ Azure AI & Generative AI Courses 🤖 ✅ SQL & Data Engineering Learning Paths 💻 💡 Why Learn Microsoft Certifications? ✨ Industry-Recognized Credentials ✨ Hands-on Learning ✨ High Demand Skills ✨ Better Career Opportunities 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/4nLVyVc 🔥 Start learning today and future-proof your career with Microsoft-certified skills.	1 041
17	🧠 7 Resume Tips for Data Science & ML Roles 📄✅ 1️⃣ Start with a Strong Summary ⦁ Highlight skills, tools, and domain experience ⦁ Mention years of experience and key achievements 2️⃣ Showcase Projects that Matter ⦁ Focus on real-world impact, not just toy datasets ⦁ Mention metrics (e.g., “Improved accuracy by 12%”) 3️⃣ Tailor for the Role ⦁ Align keywords with the job description ⦁ Use relevant tools and models mentioned in the listing 4️⃣ Highlight Tools & Techniques ⦁ Python, SQL, Pandas, Scikit-learn, TensorFlow ⦁ Also list Git, Docker, AWS if used 5️⃣ Add Business Context ⦁ Mention how your model helped reduce costs, improve conversion, etc. ⦁ Show you understand the why behind the model 6️⃣ Keep It One Page ⦁ Concise and clean layout ⦁ Use bullet points, not long paragraphs 7️⃣ Include Public Work ⦁ GitHub, blog posts, Kaggle profile ⦁ Show you build, write, and share 💬 Double tap ❤️ for more!	1 449
18	𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 \| 𝟭𝟬𝟬% 𝗝𝗼𝗯 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲😍 ✅ Build Python, Machine Learning & AI Skills ✅ 60+ Hiring Drives Every Month ✅ 1-on-1 Expert Mentorship ✅ 500+ Partner Companies ✅ Highest Salary: ₹12.65 LPA 𝗕𝗼𝗼𝗸 𝗮 𝗙𝗥𝗘𝗘 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 :- 👇:- https://pdlink.in/4fdWxJB Hurry Up 🏃‍♂️! Limited seats are available.	1 078
19	🚀 Complete Roadmap to Become a Data Scientist in 5 Months 📅 Week 1-2: Fundamentals ✅ Day 1-3: Introduction to Data Science, its applications, and roles. ✅ Day 4-7: Brush up on Python programming 🐍. ✅ Day 8-10: Learn basic statistics 📊 and probability 🎲. 🔍 Week 3-4: Data Manipulation & Visualization 📝 Day 11-15: Master Pandas for data manipulation. 📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization. 🤖 Week 5-6: Machine Learning Foundations 🔬 Day 21-25: Introduction to scikit-learn. 📊 Day 26-30: Learn Linear & Logistic Regression. 🏗 Week 7-8: Advanced Machine Learning 🌳 Day 31-35: Explore Decision Trees & Random Forests. 📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction. 🧠 Week 9-10: Deep Learning 🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras. 📸 Day 46-50: Learn CNNs & RNNs for image & text data. 🏛 Week 11-12: Data Engineering 🗄 Day 51-55: Learn SQL & Databases. 🧹 Day 56-60: Data Preprocessing & Cleaning. 📊 Week 13-14: Model Evaluation & Optimization 📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning. 📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score). 🏗 Week 15-16: Big Data & Tools 🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark). ☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure). 🚀 Week 17-18: Deployment & Production 🛠 Day 81-85: Deploy models using Flask or FastAPI. 📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku). 🎯 Week 19-20: Specialization 📝 Day 91-95: Choose NLP or Computer Vision, based on your interest. 🏆 Week 21-22: Projects & Portfolio 📂 Day 96-100: Work on Personal Data Science Projects. 💬 Week 23-24: Soft Skills & Networking 🎤 Day 101-105: Improve Communication & Presentation Skills. 🌐 Day 106-110: Attend Online Meetups & Forums. 🎯 Week 25-26: Interview Preparation 💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank). 📂 Day 116-120: Review your projects & prepare for discussions. 👨‍💻 Week 27-28: Apply for Jobs 📩 Day 121-125: Start applying for Entry-Level Data Scientist positions. 🎤 Week 29-30: Interviews 📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems. 🔄 Week 31-32: Continuous Learning 📰 Day 131-135: Stay updated with the Latest Data Science Trends. 🏆 Week 33-34: Accepting Offers 📝 Day 136-140: Evaluate job offers & Negotiate Your Salary. 🏢 Week 35-36: Settling In 🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning! 🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥	1 480
20	📊 𝗖𝗶𝘀𝗰𝗼 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 \| 𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗼𝘄! 🚀 🚀 Data Analytics is one of the most in-demand career paths in 2026 🔥 Program Benefits: ✅ FREE Certification ✅ Self-Paced Learning ✅ Beginner Friendly ✅ Industry-Relevant Curriculum ✅ Resume & LinkedIn Booster 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/4gaeVVV 📢 Share with friends who want to start a career in Data Analytics!	1 100

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