SQL Interview Questions with Answers Part-1: ☑️
1. What is SQL?
SQL (Structured Query Language) is a standardized programming language designed to manage and manipulate relational databases. It allows you to query, insert, update, and delete data, as well as create and modify schema objects like tables and views.
2. Differentiate between SQL and NoSQL databases.
SQL databases are relational, table-based, and use structured query language with fixed schemas, ideal for complex queries and transactions. NoSQL databases are non-relational, can be document, key-value, graph, or column-oriented, and are schema-flexible, designed for scalability and handling unstructured data.
3. What are the different types of SQL commands?
⦁ DDL (Data Definition Language): CREATE, ALTER, DROP (define and modify structure)
⦁ DML (Data Manipulation Language): SELECT, INSERT, UPDATE, DELETE (data operations)
⦁ DCL (Data Control Language): GRANT, REVOKE (permission control)
⦁ TCL (Transaction Control Language): COMMIT, ROLLBACK, SAVEPOINT (transaction management)
4. Explain the difference between WHERE and HAVING clauses.
⦁
⦁
5. Write a SQL query to find the second highest salary in a table.
Using a subquery:
Or using DENSE_RANK():
6. What is a JOIN? Explain different types of JOINs.
A JOIN combines rows from two or more tables based on a related column:
⦁ INNER JOIN: returns matching rows from both tables.
⦁ LEFT JOIN (LEFT OUTER JOIN): all rows from the left table, matched rows from right.
⦁ RIGHT JOIN (RIGHT OUTER JOIN): all rows from right table, matched rows from left.
⦁ FULL JOIN (FULL OUTER JOIN): all rows when there’s a match in either table.
⦁ CROSS JOIN: Cartesian product of both tables.
7. How do you optimize slow-performing SQL queries?
⦁ Use indexes appropriately to speed up lookups.
⦁ Avoid SELECT *; only select necessary columns.
⦁ Use joins carefully; filter early with WHERE clauses.
⦁ Analyze execution plans to identify bottlenecks.
⦁ Avoid unnecessary subqueries; use EXISTS or JOINs.
⦁ Limit result sets with pagination if dealing with large datasets.
8. What is a primary key? What is a foreign key?
⦁ Primary Key: A unique identifier for records in a table; it cannot be NULL.
⦁ Foreign Key: A field that creates a link between two tables by referring to the primary key in another table, enforcing referential integrity.
9. What are indexes? Explain clustered and non-clustered indexes.
⦁ Indexes speed up data retrieval by providing quick lookups.
⦁ Clustered Index: Sorts and stores the actual data rows in the table based on the key; a table can have only one clustered index.
⦁ Non-Clustered Index: Creates a separate structure that points to the data rows; tables can have multiple non-clustered indexes.
10. Write a SQL query to fetch the top 5 records from a table.
In SQL Server and PostgreSQL:
In SQL Server (older syntax):
React ♥️ for Part 2
1. What is SQL?
SQL (Structured Query Language) is a standardized programming language designed to manage and manipulate relational databases. It allows you to query, insert, update, and delete data, as well as create and modify schema objects like tables and views.
2. Differentiate between SQL and NoSQL databases.
SQL databases are relational, table-based, and use structured query language with fixed schemas, ideal for complex queries and transactions. NoSQL databases are non-relational, can be document, key-value, graph, or column-oriented, and are schema-flexible, designed for scalability and handling unstructured data.
3. What are the different types of SQL commands?
⦁ DDL (Data Definition Language): CREATE, ALTER, DROP (define and modify structure)
⦁ DML (Data Manipulation Language): SELECT, INSERT, UPDATE, DELETE (data operations)
⦁ DCL (Data Control Language): GRANT, REVOKE (permission control)
⦁ TCL (Transaction Control Language): COMMIT, ROLLBACK, SAVEPOINT (transaction management)
4. Explain the difference between WHERE and HAVING clauses.
⦁
WHERE filters rows before grouping (used with SELECT, UPDATE).⦁
HAVING filters groups after aggregation (used with GROUP BY), e.g., filtering aggregated results like sums or counts.5. Write a SQL query to find the second highest salary in a table.
Using a subquery:
SELECT MAX(salary) FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
Or using DENSE_RANK():
SELECT salary FROM (
SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) as rnk
FROM employees) t
WHERE rnk = 2;
6. What is a JOIN? Explain different types of JOINs.
A JOIN combines rows from two or more tables based on a related column:
⦁ INNER JOIN: returns matching rows from both tables.
⦁ LEFT JOIN (LEFT OUTER JOIN): all rows from the left table, matched rows from right.
⦁ RIGHT JOIN (RIGHT OUTER JOIN): all rows from right table, matched rows from left.
⦁ FULL JOIN (FULL OUTER JOIN): all rows when there’s a match in either table.
⦁ CROSS JOIN: Cartesian product of both tables.
7. How do you optimize slow-performing SQL queries?
⦁ Use indexes appropriately to speed up lookups.
⦁ Avoid SELECT *; only select necessary columns.
⦁ Use joins carefully; filter early with WHERE clauses.
⦁ Analyze execution plans to identify bottlenecks.
⦁ Avoid unnecessary subqueries; use EXISTS or JOINs.
⦁ Limit result sets with pagination if dealing with large datasets.
8. What is a primary key? What is a foreign key?
⦁ Primary Key: A unique identifier for records in a table; it cannot be NULL.
