Data Science & Machine Learning

前往频道在 Telegram

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

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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4 204 教育2 103...

📈 Telegram 频道 Data Science & Machine Learning 的分析概览

频道 Data Science & Machine Learning (@datasciencefun) 英语语言赛道中的是活跃参与者。目前社区聚集了 75 899 名订阅者，在教育类别中位列第 2 103，并在印度地区排名第 4 204 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 75 899 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 731，过去 24 小时变化为 33，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 2.95%。内容发布后 24 小时内通常能获得 0.86% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 2 239 次浏览，首日通常累积 650 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 3。
主题关注点： 内容集中在 learning, accuracy, distribution, panda, dataset 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：
“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”

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。

75 899

订阅者

+3324 小时

+587 天

+73130 天

2 239

帖子浏览量

~ 65024 小时

~ 85748 小时

2.95%

参与率

~ 2

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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日期	订阅者增长	提及	频道
24 六月	+69
23 六月	+34
22 六月	+4
21 六月	+15
20 六月	+7
19 六月	+15
18 六月	+6
17 六月	+8
16 六月	+39
15 六月	+14
14 六月	+42
13 六月	+41
12 六月	+31
11 六月	+29
10 六月	+33
09 六月	+42
08 六月	+28
07 六月	+23
06 六月	+27
05 六月	+36
04 六月	+38
03 六月	+46
02 六月	+22
01 六月	+24

频道帖子

📊 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!

