Data Science & Machine Learning
Open in 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
Show more
📈 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 77 616 subscribers, ranking 2 020 in the Education category and 3 811 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 77 616 subscribers.
According to the latest data from 14 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -147 over the last 30 days and by -20 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.41%. Within the first 24 hours after publication, content typically collects 0.97% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 872 views. Within the first day, a publication typically gains 755 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
- 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 15 November, 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.
77 616
Subscribers
-2024 hours
-947 days
-14730 days
Loading data
Similar Channels
Tags Cloud
Incoming and Outgoing Mentions
---
---
---
---
---
---
Attracting Subscribers
November '26Nov '26
November '26
+94
in 2 channels
October '26
+315
in 7 channels
Get PRO
September '26
+468
in 12 channels
Get PRO
August '26
+560
in 12 channels
Get PRO
July '26
+932
in 17 channels
Get PRO
June '26
+859
in 15 channels
Get PRO
May '26
+1 098
in 11 channels
Get PRO
April '26
+629
in 16 channels
Get PRO
March '26
+315
in 4 channels
Get PRO
February '26
+843
in 12 channels
Get PRO
January '26
+1 339
in 18 channels
Get PRO
December '25
+1 264
in 6 channels
Get PRO
November '25
+1 415
in 15 channels
Get PRO
October '25
+1 119
in 18 channels
Get PRO
September '25
+598
in 24 channels
Get PRO
August '25
+2 431
in 24 channels
Get PRO
July '25
+2 317
in 36 channels
Get PRO
June '25
+1 379
in 41 channels
Get PRO
May '25
+2 702
in 49 channels
Get PRO
April '25
+4 574
in 35 channels
Get PRO
March '25
+1 830
in 36 channels
Get PRO
February '25
+1 553
in 36 channels
Get PRO
January '25
+1 334
in 55 channels
Get PRO
December '24
+1 276
in 32 channels
Get PRO
November '24
+2 847
in 28 channels
Get PRO
October '24
+3 775
in 23 channels
Get PRO
September '24
+3 717
in 24 channels
Get PRO
August '24
+3 956
in 21 channels
Get PRO
July '24
+3 673
in 32 channels
Get PRO
June '24
+3 950
in 34 channels
Get PRO
May '24
+3 398
in 37 channels
Get PRO
April '24
+2 919
in 23 channels
Get PRO
March '24
+3 461
in 14 channels
Get PRO
February '24
+2 792
in 9 channels
Get PRO
January '24
+3 714
in 11 channels
Get PRO
December '23
+2 830
in 7 channels
Get PRO
November '23
+1 010
in 12 channels
Get PRO
October '23
+474
in 9 channels
Get PRO
September '23
+409
in 0 channels
Get PRO
August '23
+946
in 0 channels
Get PRO
July '23
+875
in 0 channels
Get PRO
June '23
+744
in 0 channels
Get PRO
May '23
+372
in 0 channels
Get PRO
April '23
+577
in 0 channels
Get PRO
March '23
+253
in 0 channels
Get PRO
February '23
+263
in 0 channels
Get PRO
January '23
+287
in 0 channels
Get PRO
December '22
+303
in 0 channels
Get PRO
November '22
+400
in 0 channels
Get PRO
October '22
+275
in 0 channels
Get PRO
September '22
+143
in 0 channels
Get PRO
August '22
+383
in 0 channels
Get PRO
July '22
+304
in 0 channels
Get PRO
June '22
+142
in 0 channels
Get PRO
May '22
+125
in 0 channels
Get PRO
April '22
+107
in 0 channels
Get PRO
March '22
+190
in 0 channels
Get PRO
February '22
+147
in 0 channels
Get PRO
January '22
+291
in 0 channels
Get PRO
December '21
+289
in 0 channels
Get PRO
November '21
+19
in 0 channels
Get PRO
October '21
+39
in 0 channels
Get PRO
September '21
+274
in 0 channels
Get PRO
August '21
+168
in 0 channels
Get PRO
July '21
+323
in 0 channels
Get PRO
June '21
+360
in 0 channels
Get PRO
May '21
+382
in 0 channels
Get PRO
April '21
+327
in 0 channels
Get PRO
March '21
+434
in 0 channels
Get PRO
February '21
+449
in 0 channels
Get PRO
January '21
+480
in 0 channels
Get PRO
December '20
+1 208
in 0 channels
| Date | Subscriber Growth | Mentions | Channels | |
| 15 November | 0 | |||
| 14 November | 0 | |||
| 13 November | 0 | |||
| 12 November | +3 | |||
| 11 November | +2 | |||
| 10 November | +1 | |||
| 09 November | +6 | |||
| 08 November | +9 | |||
| 07 November | 0 | |||
| 06 November | +9 | |||
| 05 November | +7 | |||
| 04 November | +19 | |||
| 03 November | +1 | |||
| 02 November | +31 | |||
| 01 November | +6 |
Channel Posts
𝟰 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗘𝘅𝗰𝗲𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 📊
🎓 Explore 4 FREE Excel Courses to upgrade your skills!
