Kandinsky 5.0 Video Lite and Kandinsky 5.0 Video Pro generative models on the global text-to-video landscape
๐Pro is currently the #1 open-source model worldwide
๐Lite (2B parameters) outperforms Sora v1.
๐Only Google (Veo 3.1, Veo 3), OpenAI (Sora 2), Alibaba (Wan 2.5), and KlingAI (Kling 2.5, 2.6) outperform Pro โ these are objectively the strongest video generation models in production today. We are on par with Luma AI (Ray 3) and MiniMax (Hailuo 2.3): the maximum ELO gap is 3 points, with a 95% CI of ยฑ21.
Useful links
๐Full leaderboard: LM Arena
๐Kandinsky 5.0 details: technical report
๐Open-source Kandinsky 5.0: GitHub and Hugging Face
๐Pro is currently the #1 open-source model worldwide
๐Lite (2B parameters) outperforms Sora v1.
๐Only Google (Veo 3.1, Veo 3), OpenAI (Sora 2), Alibaba (Wan 2.5), and KlingAI (Kling 2.5, 2.6) outperform Pro โ these are objectively the strongest video generation models in production today. We are on par with Luma AI (Ray 3) and MiniMax (Hailuo 2.3): the maximum ELO gap is 3 points, with a 95% CI of ยฑ21.
Useful links
๐Full leaderboard: LM Arena
๐Kandinsky 5.0 details: technical report
๐Open-source Kandinsky 5.0: GitHub and Hugging Face
โค4
1. What is the AdaBoost Algorithm?
AdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level that means with Decision trees with only 1 split. These trees are also called Decision Stumps. What this algorithm does is that it builds a model and gives equal weights to all the data points. It then assigns higher weights to points that are wrongly classified. Now all the points which have higher weights are given more importance in the next model. It will keep training models until and unless a lower error is received.
2. What is the Sliding Window method for Time Series Forecasting?
Time series can be phrased as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem.
In the sliding window method, the previous time steps can be used as input variables, and the next time steps can be used as the output variable.
In statistics and time series analysis, this is called a lag or lag method. The number of previous time steps is called the window width or size of the lag. This sliding window is the basis for how we can turn any time series dataset into a supervised learning problem.
3. What do you understand by sub-queries in SQL?
A subquery is a query inside another query where a query is defined to retrieve data or information back from the database. In a subquery, the outer query is called as the main query whereas the inner query is called subquery. Subqueries are always executed first and the result of the subquery is passed on to the main query. It can be nested inside a SELECT, UPDATE or any other query. A subquery can also use any comparison operators such as >,< or =.
4. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook?
Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A workbook contains sheets, which can be a worksheet, dashboard, or a story.
A worksheet contains a single view along with shelves, legends, and the Data pane.
A dashboard is a collection of views from multiple worksheets.
A story contains a sequence of worksheets or dashboards that work together to convey information.
5. How is a Random Forest related to Decision Trees?
Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks.
Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do.
A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance.
6. What are some disadvantages of using Naive Bayes Algorithm?
Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.
AdaBoost also called Adaptive Boosting is a technique in Machine Learning used as an Ensemble Method. The most common algorithm used with AdaBoost is decision trees with one level that means with Decision trees with only 1 split. These trees are also called Decision Stumps. What this algorithm does is that it builds a model and gives equal weights to all the data points. It then assigns higher weights to points that are wrongly classified. Now all the points which have higher weights are given more importance in the next model. It will keep training models until and unless a lower error is received.
2. What is the Sliding Window method for Time Series Forecasting?
Time series can be phrased as supervised learning. Given a sequence of numbers for a time series dataset, we can restructure the data to look like a supervised learning problem.
In the sliding window method, the previous time steps can be used as input variables, and the next time steps can be used as the output variable.
In statistics and time series analysis, this is called a lag or lag method. The number of previous time steps is called the window width or size of the lag. This sliding window is the basis for how we can turn any time series dataset into a supervised learning problem.
3. What do you understand by sub-queries in SQL?
A subquery is a query inside another query where a query is defined to retrieve data or information back from the database. In a subquery, the outer query is called as the main query whereas the inner query is called subquery. Subqueries are always executed first and the result of the subquery is passed on to the main query. It can be nested inside a SELECT, UPDATE or any other query. A subquery can also use any comparison operators such as >,< or =.
