Data Analytics
Kanalga Telegram’da o‘tish
Perfect channel to learn Data Analytics Learn SQL, Python, Alteryx, Tableau, Power BI and many more For Promotions: @coderfun @love_data
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Data Analytics analitikasi
Data Analytics (@sqlspecialist) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 110 174 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 1 094-o'rinni va Hindiston mintaqasida 2 294-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 110 174 obunachiga ega bo‘ldi.
16 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 522 ga, so‘nggi 24 soatda esa 4 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 3.29% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.63% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 3 630 marta ko‘riladi; birinchi sutkada odatda 1 794 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 8 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent row, sql, analytic, analyst, visualization kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Perfect channel to learn Data Analytics
Learn SQL, Python, Alteryx, Tableau, Power BI and many more
For Promotions: @coderfun @love_data”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 17 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
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Kanal postlari
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| 2 | 🚀 Power BI Interview Challenge #2 🔥
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this Power BI problem.
You have a Sales table with the columns: Order Date & Sales
Create a DAX measure to calculate Month-to-Date (MTD) Sales.
𝗠𝗲: Challenge accepted! 💪
MTD Sales =
TOTALMTD(
SUM(Sales[Sales]),
Sales[Order Date]
)
💡 Explanation:
• TOTALMTD() calculates cumulative sales from the beginning of the current month up to the selected date.
• SUM(Sales) returns the total sales amount.
• Sales[Order Date] is the date column used for the MTD calculation.
• The measure automatically resets at the beginning of each new month.
🎯 Expected Output Example
Date | Sales | MTD Sales
Jul 1 | 2,000 | 2,000
Jul 2 | 3,500 | 5,500
Jul 3 | 1,500 | 7,000
Jul 4 | 4,000 | 11,000
🚀 Bonus (Using a Calendar Table)
MTD Sales =
TOTALMTD(
[Total Sales],
'Calendar'[Date]
)
Using a dedicated Calendar table improves model performance and ensures accurate time intelligence calculations.
🚀 Tip for Power BI Job Seekers:
Always create a proper Date Table and mark it as a Date Table in Power BI before using Time Intelligence functions. Many interview questions are designed to test this best practice.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
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| 3 |  🚀 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 💻🔥
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| 4 | 🚀 Power BI Interview Challenge #1 🔥
𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this Power BI problem.
You have a Sales table with the following columns:
Order Date
Sales
Create a DAX measure to calculate Year-to-Date (YTD) Sales.
𝗠𝗲: Challenge accepted! 💪
YTD Sales =
TOTALYTD(
SUM(Sales[Sales]),
Sales[Order Date]
)
💡 Explanation:
TOTALYTD() calculates the cumulative sales from the beginning of the year up to the current date.
• SUM(Sales) returns the total sales amount
• Sales[Order Date] is the date column used for the YTD calculation
• The measure automatically resets at the start of each new year[Sales]
🎯 Expected Output Example
Month | Sales | YTD Sales
--- | --- | ---
Jan | 10,000 | 10,000
Feb | 15,000 | 25,000
Mar | 12,000 | 37,000
Apr | 18,000 | 55,000
🚀 Bonus (Using a Calendar Table)
YTD Sales =
TOTALYTD(
[Total Sales],
'Calendar'[Date]
)
Using a dedicated Calendar/Date table is considered a Power BI best practice and is recommended for all time intelligence calculations.
🚀 Tip for Power BI Job Seekers:
Time Intelligence is one of the most frequently tested topics in Power BI interviews. Make sure you can confidently write measures for:
• YTD (Year-to-Date)
• MTD (Month-to-Date)
• QTD (Quarter-to-Date)
• Previous Year Sales
• YoY Growth %
• Rolling 12 Months
These are commonly used in business dashboards and technical interviews.
Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
❤️ React with ❤️ for more Power BI interview challenges! | 3 494 |
| 5 |  🚀 𝗧𝗼𝗽 𝟱 𝗦𝗸𝗶𝗹𝗹𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗜𝗻 𝟮𝟬𝟮𝟲 – 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘! 🎓
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| 6 | 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this SQL query.
Find the employee(s) who have worked on the highest number of distinct projects.
Assume the table structure: employee_projects(employee_id, project_id)
𝗠𝗲: Challenge accepted! 💪
SELECT
employee_id,
total_projects
FROM (
SELECT
employee_id,
COUNT(DISTINCT project_id) AS total_projects,
DENSE_RANK() OVER (
ORDER BY COUNT(DISTINCT project_id) DESC
) AS rnk
FROM employee_projects
GROUP BY employee_id
) ranked
WHERE rnk = 1;
💡 Explanation:
This query counts the number of unique projects each employee has worked on and identifies those with the highest count.