✅ Excel Formulas & Functions
✅ Pivot Tables & Charts
✅ Data Cleaning & Analysis
✅ Dashboards & Reporting
🎯 Perfect for Students, Freshers & Aspiring Data Analysts!
🔗 𝗟𝗶𝗻𝗸 👇:-
https://pdlink.in/48mO4B2
📌 SAVE & SHARE with friends who want to master Excel! 🔥
| 2 |  𝗙𝗥𝗘𝗘 𝗩𝗜𝗥𝗧𝗨𝗔𝗟 𝗜𝗡𝗧𝗘𝗥𝗡𝗦𝗛𝗜𝗣𝗦 𝗪𝗜𝗧𝗛 𝗧𝗢𝗣 𝗖𝗢𝗠𝗣𝗔𝗡𝗜𝗘𝗦! 🎓
💻 Explore FREE Virtual Internship & Job Simulation Programs from leading companies!
✅ Learn Industry-Relevant Skills
✅ Work on Practical Tasks
✅ Self-Paced Online Learning
✅ Explore Completion Certificates
✅ Strengthen Your Resume & LinkedIn Profile
🎯 Perfect for Students, Freshers & Job Seekers
🔗 𝗟𝗶𝗻𝗸 👇:-
https://pdlink.in/4t8DRlq
📲 SAVE & SHARE with friends looking for career opportunities! | 508 |
| 3 |  𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗚𝗲𝘁 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 𝗜𝗡 𝗚𝗼𝗼𝗴𝗹𝗲🔥
Discover *4 FREE resources* to help you prepare for a career at Google!
✅ Improve Your Coding Skills
✅ Strengthen Interview Preparation
✅ Build Technical Knowledge
✅ Prepare for Top Tech Opportunities
🎓Perfect for Students, Freshers & Aspiring Software Engineers!
🔗 𝗟𝗶𝗻𝗸 👇:-
https://pdlink.in/4AwRPQy
📲 SAVE & SHARE with friends dreaming of working at Google! | 728 |
| 4 |  𝟯 𝗙𝗥𝗘𝗘 𝗛𝘂𝗯𝗦𝗽𝗼𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗦𝘁𝘂𝗱𝗲𝗻𝘁𝘀 & 𝗙𝗿𝗲𝘀𝗵𝗲𝗿𝘀 𝗶𝗻 𝟮𝟬𝟮𝟲🎓
💫HubSpot Academy offers three free online certifications that match these interests.
💫Pick one free course and try it out on your next college event and the certification is free.
🔗 𝗟𝗶𝗻𝗸 :- 👇
https://pdlink.in/4iM5xaQ
📲 *Save & Share with your friends! 🔥* | 800 |
| 5 | CTE:
WITH employee_data AS (
SELECT **
FROM employees
)
SELECT **
FROM employee_data;
View:
CREATE VIEW employee_data AS
SELECT **
FROM employees;
The main difference is that a CTE is temporary within a statement, while a view has a persistent definition in the database.
🔹 9. Real-World Data Science Use Cases
CTEs are widely used in analytical SQL queries to:
• Calculate monthly revenue before comparing sales performance.
• Identify high-value customers.
• Find products with declining sales.
• Calculate department-wise employee statistics.
• Prepare clean datasets for reporting.
• Break complex business queries into smaller steps.
Example: Find customers whose total spending is greater than 50,000.
WITH customer_spending AS (
SELECT
customer_id,
SUM(amount) AS total_spending
FROM orders
GROUP BY customer_id
)
SELECT
customer_id,
total_spending
FROM customer_spending
WHERE total_spending > 50000;
This is useful for customer segmentation and identifying high-value customers.
🔹 10. Common Mistakes to Avoid
• Forgetting the WITH keyword.
• Forgetting parentheses around the CTE query.
• Missing commas between multiple CTE definitions.
• Referencing a CTE outside the SQL statement where it is defined.
• Assuming a CTE permanently stores data.
• Giving CTEs unclear names.
