Data Analytics
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 110 744 suscriptores, ocupando la posición 1 082 en la categoría Tecnologías y Aplicaciones y el puesto 2 206 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 110 744 suscriptores.
Según los últimos datos del 25 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -102, y en las últimas 24 horas de -15, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.99%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.27% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 314 visualizaciones. En el primer día suele acumular 1 405 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 6.
- 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 26 octubre, 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.
=Product_Sales/Total_Sales
"If the total sales value is stored in a fixed cell, I would use an absolute reference."
=B2/$B$10
"I would then format the result as a percentage."
3️⃣ How would you identify customers whose sales have decreased compared with the previous month?
Sample Answer:
"I would first create a monthly sales summary by customer. Then I would compare the current month's sales with the previous month's sales.
A negative difference would indicate a decrease."
=Current_Month_Sales-Previous_Month_Sales
"I could then use conditional formatting to highlight customers with negative changes."
4️⃣ How would you calculate a rolling 3-month average?
Sample Answer:
"A rolling average calculates the average over a moving window of time.
For example, a 3-month rolling average can be calculated using the current month and the previous two months."
=AVERAGE(B2:B4)
"As I move down the dataset, the range moves forward by one month."
5️⃣ How would you calculate employee tenure in Excel?
Sample Answer:
"If I have an employee's joining date, I can calculate their tenure using DATEDIF or by calculating the difference between the joining date and today's date."
For example:
=DATEDIF(A2,TODAY(),"Y")
"This returns the completed number of years."
6️⃣ How would you identify overdue payments?
Sample Answer:
"I would compare the due date with the current date and check whether the payment status is still pending."
For example:
=IF(AND(B2<TODAY(),C2="Pending"),"Overdue","On Time")
"I could then use conditional formatting to highlight overdue transactions."
7️⃣ How would you identify the top 10% of customers by sales?
Sample Answer:
"I would first calculate total sales by customer. Then I could use a percentile-based approach or rank customers based on sales.
For example, I could calculate the 90th percentile and identify customers whose sales are greater than or equal to that threshold."
=PERCENTILE.INC(B2:B100,0.9)=(Current_Month-Previous_Month)/Previous_Month
“I would then visualize the trend using an appropriate chart and investigate significant increases or decreases to understand the underlying business reasons.”
📌 Important Excel areas for Data Analyst interviews:
• Lookup functions
• Conditional functions
• Text functions
• Date functions
• PivotTables
• Power Query
• Data cleaning
• Dynamic arrays
• Charts and dashboards
Double Tap ❤️ For More
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1.36 ₽ · /balance_help=XLOOKUP(A2,Customer_ID,Customer_Name,"Not Found")
3️⃣ What is IFERROR and when would you use it?
Sample Answer:
“IFERROR allows me to return an alternative result when a formula produces an error.
For example, if I'm calculating profit margin and revenue could be zero, I can prevent a #DIV/0! error.”
=IFERROR(Profit/Revenue,0)
“I use it carefully because hiding errors without understanding their cause can mask data-quality problems.”
4️⃣ What is the difference between CONCAT, CONCATENATE, and TEXTJOIN?
Sample Answer:
“These functions are used to combine text.
CONCAT combines text from multiple cells or ranges.
CONCATENATE is an older function that combines text values.
TEXTJOIN is more flexible because it allows me to specify a delimiter and ignore empty cells.”
Example:
=TEXTJOIN(", ",TRUE,A2:C2)
5️⃣ How would you extract the first, middle, or last part of a text value?
Sample Answer:
“I can use functions such as LEFT, RIGHT, and MID.
For example:
=LEFT(A2,5)
=RIGHT(A2,4)
=MID(A2,3,5)
The appropriate function depends on the structure of the text and the business requirement.”
6️⃣ How do you find duplicate values using a formula?
Sample Answer:
“I can use COUNTIF to determine how many times a value appears.
For example:”
=IF(COUNTIF(A:A,A2)>1,"Duplicate","Unique")
“This marks a value as Duplicate if it appears more than once in column A.”
7️⃣ What is Power Query in Excel?
Sample Answer:
“Power Query is a data preparation and transformation tool available in Excel.
It can connect to different data sources and perform repeatable transformations such as removing duplicates, changing data types, filtering rows, splitting columns, merging datasets, and appending data.
One major advantage is that once the transformation steps are created, they can be refreshed when new data arrives instead of manually repeating the entire process.”
8️⃣ What is the difference between Merge and Append in Power Query?
Sample Answer:
“Merge combines columns from two queries based on a matching key, similar to a JOIN in SQL.
Append combines rows from two or more queries with similar structures, similar to UNION ALL in SQL.
For example:
Merge → combine Customer information with Customer Transactions.
Append → combine January, February, and March sales datasets.”
9️⃣ How would you find the top 5 products by revenue in Excel?
Sample Answer:
“I would first calculate or summarize revenue by product, preferably using a PivotTable. Then I would sort the products in descending order and filter the top five.
For a dynamic solution, I could also use modern Excel functions such as SORT and TAKE.”