• COUNT(DISTINCT project_id) counts unique projects for each employee
• GROUP BY employee_id creates one record per employee
• DENSE_RANK() ranks employees based on the number of projects
• The outer query returns all employees tied for the highest number of projects
This question tests your understanding of:
✅ COUNT(DISTINCT)
✅ GROUP BY
✅ Window Functions DENSE_RANK
✅ Ranking Aggregated Results
🎯 Expected Output Example
Employee ID | Total Projects
101 | 12
205 | 12
Both employees have worked on the highest number of distinct projects.
🚀 Alternative Without Window Functions
SELECT
employee_id,
COUNT(DISTINCT project_id) AS total_projects
FROM employee_projects
GROUP BY employee_id
HAVING COUNT(DISTINCT project_id) = (
SELECT MAX(project_count)
FROM (
SELECT
COUNT(DISTINCT project_id) AS project_count
FROM employee_projects
GROUP BY employee_id
) t
);
This solution uses nested subqueries and MAX() instead of window functions.
🚀 Tip for SQL Job Seekers:
Many interview questions involve ranking aggregated results, such as:
Highest number of projects, Most orders, Maximum sales, Highest attendance, Most logins
Practice combining GROUP BY with window functions like DENSE_RANK() to solve these efficiently.
❤️ React with ❤️ for more interview challenges! | 3 236 |
| 7 |  🚀 𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 - 𝗟𝗮𝘂𝗻𝗰𝗵 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗖𝗮𝗿𝗲𝗲𝗿
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| 9 | 🚀 Essential Tools Every Data Analyst Should Know
If you're starting your journey as a Data Analyst, focus on these essential tools first. These are the tools most commonly required in job descriptions and used in day-to-day work.
📊 1. Microsoft Excel
Used For:
Data Cleaning
Formulas & Functions
Pivot Tables
Dashboards
🗄️ 2. SQL
Used For:
Querying Databases
Data Extraction
Data Analysis
Reporting
📈 3. Power BI
Used For:
Interactive Dashboards
Data Visualization
Business Intelligence
KPI Reporting
📊 4. Tableau
Used For:
Data Visualization
Dashboard Creation
Business Reporting
🐍 5. Python
Used For:
Data Cleaning
Automation
Data Analysis
Data Visualization
🔄 6. Power Query
Used For:
Data Transformation
Data Cleaning
ETL Processes
🚀 Double Tap ❤️ For More
-----
1.21 ₽ · /balance_help | 2 813 |
| 10 |  𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗵𝗲𝘀𝗲 𝗛𝗶𝗴𝗵-𝗗𝗲𝗺𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗛𝗶𝗴𝗵-𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯𝘀 🔥
This guide highlights 3 powerful skills that are opening doors to high-paying roles across tech and business .🎓
Perfect For
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| 11 | 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this SQL query.
Q: Find the employee(s) who received the highest salary increment compared to their previous salary.
Assume the table structure:
salary_history(employee_id, salary, effective_date)
𝗠𝗲: Challenge accepted! 💪
WITH salary_changes AS (
SELECT
employee_id,
salary,
effective_date,
salary - LAG(salary) OVER (
PARTITION BY employee_id
ORDER BY effective_date
) AS salary_increment
FROM salary_history
)
SELECT
employee_id,
salary_increment
FROM (
SELECT
employee_id,
salary_increment,
DENSE_RANK() OVER (
ORDER BY salary_increment DESC
) AS rnk
FROM salary_changes
WHERE salary_increment IS NOT NULL
) ranked
WHERE rnk = 1;
💡 Explanation:
This query calculates each employee's salary increment and then finds the highest increment across all employees.
• LAG(salary) retrieves the employee's previous salary
• The difference between the current and previous salary gives the increment
• DENSE_RANK() ranks increments from highest to lowest
• The outer query returns all employees tied for the highest salary increment
This question tests your understanding of:
✅ LAG() Window Function
✅ Common Table Expressions (CTEs)
✅ DENSE_RANK()
✅ Time-Series Data Analysis
🎯 Expected Output Example
Employee ID | Salary Increment
101 | 20,000
205 | 20,000
Both employees received the largest salary increase.
🚀 Why Interviewers Ask This?
This is a classic window function interview question. It evaluates your ability to compare a row with its previous row—a common requirement in payroll, finance, and audit systems.
🚀 Tip for SQL Job Seekers:
Master these analytical window functions:
LAG() / LEAD() / FIRST_VALUE() / LAST_VALUE() / NTILE()
These functions are frequently tested in product-based companies and data-focused interviews because they simplify complex row-by-row comparisons.
❤️ React with ❤️ for more interview challenges! | 3 046 |
| 12 |  𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀🎓
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| 13 | 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this SQL query.
