Data Science & Machine Learning

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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

Network:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India4 411 Education2 124...

📈 Analytical overview of Telegram channel Data Science & Machine Learning

Channel Data Science & Machine Learning (@datasciencefun) in the English language segment is an active participant. Currently, the community unites 75 425 subscribers, ranking 2 124 in the Education category and 4 411 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 75 425 subscribers.

According to the latest data from 03 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 991 over the last 30 days and by 46 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 3.42%. Within the first 24 hours after publication, content typically collects 1.44% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 580 views. Within the first day, a publication typically gains 1 089 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 learning, accuracy, distribution, panda, dataset.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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”

Thanks to the high frequency of updates (latest data received on 04 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.

75 425

Subscribers

+4624 hours

+1687 days

+99130 days

2 580

Post views

~ 1 08924 hours

~ 1 47848 hours

3.42%

Engagement rate

~ 2

Posts per day

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Date	Subscriber Growth	Mentions	Channels
04 June	+38
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Channel Posts

✅ Advanced SQL (Subqueries & CTEs) 🗄️🔥 👉 Now we move to advanced SQL concepts heavily used in: ✔ Data Analysis ✔ Reporting ✔ Dashboards ✔ Interviews 🔹 1. What is a Subquery? A subquery is a query written inside another query. 👉 Also called: ✅ Nested Query 🔥 2. Example of Subquery 👉 Find employees earning above average salary. SELECT name, salary FROM employees WHERE salary > ( SELECT AVG(salary) FROM employees ); How it works: 1️⃣ Inner query calculates average salary 2️⃣ Outer query filters employees 🔹 3. Types of Subqueries ✔ Single-row subquery ✔ Multiple-row subquery ✔ Correlated subquery 🔹 4. Correlated Subquery ⭐ 👉 Inner query depends on outer query. SELECT e1.name FROM employees e1 WHERE salary > ( SELECT AVG(salary) FROM employees e2 WHERE e1.department = e2.department ); 🔥 5. What is a CTE? CTE = Common Table Expression 👉 Temporary result set used inside a query. Defined using: WITH 🔹 6. Example of CTE ⭐ WITH avg_salary AS ( SELECT AVG(salary) AS avg_sal FROM employees ) SELECT * FROM employees WHERE salary > ( SELECT avg_sal FROM avg_salary ); 🔹 7. Why Use CTEs? ✔ Makes queries readable ✔ Simplifies complex logic ✔ Easier debugging 🔹 8. Difference Between Subquery & CTE Subquery : Nested inside query CTE : Defined separately Subquery : Harder to read CTE : More readable Subquery : Repeated logic possible CTE : Reusable 🔹 9. Why This is Important? ✔ Frequently asked in interviews ✔ Used in dashboards & analytics ✔ Important for real-world SQL projects 🎯 Today’s Goal ✔ Understand subqueries ✔ Learn correlated subqueries ✔ Understand CTEs ✔ Write cleaner SQL queries 👉 SQL Notes: https://whatsapp.com/channel/0029VbCyzS02ZjCwoShXXc2j 💬 Tap ❤️ for more!