Example:
=TAKE(SORT(A2:B100,2,-1),5)=A2**B2
An absolute reference remains fixed when the formula is copied.
For example:
=A2**$B$1
Absolute references are particularly useful when applying a calculation using a fixed assumption, tax rate, exchange rate, or target value.”
🔟 How would you clean a large dataset in Excel?
Sample Answer:
“I would first understand the structure and identify data-quality issues.
My process would typically include:
• Removing or investigating duplicates
• Handling missing values
• Standardizing text and date formats
• Correcting inconsistent values
• Checking data types
• Identifying invalid or unusual values
• Using formulas or Power Query for repeatable transformations
• Validating the cleaned dataset before analysis
For a large or recurring dataset, I would prefer Power Query rather than manually cleaning the data every time.”
Double Tap ❤️ For Part-6=COUNT(A2:A100)
=COUNTA(A2:A100)
=COUNTIF(B2:B100,"Completed")
=COUNTIFS(B2:B100,"Completed",C2:C100,">1000")
4️⃣ What is the difference between SUMIF and SUMIFS?
Sample Answer:
“SUMIF is used when I have one condition, while SUMIFS is used when I need to apply multiple conditions.
For example, to calculate sales for the India region:
=SUMIF(A:A,"India",B:B)
To calculate sales for India where the product is Laptop:
=SUMIFS(C:C,A:A,"India",B:B,"Laptop")
5️⃣ How do you remove duplicate records in Excel?
Sample Answer:
“I first determine which columns should uniquely identify a record. Then I can use Excel's Remove Duplicates feature to identify and remove duplicate rows.
However, I would not immediately delete duplicates. I would first verify whether they are genuine duplicates or legitimate repeated transactions.”
6️⃣ How do you handle missing values in Excel?
Sample Answer:
“First, I identify how many values are missing and understand why they are missing.
Depending on the situation, I may replace them with an appropriate value, use a formula such as IF or IFERROR, flag them as ‘Unknown’, or exclude them if the business requirement allows it.
I would avoid blindly replacing missing values because that can affect the accuracy of the analysis.”
7️⃣ What is conditional formatting?
Sample Answer:
“Conditional formatting automatically changes the appearance of cells based on specified conditions.
For example, I can use it to highlight sales below target, overdue transactions, duplicate values, negative profit, or unusually high values.
It is useful for quickly identifying patterns and exceptions in a dataset.”
8️⃣ How would you identify the top 10 customers by sales in Excel?
Sample Answer:
“I could use a PivotTable to summarize total sales by customer, sort the values in descending order, and filter the result to the top 10 customers.WITH RankedEmployees AS (
SELECT Employee_ID,
Department,
Salary,
DENSE_RANK() OVER (
PARTITION BY Department
ORDER BY Salary DESC
) AS Salary_Rank
FROM Employees
)
SELECT Employee_ID, Department, Salary
FROM RankedEmployees
WHERE Salary_Rank = 1;
2️⃣ How do you find customers who have never placed an order?
Sample Answer:
"I would use a LEFT JOIN between the Customers and Orders tables and then filter for customers where no matching order exists."
SELECT c.Customer_ID,
c.Customer_Name
FROM Customers c
LEFT JOIN Orders o
ON c.Customer_ID = o.Customer_ID
WHERE o.Customer_ID IS NULL;
3️⃣ How do you find the total sales for each customer?
Sample Answer:
"I would group the sales data by Customer_ID and use SUM() to calculate the total sales."
SELECT Customer_ID,
SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY Customer_ID;
4️⃣ How do you find the top 5 customers by sales?
Sample Answer:
"I would aggregate sales by customer, sort the result in descending order, and then return the top five customers."
SELECT Customer_ID,
SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY Customer_ID
ORDER BY Total_Sales DESC
LIMIT 5;
"The exact syntax for limiting rows can vary depending on the database, such as TOP in SQL Server."
5️⃣ How do you calculate the average order value?
Sample Answer:
"Average Order Value can be calculated by dividing total sales by the number of orders. If each row represents one order, AVG() can also be used directly on the order amount."
SELECT AVG(Order_Amount) AS Average_Order_Value
FROM Orders;
6️⃣ How would you identify customers who placed more than 5 orders?
Sample Answer:
"I would group the orders by Customer_ID and use HAVING to filter customers whose order count is greater than five."
SELECT Customer_ID,
COUNT(**) AS Order_Count
FROM Orders
GROUP BY Customer_ID
HAVING COUNT(**) > 5;
7️⃣ How do you find records from the last 30 days?
Sample Answer:
"I would compare the date column with the current date and subtract 30 days. The exact syntax depends on the database."
For example:
SELECT **
FROM Orders
WHERE Order_Date >= CURRENT_DATE - INTERVAL '30' DAY;
8️⃣ How do you find the total sales by month?
Sample Answer:
"I would extract the month from the order date, group the data by month, and calculate the total sales."
