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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 109 659 suscriptores, ocupando la posición 1 122 en la categoría Tecnologías y Aplicaciones y el puesto 2 340 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 109 659 suscriptores.
Según los últimos datos del 24 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 584, y en las últimas 24 horas de 71, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.76%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.68% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 024 visualizaciones. En el primer día suele acumular 743 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 25 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.
109 659
Suscriptores
+7124 horas
+267 días
+58430 días
Archivo de publicaciones
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👥 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 :)
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7 High-Impact Portfolio Project Ideas for Aspiring Data Analysts
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✅ HR Analytics Dashboard – Visualize attrition, department strength, and performance reviews
✅ Survey Data Analysis – Clean, visualize, and derive insights from user feedback or product surveys
✅ E-commerce Product Analysis – Analyze top-selling products, revenue by category, and return rates
✅ Airbnb Price Predictor – Use machine learning to predict listing prices based on location, amenities, and ratings
These projects showcase real-world skills and storytelling with data.
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109 681
What does the following SQL query return?
SELECT COUNT(email) FROM customers;
109 681
📊 Aggregate Functions (COUNT, SUM, AVG, MIN, MAX)
Aggregate functions are used to perform calculations on multiple rows of a table and return a single value. They're mostly used with GROUP BY, but also work standalone.
1. COUNT()
Returns the number of rows.
Example:
SELECT COUNT(*) FROM employees;
Counts all employees in the table.
You can also count only non-null values in a column:
SELECT COUNT(email) FROM customers;
2. SUM()
Adds up all the values in a numeric column.
Example:
SELECT SUM(salary) FROM employees;
Gives you the total salary payout.
3. AVG()
Calculates the average value of a numeric column.
Example:
SELECT AVG(price) FROM products;
Finds the average product price.
4. MIN()
Returns the lowest value.
Example:
SELECT MIN(salary) FROM employees;
Finds the smallest salary.
5. MAX()
Returns the highest value.
Example:
SELECT MAX(salary) FROM employees;
Finds the highest salary in the table.
Bonus Example:
SELECT
COUNT(*) AS total_orders,
SUM(amount) AS total_revenue,
AVG(amount) AS avg_order_value
FROM orders;
This gives you a quick business summary: number of orders, total revenue, and average order value.
React with ❤️ if you're excited for the next topic: 👥 GROUP BY & HAVING Clauses.
109 681
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109 681
What will this query return?
SELECT * FROM customers WHERE city = 'Delhi' AND name LIKE 'A%';
109 681
Let’s go to the next topic in our SQL Roadmap!
🔢 SQL Operators (IN, BETWEEN, LIKE, AND, OR)
These operators help you build flexible and powerful conditions inside your WHERE clause.
1. IN Operator
Used to match multiple values in a column.
Example:
SELECT * FROM customers WHERE city IN ('Delhi', 'Mumbai', 'Bangalore');
This fetches customers who live in any of the three cities.
2. BETWEEN Operator
Used to filter values within a range (inclusive).
Example:
SELECT * FROM orders WHERE order_date BETWEEN '2024-01-01' AND '2024-12-31';
Returns all orders placed in 2024.
3. LIKE Operator
Used for pattern matching. Especially useful with wildcards (%).
Example:
SELECT * FROM customers WHERE name LIKE 'A%';
Finds customers whose names start with "A".
Another example:
SELECT * FROM emails WHERE address LIKE '%@gmail.com';
Finds all Gmail users.
4. AND Operator
Combines multiple conditions — all must be true.
Example:
SELECT * FROM employees WHERE department = 'HR' AND salary > 50000;
Finds HR employees earning more than 50,000.
5. OR Operator
Returns results if any one condition is true.
Example:
SELECT * FROM products WHERE category = 'Electronics' OR category = 'Books';
Fetches products that belong to either of the two categories.
Pro Tip:
Combine these operators for complex logic!
SELECT * FROM orders
WHERE status = 'Delivered'
AND delivery_date BETWEEN '2025-01-01' AND '2025-03-31';
React with ❤️ if you're ready for the next one: 📊 Aggregate Functions (COUNT, SUM, AVG, MIN, MAX).