2	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 🎓	1 522
3	🔰 Important Pandas Methods for Data Science	1 617
4	🔰 Important Pandas Methods for Data Science 🔗 LearnPython	1
5	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.🏃‍♂️	1 276
6	✅ 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	1 431
7	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.	988
8	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 :)	1 182
9	🚀 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.	947
10	🧠 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!	1 358
11	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.	1 019
12	🚀 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! 🚀🔥	1 393
13	📊 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!	1 042
14	🔥 Top SQL Interview Questions with Answers 🎯 1️⃣ Find 2nd Highest Salary 📊 Table: employees id \| name \| salary 1 \| Rahul \| 50000 2 \| Priya \| 70000 3 \| Amit \| 60000 4 \| Neha \| 70000 ❓ Problem Statement: Find the second highest distinct salary from the employees table. ✅ Solution SELECT MAX(salary) FROM employees WHERE salary < ( SELECT MAX(salary) FROM employees ); 🎯 2️⃣ Find Nth Highest Salary 📊 Table: employees id \| name \| salary 1 \| A \| 100 2 \| B \| 200 3 \| C \| 300 4 \| D \| 200 ❓ Problem Statement: Write a query to find the 3rd highest salary. ✅ Solution SELECT salary FROM ( SELECT salary, DENSE_RANK() OVER(ORDER BY salary DESC) r FROM employees ) t WHERE r = 3; 🎯 3️⃣ Find Duplicate Records 📊 Table: employees id \| name 1 \| Rahul 2 \| Amit 3 \| Rahul 4 \| Neha ❓ Problem Statement: Find all duplicate names in the employees table. ✅ Solution SELECT name, COUNT() FROM employees GROUP BY name HAVING COUNT() > 1; 🎯 4️⃣ Customers with No Orders 📊 Table: customers customer_id \| name 1 \| Rahul 2 \| Priya 3 \| Amit 📊 Table: orders order_id \| customer_id 101 \| 1 102 \| 2 ❓ Problem Statement: Find customers who have not placed any orders. ✅ Solution SELECT c.name FROM customers c LEFT JOIN orders o ON c.customer_id = o.customer_id WHERE o.customer_id IS NULL; 🎯 5️⃣ Top 3 Salaries per Department 📊 Table: employees name \| department \| salary A \| IT \| 100 B \| IT \| 200 C \| IT \| 150 D \| HR \| 120 E \| HR \| 180 ❓ Problem Statement: Find the top 3 highest salaries in each department. ✅ Solution SELECT * FROM ( SELECT name, department, salary, ROW_NUMBER() OVER( PARTITION BY department ORDER BY salary DESC ) r FROM employees ) t WHERE r <= 3; 🎯 6️⃣ Running Total of Sales 📊 Table: sales date \| sales 2024-01-01 \| 100 2024-01-02 \| 200 2024-01-03 \| 300 ❓ Problem Statement: Calculate the running total of sales by date. ✅ Solution SELECT date, sales, SUM(sales) OVER(ORDER BY date) AS running_total FROM sales; 🎯 7️⃣ Employees Above Average Salary 📊 Table: employees name \| salary A \| 100 B \| 200 C \| 300 ❓ Problem Statement: Find employees earning more than the average salary. ✅ Solution SELECT name, salary FROM employees WHERE salary > ( SELECT AVG(salary) FROM employees ); 🎯 8️⃣ Department with Highest Total Salary 📊 Table: employees name \| department \| salary A \| IT \| 100 B \| IT \| 200 C \| HR \| 500 ❓ Problem Statement: Find the department with the highest total salary. ✅ Solution SELECT department, SUM(salary) AS total_salary FROM employees GROUP BY department ORDER BY total_salary DESC LIMIT 1; 🎯 9️⃣ Customers Who Placed Orders 📊 Tables: Same as Q4 ❓ Problem Statement: Find customers who have placed at least one order. ✅ Solution SELECT name FROM customers c WHERE EXISTS ( SELECT 1 FROM orders o WHERE c.customer_id = o.customer_id ); 🎯 🔟 Remove Duplicate Records 📊 Table: employees id \| name 1 \| Rahul 2 \| Rahul 3 \| Amit ❓ Problem Statement: Delete duplicate records but keep one unique record. ✅ Solution DELETE FROM employees WHERE id NOT IN ( SELECT MIN(id) FROM employees GROUP BY name ); 🚀 Pro Tip: 👉 In interviews: First explain logic Then write query Then optimize Double Tap ♥️ For More	1 284
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18	🧠 Technologies for Data Analysts! 📊 Data Manipulation & Analysis ▪️ Excel – Spreadsheet Data Analysis & Visualization ▪️ SQL – Structured Query Language for Data Extraction ▪️ Pandas (Python) – Data Analysis with DataFrames ▪️ NumPy (Python) – Numerical Computing for Large Datasets ▪️ Google Sheets – Online Collaboration for Data Analysis 📈 Data Visualization ▪️ Power BI – Business Intelligence & Dashboarding ▪️ Tableau – Interactive Data Visualization ▪️ Matplotlib (Python) – Plotting Graphs & Charts ▪️ Seaborn (Python) – Statistical Data Visualization ▪️ Google Data Studio – Free, Web-Based Visualization Tool 🔄 ETL (Extract, Transform, Load) ▪️ SQL Server Integration Services (SSIS) – Data Integration & ETL ▪️ Apache NiFi – Automating Data Flows ▪️ Talend – Data Integration for Cloud & On-premises 🧹 Data Cleaning & Preparation ▪️ OpenRefine – Clean & Transform Messy Data ▪️ Pandas Profiling (Python) – Data Profiling & Preprocessing ▪️ DataWrangler – Data Transformation Tool 📦 Data Storage & Databases ▪️ SQL – Relational Databases (MySQL, PostgreSQL, MS SQL) ▪️ NoSQL (MongoDB) – Flexible, Schema-less Data Storage ▪️ Google BigQuery – Scalable Cloud Data Warehousing ▪️ Redshift – Amazon’s Cloud Data Warehouse ⚙️ Data Automation ▪️ Alteryx – Data Blending & Advanced Analytics ▪️ Knime – Data Analytics & Reporting Automation ▪️ Zapier – Connect & Automate Data Workflows 📊 Advanced Analytics & Statistical Tools ▪️ R – Statistical Computing & Analysis ▪️ Python (SciPy, Statsmodels) – Statistical Modeling & Hypothesis Testing ▪️ SPSS – Statistical Software for Data Analysis ▪️ SAS – Advanced Analytics & Predictive Modeling 🌐 Collaboration & Reporting ▪️ Power BI Service – Online Sharing & Collaboration for Dashboards ▪️ Tableau Online – Cloud-Based Visualization & Sharing ▪️ Google Analytics – Web Traffic Data Insights ▪️ Trello / JIRA – Project & Task Management for Data Projects Data-Driven Decisions with the Right Tools! React ❤️ for more	1 520
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20	Which Tableau feature is commonly used for "What-If Analysis"?	2 316

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