4. Explain the Difference Between Tableau Worksheet, Dashboard, Story, and Workbook?
Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A workbook contains sheets, which can be a worksheet, dashboard, or a story.
A worksheet contains a single view along with shelves, legends, and the Data pane.
A dashboard is a collection of views from multiple worksheets.
A story contains a sequence of worksheets or dashboards that work together to convey information.
5. How is a Random Forest related to Decision Trees?
Random forest is an ensemble learning method that works by constructing a multitude of decision trees. A random forest can be constructed for both classification and regression tasks.
Random forest outperforms decision trees, and it also does not have the habit of overfitting the data as decision trees do.
A decision tree trained on a specific dataset will become very deep and cause overfitting. To create a random forest, decision trees can be trained on different subsets of the training dataset, and then the different decision trees can be averaged with the goal of decreasing the variance.
6. What are some disadvantages of using Naive Bayes Algorithm?
Some disadvantages of using Naive Bayes Algorithm are:
It relies on a very big assumption that the independent variables are not related to each other.
It is generally not suitable for datasets with large numbers of numerical attributes.
It has been observed that if a rare case is not in the training dataset but is in the testing dataset, then it will most definitely be wrong.
โค4
Data Analytics Interview Questions with Answers Part-1: ๐ฑ
1. What is the difference between data analysis and data analytics?
โฆ Data analysis involves inspecting, cleaning, and modeling data to discover useful information and patterns for decision-making.
โฆ Data analytics is a broader process that includes data collection, transformation, analysis, and interpretation, often involving predictive and prescriptive techniques to drive business strategies.
2. Explain the data cleaning process you follow.
โฆ Identify missing, inconsistent, or corrupt data.
โฆ Handle missing data by imputation (mean, median, mode) or removal if appropriate.
โฆ Standardize formats (dates, strings).
โฆ Remove duplicates.
โฆ Detect and treat outliers.
โฆ Validate cleaned data against known business rules.
3. How do you handle missing or duplicate data?
โฆ Missing data: Identify patterns; if random, impute using statistical methods or predictive modeling; else consider domain knowledge before removal.
โฆ Duplicate data: Detect with key fields; remove exact duplicates or merge fuzzy duplicates based on context.
4. What is a primary key in a database?
A primary key uniquely identifies each record in a table, ensuring entity integrity and enabling relationships between tables via foreign keys.
5. Write a SQL query to find the second highest salary in a table.
6. Explain INNER JOIN vs LEFT JOIN with examples.
โฆ INNER JOIN: Returns only matching rows between two tables.
โฆ LEFT JOIN: Returns all rows from the left table, plus matching rows from the right; if no match, right columns are NULL.
Example:
7. What are outliers? How do you detect and treat them?
โฆ Outliers are data points significantly different from others that can skew analysis.
โฆ Detect with boxplots, z-score (>3), or IQR method (values outside 1.5*IQR).
โฆ Treat by investigating causes, correcting errors, transforming data, or removing if theyโre noise.
8. Describe what a pivot table is and how you use it.
A pivot table is a data summarization tool that groups, aggregates (sum, average), and displays data cross-categorically. Used in Excel and BI tools for quick insights and reporting.
9. How do you validate a data modelโs performance?
โฆ Use relevant metrics (accuracy, precision, recall for classification; RMSE, MAE for regression).
โฆ Perform cross-validation to check generalizability.
โฆ Test on holdout or unseen data sets.
10. What is hypothesis testing? Explain t-test and z-test.
โฆ Hypothesis testing assesses if sample data supports a claim about a population.
โฆ t-test: Used when sample size is small and population variance is unknown, often comparing means.
โฆ z-test: Used for large samples with known variance to test population parameters.
React โฅ๏ธ for Part-2
1. What is the difference between data analysis and data analytics?
โฆ Data analysis involves inspecting, cleaning, and modeling data to discover useful information and patterns for decision-making.
โฆ Data analytics is a broader process that includes data collection, transformation, analysis, and interpretation, often involving predictive and prescriptive techniques to drive business strategies.
2. Explain the data cleaning process you follow.
โฆ Identify missing, inconsistent, or corrupt data.
