¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
Data Analytics
Ir al canal en Telegram
Perfect channel to learn Data Analytics Learn SQL, Python, Alteryx, Tableau, Power BI and many more For Promotions: @coderfun @love_data
Mostrar más📈 Análisis del canal de Telegram Data Analytics
El canal Data Analytics (@sqlspecialist) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 109 661 suscriptores, ocupando la posición 1 126 en la categoría Tecnologías y Aplicaciones y el puesto 2 339 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 109 661 suscriptores.
Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 529, y en las últimas 24 horas de 20, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.83%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.72% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 097 visualizaciones. En el primer día suele acumular 784 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 8.
- Intereses temáticos: El contenido se centra en temas clave como row, sql, analytic, analyst, visualization.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Perfect channel to learn Data Analytics
Learn SQL, Python, Alteryx, Tableau, Power BI and many more
For Promotions: @coderfun @love_data”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 24 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
109 661
Suscriptores
+2024 horas
-647 días
+52930 días
Archivo de publicaciones
109 661
🔄 Window Functions (ROW_NUMBER, RANK, PARTITION BY)
Window functions perform calculations across rows related to the current row — but unlike GROUP BY, they don’t collapse your data!
They are super useful for running totals, rankings, and finding duplicates.
1. ROW_NUMBER()
Gives a unique number to each row within a partition of a result set.
SELECT name, department, salary,
ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS row_num
FROM employees;
Ranks employees by salary within each department.
2. RANK() vs DENSE_RANK()
RANK() leaves gaps after ties.
DENSE_RANK() doesn’t.
SELECT name, salary,
RANK() OVER (ORDER BY salary DESC) AS rank,
DENSE_RANK() OVER (ORDER BY salary DESC) AS dense_rank
FROM employees;
3. PARTITION BY
It’s like a GROUP BY, but for window functions.
SELECT department, name, salary,
AVG(salary) OVER (PARTITION BY department) AS dept_avg_salary
FROM employees;
This shows each employee's salary alongside the average salary of their department — without collapsing the rows.
Other Useful Window Functions:
NTILE(n) – Divides rows into n buckets
LAG() / LEAD() – Look at previous/next row’s value
SUM() / AVG() over a window – for running totals
React with ❤️ if you're pumped for the next one: ⚙️ Data Manipulation (INSERT, UPDATE, DELETE).
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
109 661
𝗔𝗜 & 𝗠𝗟 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
Qualcomm—a global tech giant offering completely FREE courses that you can access anytime, anywhere.
✅ 100% Free — No hidden charges, subscriptions, or trials
✅ Created by Industry Experts
✅ Self-paced & Online — Learn from anywhere, anytime
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/3YrFTyK
Enroll Now & Get Certified 🎓
109 661
What is the main advantage of using a Common Table Expression (CTE) in SQL?
109 661
Here comes one of the cleanest and most powerful features in SQL:
🧠 Common Table Expressions (CTEs)
A CTE (Common Table Expression) is a temporary result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. It makes your queries more readable and modular, especially when dealing with complex logic.
Syntax:
WITH cte_name AS (
SELECT column1, column2
FROM table_name
WHERE condition
)
SELECT *
FROM cte_name;
Example: Let’s say we want to get the top 3 highest-paid employees from each department.
WITH ranked_employees AS (
SELECT name, department, salary,
ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rank
FROM employees
)
SELECT *
FROM ranked_employees
WHERE rank <= 3;
Why to use CTEs:
- Easier to break down complex queries
- You can use them multiple times in the main query
- Readable and cleaner than subqueries
You can chain multiple CTEs together and even write recursive CTEs for hierarchical data.
React with ❤️ if you're excited for the next one: 🔄 Window Functions (ROW_NUMBER, RANK, PARTITION BY).
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
109 661
𝟰 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗙𝗿𝗲𝗲 𝗦𝗤𝗟 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀😍
Struggling to learn SQL as a beginner data analyst? You’re not alone — and you don’t have to stay stuck👋
Here are 4 top-notch, beginner-friendly SQL courses that are 100% free🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44lQvmw
Enroll For FREE & Get Certified 🎓️
109 661
Now, let’s dive into a couple of powerful but often overlooked SQL features:
🧾 Views & Indexes (Basics)
1. Views
A View is a virtual table based on a SQL query. It doesn’t store data itself — it just stores the query logic.
Why use Views?
- Simplifies complex queries
- Improves code reusability
- Adds a layer of security by hiding certain columns
Creating a View:
CREATE VIEW high_salary_employees AS
SELECT name, salary
FROM employees
WHERE salary > 80000;
Using the View:
SELECT * FROM high_salary_employees;
Updating a View:
CREATE OR REPLACE VIEW high_salary_employees AS
SELECT name, salary, department
FROM employees
WHERE salary > 80000;
2. Indexes
An Index is like a book’s table of contents — it helps the database find data faster, especially in large tables.