2	🚀 𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝟮𝟬𝟮𝟲 – 𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗼𝘄! TCS iON is offering FREE certification courses to help students, freshers & professionals build job-ready skills from home 🌍 ✅ 100% Free Online Courses ✅ Free Verified Certificates ✅ Self-Paced Learning ✅ Beginner-Friendly Programs ✅ Learn from TCS Industry Experts 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/4nTGSDh 🔥 Excellent opportunity to gain valuable certifications from one of India’s top IT companies completely FREE.	908
3	A-Z of essential data science concepts A: Algorithm - A set of rules or instructions for solving a problem or completing a task. B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently. C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics. D: Data Mining - The process of discovering patterns and extracting useful information from large datasets. E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance. F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance. G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively. H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data. I: Imputation - The process of replacing missing values in a dataset with estimated values. J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously. K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups. L: Logistic Regression - A statistical model used for binary classification tasks. M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time. N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks. O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points. P: Precision and Recall - Evaluation metrics used to assess the performance of classification models. Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data. R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables. S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks. T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations. U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes. V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets. W: Weka - A popular open-source software tool used for data mining and machine learning tasks. X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks. Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters. Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data. Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content 😄👍 Hope this helps you 😊	1 716
4	DATA ANALYST Interview Questions (0-3 yr) (SQL, Power BI) 👉 Power BI: Q1: Explain step-by-step how you will create a sales dashboard from scratch. Q2: Explain how you can optimize a slow Power BI report. Q3: Explain Any 5 Chart Types and Their Uses in Representing Different Aspects of Data. 👉SQL: Q1: Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() functions using example. Q2 – Q4 use Table: employee (EmpID, ManagerID, JoinDate, Dept, Salary) Q2: Find the nth highest salary from the Employee table. Q3: You have an employee table with employee ID and manager ID. Find all employees under a specific manager, including their subordinates at any level. Q4: Write a query to find the cumulative salary of employees department-wise, who have joined the company in the last 30 days. Q5: Find the top 2 customers with the highest order amount for each product category, handling ties appropriately. Table: Customer (CustomerID, ProductCategory, OrderAmount) 👉Behavioral: Q1: Why do you want to become a data analyst and why did you apply to this company? Q2: Describe a time when you had to manage a difficult task with tight deadlines. How did you handle it? I have curated best top-notch Data Analytics Resources 👇👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Hope this helps you 😊	2 009
5	🚀Greetings from PVR Cloud Tech!! 🌈 🔥 Do you want to become a Master in Azure Cloud Data Engineering? If you're ready to build in-demand skills and unlock exciting career opportunities, this is the perfect place to start! 📌 Start Date: 1st June 2026 ⏰ Time: 09 PM – 10 PM IST \| Monday 🔗 𝐈𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐞𝐝 𝐢𝐧 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐥𝐢𝐯𝐞 𝐬𝐞𝐬𝐬𝐢𝐨𝐧𝐬? 👉 Message us on WhatsApp: https://wa.me/917032678595?text=Interested_to_join_Azure_Data_Engineering_live_sessions 🔹 Course Content: https://drive.google.com/file/d/1QKqhRMHx2SDNDTmPAf3₅4fA6LljKHm6/view 📱 Join WhatsApp Group: https://chat.whatsapp.com/EZghn5PVmryDgJZ1TjIMRk 📥 Register Now: https://forms.gle/LidHPdfxvNeg9LpeA Team PVR Cloud Tech :) +91-9346060794	1 183