โฆ Handle missing data by imputation (mean, median, mode) or removal if appropriate.
โฆ Standardize formats (dates, strings).
โฆ Remove duplicates.
โฆ Detect and treat outliers.
โฆ Validate cleaned data against known business rules.
3. How do you handle missing or duplicate data?
โฆ Missing data: Identify patterns; if random, impute using statistical methods or predictive modeling; else consider domain knowledge before removal.
โฆ Duplicate data: Detect with key fields; remove exact duplicates or merge fuzzy duplicates based on context.
4. What is a primary key in a database?
A primary key uniquely identifies each record in a table, ensuring entity integrity and enabling relationships between tables via foreign keys.
5. Write a SQL query to find the second highest salary in a table.
SELECT MAX(salary)
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
6. Explain INNER JOIN vs LEFT JOIN with examples.
โฆ INNER JOIN: Returns only matching rows between two tables.
โฆ LEFT JOIN: Returns all rows from the left table, plus matching rows from the right; if no match, right columns are NULL.
Example:
SELECT * FROM A INNER JOIN B ON A.id = B.id;
SELECT * FROM A LEFT JOIN B ON A.id = B.id;
7. What are outliers? How do you detect and treat them?
โฆ Outliers are data points significantly different from others that can skew analysis.
โฆ Detect with boxplots, z-score (>3), or IQR method (values outside 1.5*IQR).
โฆ Treat by investigating causes, correcting errors, transforming data, or removing if theyโre noise.
8. Describe what a pivot table is and how you use it.
A pivot table is a data summarization tool that groups, aggregates (sum, average), and displays data cross-categorically. Used in Excel and BI tools for quick insights and reporting.
9. How do you validate a data modelโs performance?
โฆ Use relevant metrics (accuracy, precision, recall for classification; RMSE, MAE for regression).
โฆ Perform cross-validation to check generalizability.
โฆ Test on holdout or unseen data sets.
10. What is hypothesis testing? Explain t-test and z-test.
โฆ Hypothesis testing assesses if sample data supports a claim about a population.
โฆ t-test: Used when sample size is small and population variance is unknown, often comparing means.
โฆ z-test: Used for large samples with known variance to test population parameters.
React โฅ๏ธ for Part-2
โค5
๐ง Most Asked Data Analyst Interview Question
โ โHow do you handle missing data?โ
โ Weak answer:
โI remove the rows.โ
โ Strong answer:
โIt depends on the business impact and data context.โ
โ๏ธ Check how much data is missing
โ๏ธ Understand why itโs missing
โ๏ธ Decide based on use case:
โข Drop rows (if very small % and random)
โข Impute (mean/median/mode)
โข Flag missing values
โข Leave as-is if meaningful
๐ฏ Interviewer is testing:
Your decision-making, not your tools.
๐ก Always explain why, not just how.
๐ React if you want Interview Prep #2 tomorrow
โ โHow do you handle missing data?โ
โ Weak answer:
โI remove the rows.โ
โ Strong answer:
โIt depends on the business impact and data context.โ
โ๏ธ Check how much data is missing
โ๏ธ Understand why itโs missing
โ๏ธ Decide based on use case:
โข Drop rows (if very small % and random)
โข Impute (mean/median/mode)
โข Flag missing values
โข Leave as-is if meaningful
๐ฏ Interviewer is testing:
Your decision-making, not your tools.
๐ก Always explain why, not just how.
๐ React if you want Interview Prep #2 tomorrow
โค10๐2
๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐ฐ๐น๐ฎ๐๐ ๐๐ ๐๐ป๐ฑ๐๐๐๐ฟ๐ ๐๐
๐ฝ๐ฒ๐ฟ๐๐ ๐
Roadmap to land your dream job in top product-based companies
๐๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐๐ฒ๐:-
- 90-Day Placement Plan
- Tech & Non-Tech Career Path
- Interview Preparation Tips
- Live Q&A
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฅ๐๐๐:-
https://pdlink.in/3Ltb3CE
Date & Time:- 06th January 2026 , 7PM
Roadmap to land your dream job in top product-based companies
๐๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐๐ฒ๐:-
- 90-Day Placement Plan
- Tech & Non-Tech Career Path
- Interview Preparation Tips
- Live Q&A
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฅ๐๐๐:-
https://pdlink.in/3Ltb3CE
Date & Time:- 06th January 2026 , 7PM
SQL Interview Questions !!