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
109 681
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109 681
What will this query return?
SELECT name FROM employees ORDER BY salary DESC LIMIT 1;
109 681
Let’s move on to the next topic in our SQL Roadmap!
✏️ Filtering & Sorting Data (ORDER BY, LIMIT)
1. ORDER BY Clause:
ORDER BY is used to sort the result set based on one or more columns — either in ascending or descending order.
Syntax:
SELECT column1, column2 FROM table_name ORDER BY column1 ASC|DESC;
Example:
SELECT name, salary FROM employees ORDER BY salary DESC;
This lists employees with the highest salaries at the top.
By default, it sorts in ascending (ASC) order if no direction is specified.
2. LIMIT Clause:
LIMIT is used to restrict the number of rows returned by a query. Super useful when you want just a sample or the top results.
Syntax:
SELECT * FROM table_name LIMIT number;
Example:
SELECT * FROM products LIMIT 5;
This fetches only the first 5 products.
You can also combine ORDER BY and LIMIT:
SELECT * FROM products ORDER BY price DESC LIMIT 3;
This gets the top 3 most expensive products.
Quick Recap:
Use ORDER BY to sort your data
Use LIMIT to control how many results you get
React with ❤️ if you're excited for the next one: 🔢 SQL Operators (IN, BETWEEN, LIKE, AND, OR).
109 681
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109 681
What does the following SQL query do?
SELECT * FROM products WHERE price > 1000;
109 681
Moving on to next topic!
🔍 Basic SQL Queries (SELECT, WHERE)
1. SELECT Statement:
The SELECT command is used to retrieve data from a table. It’s the most fundamental query in SQL.
Syntax:
SELECT column1, column2 FROM table_name;
Example:
SELECT name, email FROM customers;
This fetches the name and email of all customers from the customers table.
You can also use * to select all columns:
SELECT * FROM customers;
2. WHERE Clause:
The WHERE clause is used to filter records that meet a specific condition.
Syntax:
SELECT column1, column2 FROM table_name WHERE condition;
Example:
SELECT name FROM customers WHERE city = 'Delhi';
This returns names of all customers who are from Delhi.
Another example using numbers:
SELECT * FROM products WHERE price > 1000;
This gets all products priced above 1000.
Key Point:
SELECT fetches data
WHERE filters it based on conditions
React with ❤️ if you're ready for the next one: ✏️ Filtering & Sorting Data (ORDER BY, LIMIT).
I keep quizzes after the explanation to understand you're really understanding each concept
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
109 681
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109 681
In a relational database, what is the main purpose of a foreign key?
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Awesome! Let’s dive into the next one:
🧱 Database Concepts (Tables, Rows, Columns, Keys)
1. Table:
A table is the basic structure where data is stored in a relational database. Think of it like a spreadsheet. Each table represents one type of entity — for example, a Customers table or a Products table.
2. Rows (Records):
Each row in a table represents a single record or entry.
Example: A row in the Customers table could represent one customer’s details like their name, email, and phone number.
3. Columns (Fields):
Columns represent the attributes or properties of the data.
Example: In a Products table, columns might be product_id, product_name, price, and category.
4. Keys:
Keys are special columns that help in uniquely identifying rows and establishing relationships between tables.
Primary Key (PK): Uniquely identifies each record in a table. It must be unique and not null.
Example: customer_id in a Customers table.
Foreign Key (FK): A field in one table that refers to the primary key in another table. It’s used to link tables together.
Example: customer_id in an Orders table links to the Customers table.
Real-World Analogy:
Imagine a school:
The "Student" table holds data about each student.
Each row is one student.
Each column is an attribute like name, roll number, or class.
The primary key might be roll_number.
A foreign key might be class_id that links to a Classes table.
React with ❤️ to keep the momentum going!
Next up: 🔍 Basic SQL Queries (SELECT, WHERE).
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
109 681
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109 681
Which type of database is best suited for complex JOIN operations?