6	✅ SQL JOINS 🗄️🔗 👉 SQL JOINS are used to combine data from multiple tables. 🔹 1. Why JOINS are Needed? In real databases, data is stored in different tables. Example: Employees Table emp_id: 1 name: Rahul Salary Table emp_id: 1 salary: 50000 👉 To combine employee name with salary → use JOIN. 🔥 2. INNER JOIN ⭐ Returns only matching rows from both tables. SELECT employees.name, salary.salary FROM employees INNER JOIN salary ON employees.emp_id = salary.emp_id; ✔ Most commonly used JOIN. 🔹 3. LEFT JOIN Returns: ✔ All rows from left table ✔ Matching rows from right table SELECT * FROM employees LEFT JOIN salary ON employees.emp_id = salary.emp_id; 👉 Non-matching rows return NULL. 🔹 4. RIGHT JOIN Returns: ✔ All rows from right table ✔ Matching rows from left table SELECT * FROM employees RIGHT JOIN salary ON employees.emp_id = salary.emp_id; 🔹 5. FULL JOIN Returns all rows from both tables. SELECT * FROM employees FULL OUTER JOIN salary ON employees.emp_id = salary.emp_id; 🔹 6. SELF JOIN ⭐ Joining a table with itself. Used for: ✔ Employee-manager relationships 🔹 7. Visual Understanding • INNER JOIN → Matching only • LEFT JOIN → All left + matching right • RIGHT JOIN → All right + matching left • FULL JOIN → Everything 🔹 8. Why JOINS are Important? ✔ Used daily in real projects ✔ Most asked interview topic ✔ Combines business data from multiple tables 🎯 Today’s Goal ✔ Understand INNER JOIN ✔ Learn LEFT/RIGHT/FULL JOIN ✔ Understand real-world use cases SQL Notes: https://whatsapp.com/channel/0029VbCyzS02ZjCwoShXXc2j 💬 Tap ❤️ for more!	2 465
7	𝗧𝗼𝗽 𝟯 𝗙𝗥𝗘𝗘 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗜𝗻 𝟮𝟬𝟮𝟲! 🚀💻 These FREE certification courses can help you build strong programming skills and stand out from the crowd 👇 ✅ Free Learning Resources ✅ Certificate Opportunities ✅ Beginner Friendly ✅ Boost Your Resume & Tech Skills 🌟 Perfect for students, freshers, aspiring developers, data analysts, and tech enthusiasts. 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/43DnP6S 📌 Start learning today and level up your career with Python!	1 842
8	✅ SQL for Data Science 🗄️📊 👉 SQL is one of the most important skills for Data Scientists and Data Analysts. Almost every company stores data inside databases, and SQL helps retrieve and analyze that data. 🔹 1. What is SQL? SQL = Structured Query Language 👉 Used to: ✔ Store data ✔ Retrieve data ✔ Filter data ✔ Analyze data 🔥 2. Common Database Systems ✔ MySQL ✔ PostgreSQL ✔ SQLite ✔ Microsoft SQL Server 🔹 3. Basic SQL Query ✅ SELECT Statement Used to retrieve data from a table. SELECT * FROM employees; 👉 ** means all columns. 🔹 4. Select Specific Columns SELECT name, salary FROM employees; 🔹 5. WHERE Clause ⭐ Used for filtering data. SELECT * FROM employees WHERE salary > 50000; 🔹 6. ORDER BY Sort data. SELECT * FROM employees ORDER BY salary DESC; ✔ ASC → Ascending ✔ DESC → Descending 🔹 7. Aggregate Functions ⭐ Used for calculations. Function: COUNT() Purpose: Count rows Function: SUM() Purpose: Total Function: AVG() Purpose: Average Function: MAX() Purpose: Highest value Function: MIN() Purpose: Lowest value ✅ Example SELECT AVG(salary) FROM employees; 🔹 8. GROUP BY ⭐ Used to group data. SELECT department, AVG(salary) FROM employees GROUP BY department; 🔹 9. Why SQL is Important? ✔ Most asked interview skill ✔ Used daily by analysts & data scientists ✔ Essential for working with databases 🎯 Today’s Goal ✔ Learn SELECT queries ✔ Filter using WHERE ✔ Use aggregate functions ✔ Understand GROUP BY 👉 SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v 🗄️🔥 💬 Tap ❤️ for more!	2 646
9	✅ End-to-End Machine Learning Project Workflow 🤖🚀 👉 Today you’ll learn how real-world ML projects are built from start to finish. This is one of the most important topics for interviews and projects. 