Q: Find the customer(s) who placed orders in every month of the year 2025.
Assume the table structure:
orders(order_id, customer_id, order_date)
𝗠𝗲: Challenge accepted! 💪
SELECT
customer_id
FROM orders
WHERE YEAR(order_date) = 2025
GROUP BY customer_id
HAVING COUNT(DISTINCT MONTH(order_date)) = 12;
💡 Explanation:
This query identifies customers who placed at least one order in every month of 2025.
• WHERE YEAR(order_date) = 2025 filters orders from the year 2025
• GROUP BY customer_id groups all orders by customer
• COUNT(DISTINCT MONTH(order_date)) counts the unique months in which each customer placed an order
• HAVING ... = 12 ensures the customer has orders in all 12 months
This question tests your understanding of:
✅ Date Functions (YEAR, MONTH)
✅ GROUP BY
✅ HAVING
✅ COUNT(DISTINCT)
🎯 Expected Output Example
| Customer ID |
|-------------|
| 101 |
| 205 |
These customers placed at least one order in every month of 2025.
🚀 Alternative (Database-Agnostic SQL)
SELECT
customer_id
FROM orders
WHERE EXTRACT(YEAR FROM order_date) = 2025
GROUP BY customer_id
HAVING COUNT(DISTINCT EXTRACT(MONTH FROM order_date)) = 12;
This version works with databases like PostgreSQL and Oracle that support the EXTRACT() function.
🚀 Tip for SQL Job Seekers:
Whenever you see interview questions containing phrases like:
"Every month" / "Every quarter" / "Every year" / "Every category"
Think of COUNT(DISTINCT ...) combined with GROUP BY and HAVING. This is a very common SQL interview pattern.
❤️ React with ❤️ for more interview challenges! | 2 897 |
| 14 |  𝗔𝗜 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 😍
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| 15 | Scenario based Interview Questions & Answers for Data Analyst
1. Scenario: You are working on a SQL database that stores customer information. The database has a table called "Orders" that contains order details. Your task is to write a SQL query to retrieve the total number of orders placed by each customer.
Question:
- Write a SQL query to find the total number of orders placed by each customer.
Expected Answer:
SELECT CustomerID, COUNT(*) AS TotalOrders
FROM Orders
GROUP BY CustomerID;
2. Scenario: You are working on a SQL database that stores employee information. The database has a table called "Employees" that contains employee details. Your task is to write a SQL query to retrieve the names of all employees who have been with the company for more than 5 years.
Question:
- Write a SQL query to find the names of employees who have been with the company for more than 5 years.
Expected Answer:
SELECT Name
FROM Employees
WHERE DATEDIFF(year, HireDate, GETDATE()) > 5;
Power BI Scenario-Based Questions
1. Scenario: You have been given a dataset in Power BI that contains sales data for a company. Your task is to create a report that shows the total sales by product category and region.
Expected Answer:
- Load the dataset into Power BI.
- Create relationships if necessary.
- Use the "Fields" pane to select the necessary fields (Product Category, Region, Sales).
- Drag these fields into the "Values" area of a new visualization (e.g., a table or bar chart).
- Use the "Filters" pane to filter data as needed.
- Format the visualization to enhance clarity and readability.
2. Scenario: You have been asked to create a Power BI dashboard that displays real-time stock prices for a set of companies. The stock prices are available through an API.
Expected Answer:
- Use Power BI Desktop to connect to the API.
- Go to "Get Data" > "Web" and enter the API URL.
- Configure the data refresh settings to ensure real-time updates (e.g., setting up a scheduled refresh or using DirectQuery if supported).
- Create visualizations using the imported data.
- Publish the report to the Power BI service and set up a data gateway if needed for continuous refresh.
3. Scenario: You have been given a Power BI report that contains multiple visualizations. The report is taking a long time to load and is impacting the performance of the application.
Expected Answer:
- Analyze the current performance using Performance Analyzer.
- Optimize data model by reducing the number of columns and rows, and removing unnecessary calculations.
- Use aggregated tables to pre-compute results.
- Simplify DAX calculations.
- Optimize visualizations by reducing the number of visuals per page and avoiding complex custom visuals.
- Ensure proper indexing on the data source.
Free SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Like if you need more similar content
Hope it helps :) | 2 766 |
| 16 |  GigaChat 3.5 Ultra Publicly Released — The New Generation of the Flagship Model
The GigaChat team has released GigaChat 3.5 Ultra as open source—a new 432B model under the MIT license. This is the first open-source hybrid of GatedDeltaNet and MLA scaled to hundreds of billions of parameters, featuring a proprietary training recipe we refined through more than 1,500 experiments. The model has grown in terms of code, mathematics, agent scenarios, and application domains—yet it’s 40% smaller than GigaChat 3.1 Ultra.