Why use Indexes?
- Speeds up SELECT queries
- Great for columns used in WHERE, JOIN, and ORDER BY
Creating an Index:
CREATE INDEX idx_employee_name ON employees(name);
Indexes can slow down INSERT, UPDATE, DELETE because they need to update the index too.
Don’t overuse them — only on frequently searched or joined columns.
React with ❤️ if you’re ready for the next interesting concept: 🧠 Common Table Expressions (CTEs).
109 661
𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐏𝐫𝐨𝐠𝐫𝐚𝐦😍
Learn Full Stack Development from IIT Alumni & Top Tech Experts.
𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:-
60+ Hiring Drives Every Month
🌟 Trusted by 7500+ Students
🤝 500+ Hiring Partners
💼 Avg. Package: ₹7.2 LPA | Highest: ₹41 LPA
Eligibility: BTech / BCA / BSc / MCA / MSc
𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐍𝐨𝐰 👇:-
https://pdlink.in/4hO7rWY
Hurry! Limited seats available. 🏃♀️
109 661
Many people still aren't fully utilizing the power of Telegram.
There are numerous channels on Telegram that can help you find the latest job and internship opportunities?
Here are some of my top channel recommendations to help you get started 👇👇
Latest Jobs & Internships: https://t.me/getjobss
Jobs Preparation Resources:
https://t.me/jobinterviewsprep
Data Science Jobs:
https://t.me/datasciencej
Interview Tips:
https://t.me/Interview_Jobs
Data Analyst Jobs:
https://t.me/jobs_SQL
AI Jobs:
https://t.me/AIjobz
Remote Jobs:
https://t.me/jobs_us_uk
FAANG Jobs:
https://t.me/FAANGJob
Software Developer Jobs: https://t.me/internshiptojobs
If you found this helpful, don’t forget to like, share, and follow for more resources that can boost your career journey!
Let me know if you know any other useful telegram channel
ENJOY LEARNING👍👍
109 661
Here’s a quick quiz based on Aliases & CASE Statements:
Quiz Question: What will the following query output? SELECT name, CASE WHEN salary >= 100000 THEN 'Executive' ELSE 'Staff' END AS role FROM employees;
109 661
🏷 Aliases & CASE Statements
1. Aliases (AS keyword)
Aliases let you rename columns or tables temporarily to make your output cleaner or more readable.
Column Alias Example:
SELECT first_name AS name, salary AS monthly_income
FROM employees;
You’ll see name and monthly_income as column headers instead of raw column names.
Table Alias Example:
SELECT e.name, d.name
FROM employees AS e
JOIN departments AS d ON e.department_id = d.id;
Here e and d are shortcuts for table names, making complex queries more readable.
2. CASE Statements
CASE is SQL’s way of doing if-else logic inside queries.
Example 1: Categorizing Data
SELECT name,
CASE
WHEN salary > 80000 THEN 'High'
WHEN salary BETWEEN 50000 AND 80000 THEN 'Medium'
ELSE 'Low'
END AS salary_category
FROM employees;
This assigns each employee a salary category.
Example 2: Conditional Aggregation
SELECT department,
COUNT(CASE WHEN gender = 'Male' THEN 1 END) AS male_count,
COUNT(CASE WHEN gender = 'Female' THEN 1 END) AS female_count
FROM employees
GROUP BY department;
Counts males and females per department.
Use aliases to simplify your SQL, and CASE when you need decision-making logic in queries.
React with ❤️ if you're excited for the next topic :🧾 Views & Indexes
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
109 661
𝗧𝗼𝗽 𝟯 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝘄𝗶𝘁𝗵 𝗘𝘅𝗰𝗲𝗹, 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 & 𝗧𝗮𝗯𝗹𝗲𝗮𝘂)😍
Breaking into data analytics can feel overwhelming—especially when you’re unsure where to begin or what tools to learn. But what if we told you that you can master the top tools Excel, Power BI, and Tableau completely for FREE?🎯
Let’s break down each course 👇
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4loaBTc
🎯 Final Thoughts: Learn Smart. Get Certified. Land the Job.
109 661
What does the following SQL query return?
SELECT name FROM employees WHERE department_id = ( SELECT id FROM departments WHERE name = 'HR' );
109 661
📦 Subqueries & Nested Queries
A subquery is a query inside another query. You can use it in SELECT, FROM, or WHERE clauses to solve complex problems step-by-step.
1. Subquery in WHERE Clause
Use this when you need to filter results based on another query.
