Endi mavjud! Telegram Tadqiqoti 2025 โ yilning asosiy insaytlari 
Data Analytics
Kanalga Telegramโda oโtish
Perfect channel to learn Data Analytics Learn SQL, Python, Alteryx, Tableau, Power BI and many more For Promotions: @coderfun @love_data
Ko'proq ko'rsatish๐ Telegram kanali Data Analytics analitikasi
Data Analytics (@sqlspecialist) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 109 661 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 1 126-o'rinni va Hindiston mintaqasida 2 339-o'rinni egallagan.
๐ Auditoriya koโrsatkichlari va dinamika
ะฝะตะฒัะดะพะผะพ sanasidan buyon loyiha tez oโsib, 109 661 obunachiga ega boโldi.
23 Iyun, 2026 dagi oxirgi maโlumotlarga koโra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 529 ga, soโnggi 24 soatda esa 20 ga oโzgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya oโrtacha 2.83% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.72% ini tashkil etuvchi reaksiyalarni toโplaydi.
- Post qamrovi: Har bir post oโrtacha 3 097 marta koโriladi; birinchi sutkada odatda 784 ta koโrish yigโiladi.
- Reaksiyalar va oโzaro taโsir: Auditoriya faol: har bir postga oโrtacha 8 ta reaksiya keladi.
- Tematik yoโnalishlar: Kontent row, sql, analytic, analyst, visualization kabi asosiy mavzularga jamlangan.
๐ Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida taโriflaydi:
โPerfect channel to learn Data Analytics
Learn SQL, Python, Alteryx, Tableau, Power BI and many more
For Promotions: @coderfun @love_dataโ
Yuqori yangilanish chastotasi (oxirgi maโlumot 24 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli boโlib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim taโsir nuqtasiga aylantirishini koโrsatadi.
109 661
Obunachilar
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Postlar arxiv
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๐ Window Functions (ROW_NUMBER, RANK, PARTITION BY)
Window functions perform calculations across rows related to the current row โ but unlike GROUP BY, they donโt 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() doesnโt.
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
Itโs 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 โ without collapsing the rows.
Other Useful Window Functions:
NTILE(n) โ Divides rows into n buckets
LAG() / LEAD() โ Look at previous/next rowโs value
SUM() / AVG() over a window โ for running totals
React with โค๏ธ if you're pumped for the next one: โ๏ธ Data Manipulation (INSERT, UPDATE, DELETE).
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
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๐๐ & ๐ ๐ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐
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What is the main advantage of using a Common Table Expression (CTE) in SQL?
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Here comes one of the cleanest and most powerful features in SQL:
๐ง 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: Letโs 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 โค๏ธ if you're excited for the next one: ๐ Window Functions (ROW_NUMBER, RANK, PARTITION BY).
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
109 661
๐ฐ ๐ ๐๐๐-๐๐ป๐ผ๐ ๐๐ฟ๐ฒ๐ฒ ๐ฆ๐ค๐ ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ณ๐ผ๐ฟ ๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐๐๐
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109 661
Now, letโs dive into a couple of powerful but often overlooked SQL features:
๐งพ Views & Indexes (Basics)
1. Views
A View is a virtual table based on a SQL query. It doesnโt store data itself โ 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 bookโs table of contents โ 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.
Donโt overuse them โ only on frequently searched or joined columns.
React with โค๏ธ if youโre ready for the next interesting concept: ๐ง Common Table Expressions (CTEs).
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๐๐๐ฒ ๐๐๐ญ๐๐ซ ๐๐ฅ๐๐๐๐ฆ๐๐ง๐ญ ๐๐ซ๐๐ข๐ง๐ข๐ง๐ ๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐
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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 ๐๐
Latest Jobs & Internships: https://t.me/getjobss
Jobs Preparation Resources:
https://t.me/jobinterviewsprep
Data Science Jobs:
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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:
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Software Developer Jobs: https://t.me/internshiptojobs
If you found this helpful, donโt 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 LEARNING๐๐
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Hereโs 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;
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๐ท 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;
Youโll 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 SQLโs 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 โค๏ธ if you're excited for the next topic :๐งพ Views & Indexes
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
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๐ง๐ผ๐ฝ ๐ฏ ๐๐ฟ๐ฒ๐ฒ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป๐ ๐ณ๐ผ๐ฟ ๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ (๐๐ถ๐๐ต ๐๐
๐ฐ๐ฒ๐น, ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ & ๐ง๐ฎ๐ฏ๐น๐ฒ๐ฎ๐)๐
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What does the following SQL query return?
SELECT name FROM employees WHERE department_id = ( SELECT id FROM departments WHERE name = 'HR' );
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๐ฆ 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 employeeโs name along with the companyโs 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; theyโre slower.
React with โค๏ธ if you want me to cover the next topic: ๐ท Aliases & Case Statements.
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๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ฃ๐ฟ๐ฒ๐บ๐ถ๐๐บ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
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109 661
๐ 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 havenโt 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;
Youโll see all orders โ even if thereโs 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 โค๏ธ if you're ready for the next one: ๐ฆ Subqueries & Nested Queries.
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
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What will the following SQL query return?
SELECT department, COUNT(*) AS total_employees FROM employees GROUP BY department HAVING COUNT(*) > 10;
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๐ฅ 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 itโs used to filter grouped data. You can't use WHERE with aggregate functions โ 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 โค๏ธ if youโre ready for the next banger: ๐ SQL JOINS (INNER, LEFT, RIGHT, FULL, SELF).
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