• Assuming CTEs always improve query performance.
Important: A CTE improves query organization and readability, but it does not automatically make a query faster. Its execution behavior depends on the database system and query optimizer.
🔹 11. SQL Interview Questions
Q1. What is a CTE in SQL?
A CTE is a temporary named result set defined using the WITH keyword and used within a single SQL statement.
Q2. Which keyword is used to create a CTE?
The WITH keyword.
Q3. Can we create multiple CTEs in one query?
Yes. Multiple CTEs can be defined in a single WITH clause, separated by commas.
Q4. What is the difference between a CTE and a view?
A CTE exists only for one SQL statement, whereas a view is a stored database object that can be reused across queries.
Q5. Do CTEs improve query performance?
Not necessarily. CTEs mainly improve readability and organization. Performance depends on the database optimizer and query structure.
🎯 Practice Questions
Try writing SQL queries for these problems:
• Create a CTE to find employees earning more than 60,000.
• Calculate department-wise average salary using a CTE.
• Find customers whose total spending exceeds 50,000.
• Create two CTEs to calculate and filter product sales.
• Use a CTE with JOIN to display customer names and their total orders.
🎯 Key Takeaways
• CTE stands for Common Table Expression.
• CTEs are created using the WITH keyword.
• They make complex SQL queries easier to understand.
• You can create multiple CTEs in one query.
• CTEs can be used with JOIN, GROUP BY, and WHERE.
• A CTE exists only within its SQL statement.
• CTEs and views are different.
• CTEs improve readability but do not guarantee better performance.
Double Tap ❤️ For More
-----
0.997056 ₽ · /balance_help | 1 086 |
| 6 |  No text... | 635 |
| 7 | Example of a subquery:
SELECT **
FROM employees
WHERE salary > (
SELECT AVG(salary)
FROM employees
);
Equivalent approach using a CTE:
WITH average_salary AS (
SELECT AVG(salary) AS avg_salary
FROM employees
)
SELECT **
FROM employees
WHERE salary > (
SELECT avg_salary
FROM average_salary
);
Both approaches solve the same problem.
🔹 8. CTE vs View
Another important interview concept. | 656 |
| 8 |  No text... | 512 |
| 9 | You can now use this result in another query.
For example, find departments with an average salary above 70,000.
WITH department_salary AS (
SELECT
department,
AVG(salary) AS average_salary
FROM employees
GROUP BY department
)
SELECT **
FROM department_salary
WHERE average_salary > 70000;
🔹 5. Using Multiple CTEs
You can create multiple CTEs within a single SQL query. This is especially useful when solving complex business problems step by step.
Suppose you want to:
• Calculate total sales by product.
• Find products generating more than 1,00,000 in sales.
WITH product_sales AS (
SELECT
product_id,
SUM(amount) AS total_sales
FROM orders
GROUP BY product_id
),
high_sales_products AS (
SELECT **
FROM product_sales
WHERE total_sales > 100000
)
SELECT **
FROM high_sales_products;
• The first CTE calculates total sales.
• The second CTE filters high-performing products.
• The final query retrieves the result.
Important: Separate multiple CTE definitions with commas.
🔹 6. CTE with JOIN
You can combine CTEs with JOIN operations.
Example: Find total sales generated by each customer.
Suppose you have:
• customers
• orders
WITH customer_sales AS (
SELECT
customer_id,
SUM(amount) AS total_sales
FROM orders
GROUP BY customer_id
)
SELECT
c.customer_name,
cs.total_sales
FROM customers c
INNER JOIN customer_sales cs
ON c.customer_id = cs.customer_id;
The CTE calculates total sales for each customer. The main query joins that result with the customers table to retrieve customer names.
🔹 7. CTE vs Subquery
This is a common SQL interview question. | 572 |
| 10 |  No text... | 451 |
| 11 | 🚀 Data Science Roadmap 2026
📍 Phase 3: SQL for Data Science
📖 Topic 13: Common Table Expressions (CTEs) in SQL
Today, let's learn about one of the most useful SQL concepts: Common Table Expressions (CTEs).
CTEs help us write cleaner, more readable SQL queries by breaking complex queries into smaller, manageable steps.
They are frequently used in data analysis, reporting, and SQL interviews.
🔹 1. What is a CTE in SQL?
CTE stands for Common Table Expression.
It is a temporary named result set that you define using the WITH keyword and use within a single SQL statement.
Think of it as creating a temporary result that you can reference in your main query.
Example:
Suppose you want to find employees earning more than 60,000.