SELECT name
FROM employees
WHERE department_id = (
SELECT id FROM departments WHERE name = 'Sales'
);
This finds all employees who work in the Sales department.
2. Subquery in SELECT Clause
This lets you fetch calculated or related values for each row.
SELECT name,
(SELECT AVG(salary) FROM employees) AS avg_salary
FROM employees;
Shows each employee’s name along with the company’s average salary.
3. Subquery in FROM Clause (Inline View)
Used when you want to treat the subquery like a temporary table.
SELECT department, total
FROM (
SELECT department, SUM(salary) AS total
FROM employees
GROUP BY department
) AS dept_summary;
This groups salaries by department in a subquery, then fetches from it.
Important:
- Always alias your subqueries (especially in the FROM clause).
- Avoid correlated subqueries if possible; they’re slower.
React with ❤️ if you want me to cover the next topic: 🏷 Aliases & Case Statements.
109 661
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
Learn from top faculty & experts - Become a skilled professional
- Learn from the best
- Learn by doing
- Learn with AI
Get FREE Course Review & Start Learning
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/41VIuSA
Enroll Now & Get a Course Completion Certification🎓
109 661
🔗 SQL JOINS (INNER, LEFT, RIGHT, FULL, SELF)
JOINS help you combine data from two or more tables based on a related column (usually a primary key and a foreign key).
1. INNER JOIN
Returns only matching rows between two tables.
SELECT customers.name, orders.order_id
FROM customers
INNER JOIN orders ON customers.id = orders.customer_id;
This returns only those customers who have placed at least one order.
2. LEFT JOIN (or LEFT OUTER JOIN)
Returns all rows from the left table, and matched rows from the right table. If no match, you'll see NULLs.
SELECT customers.name, orders.order_id
FROM customers
LEFT JOIN orders ON customers.id = orders.customer_id;
This shows all customers, including those who haven’t placed any orders.
3. RIGHT JOIN (or RIGHT OUTER JOIN)
Returns all rows from the right table, and matching rows from the left.
SELECT customers.name, orders.order_id
FROM customers
RIGHT JOIN orders ON customers.id = orders.customer_id;
You’ll see all orders — even if there’s no corresponding customer info.
4. FULL JOIN (or FULL OUTER JOIN)
Returns all rows from both tables. If there's no match, it returns NULLs.
Note: MySQL doesn't support FULL JOIN directly; use UNION of LEFT and RIGHT joins instead.
5. SELF JOIN
You join a table with itself. Great for hierarchical relationships.
SELECT e.name AS employee, m.name AS manager
FROM employees e
JOIN employees m ON e.manager_id = m.id;
This shows each employee along with their manager's name.
Pro Tip: Be careful with NULLs and always define clear join conditions to avoid cartesian products.
React with ❤️ if you're ready for the next one: 📦 Subqueries & Nested Queries.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
109 661
𝗔𝗰𝗰𝗲𝗻𝘁𝘂𝗿𝗲 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 😍
- Data Analytics and Visualization
- Coding: Development
- Project Management
- Software Engineering
These are perfect for students, freshers, or job seekers looking to stand out in a competitive job market.
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/3EmuAkw
Enroll For FREE & Get Certified🎓
109 661
What will the following SQL query return?
SELECT department, COUNT(*) AS total_employees FROM employees GROUP BY department HAVING COUNT(*) > 10;
109 661
👥 GROUP BY & HAVING Clauses
1. GROUP BY
GROUP BY is used to group rows that have the same values in specified columns and apply aggregate functions to each group.
Syntax:
SELECT column, AGG_FUNC(column2) FROM table_name GROUP BY column;
Example:
SELECT department, COUNT(*) AS total_employees
FROM employees
GROUP BY department;
This shows how many employees are in each department.
You can group by multiple columns too:
SELECT department, job_title, AVG(salary) AS avg_salary
FROM employees
GROUP BY department, job_title;
2. HAVING
HAVING is like WHERE, but it’s used to filter grouped data. You can't use WHERE with aggregate functions — that's where HAVING comes in.
Example:
SELECT department, COUNT(*) AS total_employees
FROM employees
GROUP BY department
HAVING COUNT(*) > 5;
This gives you only those departments that have more than 5 employees.
Bonus: Combine GROUP BY + ORDER BY + HAVING:
SELECT category, SUM(sales) AS total_sales
FROM products
GROUP BY category
HAVING SUM(sales) > 10000
ORDER BY total_sales DESC;
This gives you the top-selling categories with sales over 10,000.
React with ❤️ if you’re ready for the next banger: 🔗 SQL JOINS (INNER, LEFT, RIGHT, FULL, SELF).
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