๐ Write a query to find all employees whose salaries exceed the company's average salary.
๐ Write a query to retrieve the names of employees who work in the same department as 'John Doe'.
๐ Write a query to display the second highest salary from the Employee table without using the MAX function twice.
๐ Write a query to find all customers who have placed more than five orders.
๐ Write a query to count the total number of orders placed by each customer.
๐ Write a query to list employees who joined the company within the last 6 months.
๐ Write a query to calculate the total sales amount for each product.
๐ Write a query to list all products that have never been sold.
๐ Write a query to remove duplicate rows from a table.
๐ Write a query to identify the top 10 customers who have not placed any orders in the past year.
Here you can find essential SQL Interview Resources๐
https://t.iss.one/mysqldata
Like this post if you need more ๐โค๏ธ
Hope it helps :)
๐ Write a query to find all employees whose salaries exceed the company's average salary.
๐ Write a query to retrieve the names of employees who work in the same department as 'John Doe'.
๐ Write a query to display the second highest salary from the Employee table without using the MAX function twice.
๐ Write a query to find all customers who have placed more than five orders.
๐ Write a query to count the total number of orders placed by each customer.
๐ Write a query to list employees who joined the company within the last 6 months.
๐ Write a query to calculate the total sales amount for each product.
๐ Write a query to list all products that have never been sold.
๐ Write a query to remove duplicate rows from a table.
๐ Write a query to identify the top 10 customers who have not placed any orders in the past year.
Here you can find essential SQL Interview Resources๐
https://t.iss.one/mysqldata
Like this post if you need more ๐โค๏ธ
Hope it helps :)
๐2โค1
๐ง๐ผ๐ฝ ๐ฑ ๐๐ป-๐๐ฒ๐บ๐ฎ๐ป๐ฑ ๐ฆ๐ธ๐ถ๐น๐น๐ ๐๐ผ ๐๐ผ๐ฐ๐๐ ๐ผ๐ป ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฒ๐
Start learning industry-relevant data skills today at zero cost!
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๐ Enroll Now & Get Certified
Start learning industry-relevant data skills today at zero cost!
๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐:- https://pdlink.in/497MMLw
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๐ Enroll Now & Get Certified
โค1
Data Analyst Interview Questions & Preparation Tips
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with โค๏ธ if you want me to also post sample answer for the above questions
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Be prepared with a mix of technical, analytical, and business-oriented interview questions.
1. Technical Questions (Data Analysis & Reporting)
SQL Questions:
How do you write a query to fetch the top 5 highest revenue-generating customers?
Explain the difference between INNER JOIN, LEFT JOIN, and FULL OUTER JOIN.
How would you optimize a slow-running query?
What are CTEs and when would you use them?
Data Visualization (Power BI / Tableau / Excel)
How would you create a dashboard to track key performance metrics?
Explain the difference between measures and calculated columns in Power BI.
How do you handle missing data in Tableau?
What are DAX functions, and can you give an example?
ETL & Data Processing (Alteryx, Power BI, Excel)
What is ETL, and how does it relate to BI?
Have you used Alteryx for data transformation? Explain a complex workflow you built.
How do you automate reporting using Power Query in Excel?
2. Business and Analytical Questions
How do you define KPIs for a business process?
Give an example of how you used data to drive a business decision.
How would you identify cost-saving opportunities in a reporting process?
Explain a time when your report uncovered a hidden business insight.
3. Scenario-Based & Behavioral Questions
Stakeholder Management:
How do you handle a situation where different business units have conflicting reporting requirements?
How do you explain complex data insights to non-technical stakeholders?
Problem-Solving & Debugging:
What would you do if your report is showing incorrect numbers?
How do you ensure the accuracy of a new KPI you introduced?
Project Management & Process Improvement:
Have you led a project to automate or improve a reporting process?
What steps do you take to ensure the timely delivery of reports?
4. Industry-Specific Questions (Credit Reporting & Financial Services)
What are some key credit risk metrics used in financial services?