Without a CTE:
SELECT employee_name, salary
FROM employees
WHERE salary > 60000;
Using a CTE:
WITH high_salary_employees AS (
SELECT employee_name, salary
FROM employees
WHERE salary > 60000
)
SELECT **
FROM high_salary_employees;
Here:
• The CTE is created using WITH.
• The inner query selects employees earning more than 60,000.
• The outer query retrieves the result from the CTE.
🔹 2. Basic Syntax of a CTE
WITH cte_name AS (
SELECT column1, column2
FROM table_name
WHERE condition
)
SELECT **
FROM cte_name;
Important parts:
• WITH starts the CTE.
• cte_name is the name you give to the temporary result.
• AS introduces the query.
• The query inside parentheses defines the CTE.
• The main query uses the CTE.
🔹 3. CTE with WHERE Condition
You can use CTEs to filter records before applying further conditions.
Example: Find employees earning more than 50,000 and display only those earning more than 70,000.
WITH employee_data AS (
SELECT employee_name, salary
FROM employees
WHERE salary > 50000
)
SELECT **
FROM employee_data
WHERE salary > 70000;
The CTE first selects employees earning above 50,000. The outer query further filters the result.
🔹 4. CTE with GROUP BY
CTEs are very useful when working with aggregate functions.
Example: Calculate the average salary for each department.
WITH department_salary AS (
SELECT
department,
AVG(salary) AS average_salary
FROM employees
GROUP BY department
)
SELECT **
FROM department_salary;
Result: | 656 |
| 12 |  🚀🔥𝗚𝗢𝗢𝗚𝗟𝗘 𝗖𝗟𝗢𝗨𝗗 – 𝟰 𝗙𝗥𝗘𝗘 𝗖𝗢𝗨𝗥𝗦𝗘𝗦! ☁️📊
Learn *Data Analytics, BigQuery, Data Engineering & Looker* for FREE!
✅ Beginner-Friendly Learning
✅ Build In-Demand Tech Skills
✅ Boost Your Resume & Career Opportunities
🎯 Perfect for Students, Freshers & Job Seekers!
🔗 𝗟𝗶𝗻𝗸 :- 👇
https://pdlink.in/4AttkUb
📲 Save & Share with your friends! 🔥 | 793 |
| 13 | What is a Derived Table in SQL? | 1 106 |
| 14 | What is a Correlated Subquery? | 1 028 |
| 15 | What does EXISTS check? | 930 |
| 16 | Which operator is commonly used when a subquery returns multiple values? | 820 |
| 17 | What is a Subquery in SQL? | 759 |
| 18 | Essential Python and SQL topics for data analysts 😄👇
Python Topics:
Python Resources - @pythonanalyst
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
SQL Topics:
SQL Resources - @sqlanalyst
1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters
2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY
3. Data Filtering
- WHERE Clause
- ORDER BY
4. Data Joins
- JOIN Operations
- Subqueries
5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization
6. Database Management
- Connecting to Databases
- SQLAlchemy
7. Database Design
- Data Types
- Normalization
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Share with credits: https://t.me/sqlspecialist
Hope it helps :) | 810 |
| 19 |  🎓 𝟰 𝗖𝗲𝗿𝘁𝗶𝗳𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗜𝗻 𝟮𝟬𝟮𝟲 🚀
Want to build job-ready skills and strengthen your resume? Start learning these in-demand technologies
📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/45vk5ph
💫 𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸 :- https://pdlink.in/3SuUeuD
☁️ 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 :- :- https://pdlink.in/48kyZjt
🛡️ 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 :- :- https://pdlink.in/4fWJVID
🔁 𝗦𝗵𝗮𝗿𝗲 this with your friends and classmates! | 854 |
| 20 |  🚀 𝗠𝗜𝗖𝗥𝗢𝗦𝗢𝗙𝗧 + 𝗟𝗜𝗡𝗞𝗘𝗗𝗜𝗡 𝗙𝗥𝗘𝗘 𝗖𝗢𝗨𝗥𝗦𝗘𝗦! 🎓🔥
😍 Explore 5 FREE Career Learning Paths to boost your resume!
📊 Data Analytics | ☁️ Cloud Computing | 🤖 AI | 🔐 Cybersecurity | 💻 Software Development
✅ Learn In-Demand Skills
✅ Explore Certificate Opportunities
✅ Perfect for Students, Freshers & Job Seekers
🔗 𝗟𝗶𝗻𝗸 :- 👇
https://pdlink.in/4eOufX9
📲 Share with your friends! 🔥 | 1 005 |