What’s inside:
🔘A proprietary hybrid MLA + Gated DeltaNet architecture with a dedicated stabilization framework, without which this hybrid setup would not train reliably at this scale;
🔘 Gated Attention: the model can locally down-weight overly strong signals from the attention layer;
🔘GatedNorm: normalization with an explicit gate that controls signal magnitude across features;
🔘Approximately 4x lower KV cache per token: with the same memory budget, the model can support 2.14x longer context and deliver a 20% throughput increase under load;
🔘Two MTP heads, enabling up to 2.2x faster generation;
🔘FP8 across all training stages with no quality degradation compared with bf16, enabled by custom Triton and CUDA kernels;
🔘A new online RL stage after SFT and DPO.
Results:
🔘 GigaChat-3.5-Ultra-Base outperforms DeepSeek V3.2 Exp Base and DeepSeek V4 Flash Base on average across a set of general, math, and code benchmarks:
🔘 GigaChat-3.5-Ultra-Instruct is comparable to DeepSeek V3.2 in terms of average score, despite having half the size;
🔘 According to the MiniMax-M2.7 LLM judge, the average win rate against GigaChat 3.1 Ultra is 75.9%, and against GPT-5 is 68.7%.
The entire stack — data (our own LLM-filtered Common Crawl, 600+ programming languages in the code), architecture, training methodology, and infrastructure — was built end-to-end by GigaChat team.
➡️ HuggingFace | 3 009 |
| 17 | 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿:
You have 2 minutes to solve this SQL query.
Find employees whose salary is higher than the average salary of all other departments (excluding their own department).
Assume the table structure:
employees(employee_id, employee_name, department, salary)
𝗠𝗲: Challenge accepted! 💪
SELECT
employee_id,
employee_name,
department,
salary
FROM employees e1
WHERE salary > (
SELECT AVG(salary)
FROM employees e2
WHERE e2.department <> e1.department
);
💡 Explanation:
This query compares each employee's salary against the average salary of all employees outside their own department.
• The outer query processes each employee.
• The correlated subquery calculates the average salary of employees in all other departments.
• Employees whose salary exceeds that average are returned.
This question tests your understanding of:
✅ Correlated Subqueries
✅ Aggregate Functions (AVG)
✅ Conditional Filtering
✅ Cross-group Comparisons
🎯 Expected Output Example
Employee: John | Department: IT | Salary: 95,000
Employee: Sarah | Department: HR | Salary: 82,000
🚀 Alternative Using Common Table Expressions (CTEs)
WITH dept_avg AS (
SELECT
department,
AVG(salary) AS avg_salary
FROM employees
GROUP BY department
)
SELECT
e.employee_id,
e.employee_name,
e.department,
e.salary
FROM employees e
WHERE e.salary > (
SELECT AVG(avg_salary)
FROM dept_avg d
WHERE d.department <> e.department
);
This version first computes department-level averages and then compares each employee's salary with the average of the other departments' averages.
🚀 Tip for SQL Job Seekers:
Interviewers often ask questions that compare data within a group versus outside a group. These problems test your understanding of correlated subqueries and aggregate calculations across multiple levels.
❤️ React with ❤️ for more SQL interview challenges! | 2 787 |
| 18 |  𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽𝘀 | 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲🎓
These FREE virtual certificate internships can help you build practical skills, industry exposure, and resume value from top companies and global platforms — all from home.
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- PwC Power BI Virtual Internship
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| 19 | If you are interested to learn SQL for data analytics purpose and clear the interviews, just cover the following topics
1)Install MYSQL workbench
2) Select
3) From
4) where
5) group by
6) having
7) limit
8) Joins (Left, right , inner, self, cross)
9) Aggregate function ( Sum, Max, Min , Avg)
9) windows function ( row num, rank, dense rank, lead, lag, Sum () over)
10)Case
11) Like
12) Sub queries
13) CTE
14) Replace CTE with temp tables
15) Methods to optimize Sql queries
16) Solve problems and case studies at Ankit Bansal youtube channel
Trick: Just copy each term and paste on youtube and watch any 10 to 15 minute on each topic and practise it while learning , By doing this , you get the basics understanding
17) Now time to go on youtube and search data analysis end to end project using sql
18) Watch them and practise them end to end.
17) learn integration with power bi
In this way , you will not only memorize the concepts but also learn how to implement them in your current working and projects and will be able to defend it in your interviews as well.
Like for more
Here you can find essential SQL Interview Resources👇
https://t.me/DataSimplifier
Hope it helps :) | 3 010 |
| 20 |  📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 🚀
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