How would you analyze trends in customer credit behavior?
How do you ensure compliance and data security in reporting?
5. General HR Questions
Why do you want to work at this company?
Tell me about a challenging project and how you handled it.
What are your strengths and weaknesses?
Where do you see yourself in five years?
How to Prepare?
Brush up on SQL, Power BI, and ETL tools (especially Alteryx).
Learn about key financial and credit reporting metrics.(varies company to company)
Practice explaining data-driven insights in a business-friendly manner.
Be ready to showcase problem-solving skills with real-world examples.
React with โค๏ธ if you want me to also post sample answer for the above questions
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค7
๐ง Data Analyst Interview Common Interview Traps โ Day 4
โ โIs NULL equal to zero or an empty string?โ
โ Trap Answer:
โYes, NULL means no value, so itโs like zero or empty.โ
โ Smart Answer:
โNo. NULL means unknown or missing.
It behaves differently in comparisons, aggregations, and joins.โ
๐ฏ Interviewer is testing:
Your understanding of three-valued logic.
๐ก Tip:
Always handle NULLs explicitly.
React ๐ if you want interview prep #5 tomorrow
โ โIs NULL equal to zero or an empty string?โ
โ Trap Answer:
โYes, NULL means no value, so itโs like zero or empty.โ
โ Smart Answer:
โNo. NULL means unknown or missing.
It behaves differently in comparisons, aggregations, and joins.โ
๐ฏ Interviewer is testing:
Your understanding of three-valued logic.
๐ก Tip:
Always handle NULLs explicitly.
React ๐ if you want interview prep #5 tomorrow
โค4๐2
๐ Day 6 โ Data Analyst Most Asked Interview Question โ
UNION vs UNION ALL (SQL)
โโโโโโโโโโโโโโ
UNION
โข Combines result sets
โข Removes duplicate rows
โข Slightly slower due to deduplication
โข Columns count & data types must match
UNION ALL
โข Combines result sets
โข Keeps duplicates
โข Faster than UNION
โข Columns count & data types must match
โโโโโโโโโโโโโโ
Rule:
๐ Duplicates should be removed โ UNION
๐ Performance matters & duplicates allowed โ UNION ALL โ
โโโโโโโโโโโโโโ
โค๏ธ React โค๏ธ if you want interview prep Day 7 Tomorrow ๐ฅ
UNION vs UNION ALL (SQL)
โโโโโโโโโโโโโโ
UNION
โข Combines result sets
โข Removes duplicate rows
โข Slightly slower due to deduplication
โข Columns count & data types must match
UNION ALL
โข Combines result sets
โข Keeps duplicates
โข Faster than UNION
โข Columns count & data types must match
โโโโโโโโโโโโโโ
Rule:
๐ Duplicates should be removed โ UNION
๐ Performance matters & duplicates allowed โ UNION ALL โ
โโโโโโโโโโโโโโ
โค๏ธ React โค๏ธ if you want interview prep Day 7 Tomorrow ๐ฅ
โค2
๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐ฐ๐น๐ฎ๐๐ ๐ข๐ป ๐๐ฎ๐๐ฒ๐๐ ๐ง๐ฒ๐ฐ๐ต๐ป๐ผ๐น๐ผ๐ด๐ถ๐ฒ๐๐
- Data Science
- AI/ML
- Data Analytics
- UI/UX
- Full-stack Development
Get Job-Ready Guidance in Your Tech Journey
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฅ๐๐๐:-
https://pdlink.in/4sw5Ev8
Date :- 11th January 2026
- Data Science
- AI/ML
- Data Analytics
- UI/UX
- Full-stack Development
Get Job-Ready Guidance in Your Tech Journey
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐๐ผ๐ฟ ๐๐ฅ๐๐๐:-
https://pdlink.in/4sw5Ev8
Date :- 11th January 2026
โ
SQL Interview Challenge ๐ผ๐ง
๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐ฒ๐ฟ: How would you count how many employees are in each department?
๐ ๐ฒ: Iโd use the
๐น Query:
โ Why it works:
โ
โ
โ Clean, scalable, and works with large datasets
๐ Bonus Insight:
To filter only departments with more than 5 employees:
โ
โ Useful in dashboards, reports, and business logic
๐ฌ Tap โค๏ธ for more SQL interview tips!
๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐๐ฒ๐ฟ: How would you count how many employees are in each department?
๐ ๐ฒ: Iโd use the
GROUP BY clause with COUNT(*) to aggregate employee counts per department.๐น Query:
SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department;
โ Why it works:
โ
GROUP BY groups rows by department โ
COUNT(*) counts employees in each group โ Clean, scalable, and works with large datasets
๐ Bonus Insight:
To filter only departments with more than 5 employees:
SELECT department, COUNT(*) AS employee_count
FROM employees
GROUP BY department
HAVING COUNT(*) > 5;
โ
HAVING filters aggregated results โ Useful in dashboards, reports, and business logic
๐ฌ Tap โค๏ธ for more SQL interview tips!
โค6
๐๐ถ๐ด๐ต ๐๐ฒ๐บ๐ฎ๐ป๐ฑ๐ถ๐ป๐ด ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ช๐ถ๐๐ต ๐ฃ๐น๐ฎ๐ฐ๐ฒ๐บ๐ฒ๐ป๐ ๐๐๐๐ถ๐๐๐ฎ๐ป๐ฐ๐ฒ๐
Learn from IIT faculty and industry experts.
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IIT Roorkee Agentic Systems:- https://pdlink.in/4aTKgdc
Upskill in todayโs most in-demand tech domains and boost your career ๐
Learn from IIT faculty and industry experts.
IIT Roorkee DS & AI Program :- https://pdlink.in/4qHVFkI
IIT Patna AI & ML :- https://pdlink.in/4pBNxkV
IIM Mumbai DM & Analytics :- https://pdlink.in/4jvuHdE
IIM Rohtak Product Management:- https://pdlink.in/4aMtk8i
IIT Roorkee Agentic Systems:- https://pdlink.in/4aTKgdc
Upskill in todayโs most in-demand tech domains and boost your career ๐
โ
Data Analyst Interview Questions for Freshers ๐
1) What is the role of a data analyst?
Answer: A data analyst collects, processes, and performs statistical analyses on data to provide actionable insights that support business decision-making.
2) What are the key skills required for a data analyst?
Answer: Strong skills in SQL, Excel, data visualization tools (like Tableau or Power BI), statistical analysis, and problem-solving abilities are essential.
3) What is data cleaning?
Answer: Data cleaning involves identifying and correcting inaccuracies, inconsistencies, or missing values in datasets to improve data quality.
4) What is the difference between structured and unstructured data?
Answer: Structured data is organized in rows and columns (e.g., spreadsheets), while unstructured data includes formats like text, images, and videos that lack a predefined structure.
5) What is a KPI?
Answer: KPI stands for Key Performance Indicator, which is a measurable value that demonstrates how effectively a company is achieving its business goals.
6) What tools do you use for data analysis?
Answer: Common tools include Excel, SQL, Python (with libraries like Pandas), R, Tableau, and Power BI.
7) Why is data visualization important?
Answer: Data visualization helps translate complex data into understandable charts and graphs, making it easier for stakeholders to grasp insights and trends.
8) What is a pivot table?
Answer: A pivot table is a feature in Excel that allows you to summarize, analyze, and explore data by reorganizing and grouping it dynamically.
9) What is correlation?
Answer: Correlation measures the statistical relationship between two variables, indicating whether they move together and how strongly.
10) What is a data warehouse?
Answer: A data warehouse is a centralized repository that consolidates data from multiple sources, optimized for querying and analysis.
11) Explain the difference between INNER JOIN and OUTER JOIN in SQL.
Answer: INNER JOIN returns only the matching rows between two tables, while OUTER JOIN returns all matching rows plus unmatched rows from one or both tables, depending on whether itโs LEFT, RIGHT, or FULL OUTER JOIN.
12) What is hypothesis testing?
Answer: Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample to infer that a certain condition holds true for the entire population.
13) What is the difference between mean, median, and mode?
Answer:
โฆ Mean: The average of all numbers.
โฆ Median: The middle value when data is sorted.
โฆ Mode: The most frequently occurring value in a dataset.
14) What is data normalization?
Answer: Normalization is the process of organizing data to reduce redundancy and improve integrity, often by dividing data into related tables.