🔹 1. Problem Understanding 👉 First understand the business problem. Example: ✔ Predict house prices ✔ Detect spam emails ✔ Customer churn prediction 🔥 2. Collect Data Data can come from: ✔ CSV files ✔ APIs ✔ Databases ✔ Web scraping 🔹 3. Data Cleaning Clean messy data: ✔ Handle missing values ✔ Remove duplicates ✔ Fix data types ✔ Handle outliers Using: Pandas 🔹 4. Exploratory Data Analysis (EDA) Understand the dataset: ✔ Trends ✔ Patterns ✔ Correlations ✔ Distributions Using: Matplotlib & Seaborn 🔹 5. Feature Engineering ⭐ Create useful features for better prediction. Examples: ✔ Extract month from date ✔ Convert categories into numbers ✔ Create new calculated columns 🔹 6. Split Data Train Data → Learn patterns Test Data → Evaluate model Usually: ✔ 80% Training ✔ 20% Testing 🔥 7. Train Machine Learning Model Choose algorithm: ✔ Linear Regression ✔ Random Forest ✔ SVM ✔ KNN 🔹 8. Evaluate Model Check performance using: ✔ Accuracy ✔ Precision ✔ Recall ✔ RMSE 🔹 9. Hyperparameter Tuning Improve model using: ✔ Grid Search ✔ Cross Validation 🔹 10. Deploy Model ⭐ Make model usable in real world. Tools: ✔ Flask ✔ Streamlit ✔ FastAPI 🔹 11. Monitor Model After deployment: ✔ Track performance ✔ Retrain if needed 🔥 12. Real-World Workflow Summary Problem → Data → Cleaning → EDA → Feature Engineering → Model → Evaluation → Deployment 🎯 Today’s Goal ✔ Understand full ML lifecycle ✔ Learn project workflow ✔ Understand deployment basics 💬 Tap ❤️ for more!	2 619
10	𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗚𝗲𝗻𝗔𝗜 𝗢𝗻𝗹𝗶𝗻𝗲 𝗪𝗲𝗯𝗶𝗻𝗮𝗿 😍 AI is replacing analysts who don't adapt. Learn Data Analytics + GenAI with IBM & Microsoft certifications. Land your dream role with dedicated placement support. 🎓1200+ Hiring Partners. 128% avg hike. 35 LPA Highest CTC in Placements. 💫𝗕𝗼𝗼𝗸 𝘆𝗼𝘂𝗿 𝗙𝗥𝗘𝗘 𝘄𝗲𝗯𝗶𝗻𝗮𝗿 :- https://pdlink.in/4uwBw3q Hurry Up ‍♂️! Limited seats are available.	1 878
11	Data Analyst vs Data Scientist vs Business Analyst vs ML Engineer vs Gen AI Engineer	2 822
12	Which of the following is a hyperparameter in KNN?	2 705
13	Which method is commonly used for Hyperparameter Tuning?	2 563
14	What are Hyperparameters?	2 520
15	In K-Fold Cross Validation, what happens?	2 417
16	What is the main purpose of Cross Validation?	2 348
17	𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀🎓 ✨ Learn In-Demand Tech Skills ✨ Boost Your Resume & LinkedIn Profile ✨ Improve Career Opportunities ✨ Self-Paced Online Learning ✨ Great for Freshers & Students 🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇: https://pdlink.in/49p31Uh 🔥 Start learning today and prepare for high-paying tech careers with Microsoft free certification programs	2 064
18	✅ Cross Validation & Hyperparameter Tuning 🤖⚙️ 👉 Building a model is not enough. We must also make sure it performs well on unseen data. This is done using: ✔ Cross Validation ✔ Hyperparameter Tuning 🔹 1. What is Cross Validation? Cross Validation checks how well a model generalizes to new data. 👉 Instead of using only one train-test split, data is divided multiple times. 🔥 2. K-Fold Cross Validation ⭐ How it Works: 1️⃣ Split data into K parts (folds) 2️⃣ Use one fold for testing 3️⃣ Use remaining folds for training 4️⃣ Repeat until every fold is tested ✅ Example If K = 5: • 4 folds → Training • 1 fold → Testing Repeated 5 times. 🔹 3. Why Cross Validation is Important? ✔ Better model evaluation ✔ Reduces overfitting risk ✔ More reliable accuracy 🔹 4. Implementation (Python) from sklearn.model_selection import cross_val_score from sklearn.linear_model import LogisticRegression model = LogisticRegression() scores = cross_val_score(model, X, y, cv=5) print(scores) 🔥 5. What are Hyperparameters? 👉 Hyperparameters are settings controlled before training the model. Examples: ✔ Number of trees in Random Forest ✔ Value of K in KNN ✔ Learning rate 🔹 6. Hyperparameter Tuning 👉 Finding the best settings for the model. 🔥 7. Grid Search ⭐ Grid Search tries multiple parameter combinations automatically. from sklearn.model_selection import GridSearchCV ✅ Example params = { "n_neighbors": [3,5,7] } 👉 Tests different K values in KNN. 🔹 8. Why Tuning is Important? ✔ Improves model performance ✔ Increases accuracy ✔ Helps build optimized ML systems 🎯 Today’s Goal ✔ Understand cross validation ✔ Learn K-Fold method ✔ Understand hyperparameters ✔ Learn Grid Search basics 💬 Tap ❤️ for more!	2 436
19	𝗔𝗜 & 𝗠𝗟 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗯𝘆 𝗖𝗖𝗘, 𝗜𝗜𝗧 𝗠𝗮𝗻𝗱𝗶😍 Freshers get 15 LPA Average Salary with AI & ML Skills! - Eligibility: Open to everyone - Duration: 6 Months - Program Mode: Online - Taught By: IIT Mandi Professors 90% Resumes without AI + ML skills are being rejected. 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇 :- https://pdlink.in/4nmI024 Get Placement Assistance With 5000+ Companies	2 083
20	Which of the following may cause overfitting?	2 616

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