SELECT
DATE_TRUNC('month', Order_Date) AS Month,
SUM(Sales) AS Total_Sales
FROM Orders
GROUP BY DATE_TRUNC('month', Order_Date)
ORDER BY Month;
9️⃣ How do you find employees whose salary is above their department's average salary?
Sample Answer:
"I would calculate the average salary for each department and compare each employee's salary with that department-level average. A CTE makes this easier to read."
WITH DepartmentAverage AS (
SELECT Department,
AVG(Salary) AS Avg_Salary
FROM Employees
GROUP BY Department
)
SELECT e.Employee_ID,
e.Department,
e.Salary
FROM Employees e
JOIN DepartmentAverage d
ON e.Department = d.Department
WHERE e.Salary > d.Avg_Salary;SELECT Month,
Sales,
LAG(Sales) OVER (ORDER BY Month) AS Previous_Sales,
(Sales - LAG(Sales) OVER (ORDER BY Month))
* 100.0 /
LAG(Sales) OVER (ORDER BY Month) AS MoM_Growth
FROM Monthly_Sales;
“I would also handle cases where the previous month's value is zero or NULL to avoid incorrect calculations.”
🔟 What is the difference between DELETE, TRUNCATE, and DROP?
Sample Answer:
“DELETE removes selected rows from a table and can be used with a WHERE condition.
TRUNCATE removes all rows from a table while keeping the table structure.
DROP removes the entire table, including its structure and data.
So, the key difference is whether I'm removing specific records, all records, or the entire table itself.”
📌 Double Tap ❤️ For Part-4
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1.28 ₽ · /balance_helpSELECT Employee_ID, Salary
FROM Employees
WHERE Salary > (
SELECT AVG(Salary)
FROM Employees
);
2️⃣ What is a CTE?
Sample Answer:
“CTE stands for Common Table Expression. It allows us to define a temporary named result set using the WITH clause, which can then be referenced within the main query.
CTEs make complex queries easier to read, maintain, and debug.”
WITH CustomerSales AS (
SELECT Customer_ID,
SUM(Sales) AS Total_Sales
FROM Sales
GROUP BY Customer_ID
)
SELECT *
FROM CustomerSales
WHERE Total_Sales > 100000;
3️⃣ What is a window function?
Sample Answer:
“A window function performs a calculation across a set of related rows while still retaining the individual rows in the result.
Unlike GROUP BY, it does not collapse multiple rows into a single row.
Common window functions include ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), and LEAD().”
4️⃣ What is the difference between RANK(), DENSE_RANK(), and ROW_NUMBER()?
Sample Answer:
“ROW_NUMBER() assigns a unique sequential number to every row.
RANK() assigns the same rank to tied values but leaves gaps after a tie.
DENSE_RANK() also assigns the same rank to tied values but does not leave gaps.”
Example:
Values: 100, 100, 90
ROW_NUMBER: 1, 2, 3
RANK: 1, 1, 3
DENSE_RANK: 1, 1, 2
5️⃣ How would you find the second-highest salary?
Sample Answer:
“One approach is to use DENSE_RANK(). This also handles duplicate salaries correctly.”
WITH RankedEmployees AS (
SELECT Employee_ID,
Salary,
DENSE_RANK() OVER (ORDER BY Salary DESC) AS Salary_Rank
FROM Employees
)
SELECT Employee_ID, Salary
FROM RankedEmployees
WHERE Salary_Rank = 2;
6️⃣ How would you find the top 3 salaries in each department?
Sample Answer:
“I would use a window function to rank employees within each department.”
WITH RankedEmployees AS (
SELECT Employee_ID,
Department,
Salary,
DENSE_RANK() OVER (
PARTITION BY Department
ORDER BY Salary DESC
) AS Salary_Rank
FROM Employees
)
SELECT *
FROM RankedEmployees
WHERE Salary_Rank <= 3;
“The PARTITION BY ensures that ranking starts separately for each department.”
7️⃣ What is PARTITION BY in SQL?
Sample Answer:
“PARTITION BY divides the result set into groups for a window function without collapsing the rows.
For example, if I want to rank employees separately within each department, I can use PARTITION BY Department.”
SELECT Employee_ID,
Department,
Salary,
RANK() OVER (
PARTITION BY Department
ORDER BY Salary DESC
) AS Salary_Rank
FROM Employees;
8️⃣ What are LAG() and LEAD() functions?
Sample Answer:
“LAG() allows me to access a value from a previous row, while LEAD() allows me to access a value from a following row.
They are particularly useful for comparing current values with previous or future values, such as month-over-month sales.”
SELECT Month,
Sales,
LAG(Sales) OVER (ORDER BY Month) AS Previous_Month_Sales
FROM Monthly_Sales;SELECT Customer_ID, COUNT(*) AS Count_Records
FROM Customers
GROUP BY Customer_ID
HAVING COUNT(*) > 1;
"This identifies Customer_ID values that appear more than once. I would then investigate whether those records are genuine duplicates before taking any corrective action."
📌 Double Tap ❤️ For Part-3
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1.38 ₽ · /balance_help