15) How do you handle missing data?
Answer: Missing data can be handled by removing rows, imputing values (mean, median, mode), or using algorithms that support missing data.
๐ฌ React โค๏ธ for more!
1) What is the role of a data analyst?
Answer: A data analyst collects, processes, and performs statistical analyses on data to provide actionable insights that support business decision-making.
2) What are the key skills required for a data analyst?
Answer: Strong skills in SQL, Excel, data visualization tools (like Tableau or Power BI), statistical analysis, and problem-solving abilities are essential.
3) What is data cleaning?
Answer: Data cleaning involves identifying and correcting inaccuracies, inconsistencies, or missing values in datasets to improve data quality.
4) What is the difference between structured and unstructured data?
Answer: Structured data is organized in rows and columns (e.g., spreadsheets), while unstructured data includes formats like text, images, and videos that lack a predefined structure.
5) What is a KPI?
Answer: KPI stands for Key Performance Indicator, which is a measurable value that demonstrates how effectively a company is achieving its business goals.
6) What tools do you use for data analysis?
Answer: Common tools include Excel, SQL, Python (with libraries like Pandas), R, Tableau, and Power BI.
7) Why is data visualization important?
Answer: Data visualization helps translate complex data into understandable charts and graphs, making it easier for stakeholders to grasp insights and trends.
8) What is a pivot table?
Answer: A pivot table is a feature in Excel that allows you to summarize, analyze, and explore data by reorganizing and grouping it dynamically.
9) What is correlation?
Answer: Correlation measures the statistical relationship between two variables, indicating whether they move together and how strongly.
10) What is a data warehouse?
Answer: A data warehouse is a centralized repository that consolidates data from multiple sources, optimized for querying and analysis.
11) Explain the difference between INNER JOIN and OUTER JOIN in SQL.
Answer: INNER JOIN returns only the matching rows between two tables, while OUTER JOIN returns all matching rows plus unmatched rows from one or both tables, depending on whether itโs LEFT, RIGHT, or FULL OUTER JOIN.
12) What is hypothesis testing?
Answer: Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample to infer that a certain condition holds true for the entire population.
13) What is the difference between mean, median, and mode?
Answer:
โฆ Mean: The average of all numbers.
โฆ Median: The middle value when data is sorted.
โฆ Mode: The most frequently occurring value in a dataset.
14) What is data normalization?
Answer: Normalization is the process of organizing data to reduce redundancy and improve integrity, often by dividing data into related tables.
15) How do you handle missing data?
Answer: Missing data can be handled by removing rows, imputing values (mean, median, mode), or using algorithms that support missing data.
๐ฌ React โค๏ธ for more!
โค5
๐ Day 7 โ Data Analyst Most Asked Interview Question โ
DELETE vs TRUNCATE vs DROP (SQL)
โโโโโโโโโโโโโโ
DELETE
โข Removes specific rows using WHERE
โข Can be rolled back (transactional)
โข Table structure remains
โข Slower for large data
TRUNCATE
โข Removes all rows at once
โข Cannot be rolled back
โข Table structure remains
โข Faster than DELETE
DROP
โข Removes entire table
โข Deletes data + structure
โข Cannot be rolled back
โข Frees storage completely
โโโโโโโโโโโโโโ
Rule:
๐ Remove specific data โ DELETE
๐ Clear entire table fast โ TRUNCATE
๐ Remove table completely โ DROP โ
โโโโโโโโโโโโโโ
โค๏ธ React โค๏ธ if you want interview prep Day 8 Tomorrow ๐ฅ
DELETE vs TRUNCATE vs DROP (SQL)
โโโโโโโโโโโโโโ
DELETE
โข Removes specific rows using WHERE
โข Can be rolled back (transactional)
โข Table structure remains
โข Slower for large data
TRUNCATE
โข Removes all rows at once
โข Cannot be rolled back
โข Table structure remains
โข Faster than DELETE
DROP
โข Removes entire table
โข Deletes data + structure
โข Cannot be rolled back
โข Frees storage completely
โโโโโโโโโโโโโโ
Rule:
๐ Remove specific data โ DELETE
๐ Clear entire table fast โ TRUNCATE
๐ Remove table completely โ DROP โ
โโโโโโโโโโโโโโ
โค๏ธ React โค๏ธ if you want interview prep Day 8 Tomorrow ๐ฅ
โค7
๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐
๐Upgrade your skills with industry-relevant Data Analytics training at ZERO cost
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โ Certificate on completion
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๐๐ข๐ง๐ค ๐:-
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๐ 100% FREE โ Limited seats available!
