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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
Показати більше📈 Аналітичний огляд Telegram-каналу Data Analytics
Канал Data Analytics (@sqlspecialist) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 109 661 підписників, посідаючи 1 126 місце в категорії Технології та додатки та 2 339 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 109 661 підписників.
За останніми даними від 23 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 529, а за останні 24 години на 20, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 2.83%. Протягом перших 24 годин після публікації контент зазвичай збирає 0.72% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 3 097 переглядів. Протягом першої доби публікація в середньому набирає 784 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 8.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як row, sql, analytic, analyst, visualization.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Perfect channel to learn Data Analytics
Learn SQL, Python, Alteryx, Tableau, Power BI and many more
For Promotions: @coderfun @love_data”
Завдяки високій частоті оновлень (останні дані отримано 24 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
109 661
Підписники
+2024 години
-647 днів
+52930 день
Архів дописів
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7 High-Impact Portfolio Project Ideas for Aspiring Data Analysts
✅ Sales Dashboard – Use Power BI or Tableau to visualize KPIs like revenue, profit, and region-wise performance
✅ Customer Churn Analysis – Predict which customers are likely to leave using Python (Logistic Regression, EDA)
✅ Netflix Dataset Exploration – Analyze trends in content types, genres, and release years with Pandas & Matplotlib
✅ 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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109 659
What does the following SQL query return?
SELECT COUNT(email) FROM customers;
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📊 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.
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109 659
What will this query return?
SELECT * FROM customers WHERE city = 'Delhi' AND name LIKE 'A%';
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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 :)
109 659
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109 659
What will this query return?
SELECT name FROM employees ORDER BY salary DESC LIMIT 1;
109 659
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).
109 659
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109 659
What does the following SQL query do?
SELECT * FROM products WHERE price > 1000;
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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 :)
109 659
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109 659
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 :)
109 659
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109 659
Which type of database is best suited for complex JOIN operations?
109 659
Let's go to our next topic now
📄 SQL vs NoSQL
1. What is SQL (Relational) Database?
SQL databases are structured and use tables (rows and columns) to store data. They follow a strict schema, meaning the data format is predefined.
Examples: MySQL, PostgreSQL, SQLite, SQL Server
Used For: Applications where data integrity and relationships are important, like banking systems or e-commerce platforms.
2. What is NoSQL (Non-Relational) Database?
NoSQL databases are more flexible and can store unstructured or semi-structured data like JSON or key-value pairs. They don’t require a fixed schema.
Examples: MongoDB, Redis, Firebase, Cassandra
Used For: Real-time applications, large-scale data, or when rapid development and scalability are more important than structure.
Key Differences:
Data Format: SQL uses tables; NoSQL uses documents or key-value pairs.
Schema: SQL is strict; NoSQL is flexible.
Scalability: SQL scales vertically (strong server); NoSQL scales horizontally (more servers).
Use Case: SQL is great for complex queries and transactions; NoSQL excels in high-volume, real-time scenarios.
React with ❤️ to keep going! Up next: 🧱 Database Concepts (Tables, Rows, Columns, Keys).
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