๐Upgrade your skills with industry-relevant Data Analytics training at ZERO cost
โ Beginner-friendly
โ Certificate on completion
โ High-demand skill in 2026
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/497MMLw
๐ 100% FREE โ Limited seats available!
โ
If you're serious about learning Data Analytics โ follow this roadmap ๐๐ง
1. Learn Excel basics โ formulas, pivot tables, charts
2. Master SQL โ SELECT, JOIN, GROUP BY, CTEs, window functions
3. Get good at Python โ especially Pandas, NumPy, Matplotlib, Seaborn
4. Understand statistics โ mean, median, standard deviation, correlation, hypothesis testing
5. Clean and wrangle data โ handle missing values, outliers, normalization, encoding
6. Practice Exploratory Data Analysis (EDA) โ univariate, bivariate analysis
7. Work on real datasets โ sales, customer, finance, healthcare, etc.
8. Use Power BI or Tableau โ create dashboards and data stories
9. Learn business metrics KPIs โ retention rate, CLV, ROI, conversion rate
10. Build mini-projects โ sales dashboard, HR analytics, customer segmentation
11. Understand A/B Testing โ setup, analysis, significance
12. Practice SQL + Python combo โ extract, clean, visualize, analyze
13. Learn about data pipelines โ basic ETL concepts, Airflow, dbt
14. Use version control โ Git GitHub for all projects
15. Document your analysis โ use Jupyter or Notion to explain insights
16. Practice storytelling with data โ explain โso what?โ clearly
17. Know how to answer business questions using data
18. Explore cloud tools (optional) โ BigQuery, AWS S3, Redshift
19. Solve case studies โ product analysis, churn, marketing impact
20. Apply for internships/freelance โ gain experience + build resume
21. Post your projects on GitHub or portfolio site
22. Prepare for interviews โ SQL, Python, scenario-based questions
23. Keep learning โ YouTube, courses, Kaggle, LinkedIn Learning
๐ก Tip: Focus on building 3โ5 strong projects and learn to explain them in interviews.
๐ฌ Tap โค๏ธ for more!
1. Learn Excel basics โ formulas, pivot tables, charts
2. Master SQL โ SELECT, JOIN, GROUP BY, CTEs, window functions
3. Get good at Python โ especially Pandas, NumPy, Matplotlib, Seaborn
4. Understand statistics โ mean, median, standard deviation, correlation, hypothesis testing
5. Clean and wrangle data โ handle missing values, outliers, normalization, encoding
6. Practice Exploratory Data Analysis (EDA) โ univariate, bivariate analysis
7. Work on real datasets โ sales, customer, finance, healthcare, etc.
8. Use Power BI or Tableau โ create dashboards and data stories
9. Learn business metrics KPIs โ retention rate, CLV, ROI, conversion rate
10. Build mini-projects โ sales dashboard, HR analytics, customer segmentation
11. Understand A/B Testing โ setup, analysis, significance
12. Practice SQL + Python combo โ extract, clean, visualize, analyze
13. Learn about data pipelines โ basic ETL concepts, Airflow, dbt
14. Use version control โ Git GitHub for all projects
15. Document your analysis โ use Jupyter or Notion to explain insights
16. Practice storytelling with data โ explain โso what?โ clearly
17. Know how to answer business questions using data
18. Explore cloud tools (optional) โ BigQuery, AWS S3, Redshift
19. Solve case studies โ product analysis, churn, marketing impact
20. Apply for internships/freelance โ gain experience + build resume
21. Post your projects on GitHub or portfolio site
22. Prepare for interviews โ SQL, Python, scenario-based questions
23. Keep learning โ YouTube, courses, Kaggle, LinkedIn Learning
๐ก Tip: Focus on building 3โ5 strong projects and learn to explain them in interviews.
๐ฌ Tap โค๏ธ for more!
โค3