⦁ Foreign Key: A field that creates a link between two tables by referring to the primary key in another table, enforcing referential integrity.
9. What are indexes? Explain clustered and non-clustered indexes.
⦁ Indexes speed up data retrieval by providing quick lookups.
⦁ Clustered Index: Sorts and stores the actual data rows in the table based on the key; a table can have only one clustered index.
⦁ Non-Clustered Index: Creates a separate structure that points to the data rows; tables can have multiple non-clustered indexes.
10. Write a SQL query to fetch the top 5 records from a table.
In SQL Server and PostgreSQL:
SELECT * FROM table_name
ORDER BY some_column DESC
LIMIT 5;
In SQL Server (older syntax):
SELECT TOP 5 * FROM table_name
ORDER BY some_column DESC;
React ♥️ for Part 2
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- Get hired for roles in AI, Full Stack, & more
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✅ Real-World Data Science Interview Questions & Answers 🌍📊
1️⃣ What is A/B Testing?
A method to compare two versions (A & B) to see which performs better, used in marketing, product design, and app features.
Answer: Use hypothesis testing (e.g., t-tests for means or chi-square for categories) to determine if changes are statistically significant—aim for p<0.05 and calculate sample size to detect 5-10% lifts. Example: Google tests search result layouts, boosting click-through by 15% while controlling for user segments.
2️⃣ How do Recommendation Systems work?
They suggest items based on user behavior or preferences, driving 35% of Amazon's sales and Netflix views.
Answer: Collaborative filtering (user-item interactions via matrix factorization or KNN) or content-based filtering (item attributes like tags using TF-IDF)—hybrids like ALS in Spark handle scale. Pro tip: Combat cold starts with content-based fallbacks; evaluate with NDCG for ranking quality.
3️⃣ Explain Time Series Forecasting.
Predicting future values based on past data points collected over time, like demand or stock trends.
Answer: Use models like ARIMA (for stationary series with ACF/PACF), Prophet (auto-handles seasonality and holidays), or LSTM neural networks (for non-linear patterns in Keras/PyTorch). In practice: Uber forecasts ride surges with Prophet, improving accuracy by 20% over baselines during peaks.
4️⃣ What are ethical concerns in Data Science?
Bias in data, privacy issues, transparency, and fairness—especially with AI regs like the EU AI Act in 2025.
Answer: Ensure diverse data to mitigate bias (audit with fairness libraries like AIF360), use explainable models (LIME/SHAP for black-box insights), and comply with regulations (e.g., GDPR for anonymization). Real-world: Fix COMPAS recidivism bias by balancing datasets, ensuring equitable outcomes across demographics.
5️⃣ How do you deploy an ML model?
Prepare model, containerize (Docker), create API (Flask/FastAPI), deploy on cloud (AWS, Azure).
Answer: Monitor performance with tools like Prometheus or MLflow (track drift, accuracy), retrain as needed via MLOps pipelines (e.g., Kubeflow)—use serverless like AWS Lambda for low-traffic. Example: Deploy a churn model on Azure ML; it serves 10k predictions daily with 99% uptime and auto-retrains quarterly on new data.
💬 Tap ❤️ for more!
1️⃣ What is A/B Testing?
A method to compare two versions (A & B) to see which performs better, used in marketing, product design, and app features.
Answer: Use hypothesis testing (e.g., t-tests for means or chi-square for categories) to determine if changes are statistically significant—aim for p<0.05 and calculate sample size to detect 5-10% lifts. Example: Google tests search result layouts, boosting click-through by 15% while controlling for user segments.
2️⃣ How do Recommendation Systems work?
They suggest items based on user behavior or preferences, driving 35% of Amazon's sales and Netflix views.
Answer: Collaborative filtering (user-item interactions via matrix factorization or KNN) or content-based filtering (item attributes like tags using TF-IDF)—hybrids like ALS in Spark handle scale. Pro tip: Combat cold starts with content-based fallbacks; evaluate with NDCG for ranking quality.
3️⃣ Explain Time Series Forecasting.
Predicting future values based on past data points collected over time, like demand or stock trends.
Answer: Use models like ARIMA (for stationary series with ACF/PACF), Prophet (auto-handles seasonality and holidays), or LSTM neural networks (for non-linear patterns in Keras/PyTorch). In practice: Uber forecasts ride surges with Prophet, improving accuracy by 20% over baselines during peaks.
4️⃣ What are ethical concerns in Data Science?
Bias in data, privacy issues, transparency, and fairness—especially with AI regs like the EU AI Act in 2025.
Answer: Ensure diverse data to mitigate bias (audit with fairness libraries like AIF360), use explainable models (LIME/SHAP for black-box insights), and comply with regulations (e.g., GDPR for anonymization). Real-world: Fix COMPAS recidivism bias by balancing datasets, ensuring equitable outcomes across demographics.
5️⃣ How do you deploy an ML model?
Prepare model, containerize (Docker), create API (Flask/FastAPI), deploy on cloud (AWS, Azure).
Answer: Monitor performance with tools like Prometheus or MLflow (track drift, accuracy), retrain as needed via MLOps pipelines (e.g., Kubeflow)—use serverless like AWS Lambda for low-traffic. Example: Deploy a churn model on Azure ML; it serves 10k predictions daily with 99% uptime and auto-retrains quarterly on new data.
💬 Tap ❤️ for more!
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Data Analyst Interview Questions
1. What do Tableau's sets and groups mean?
Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.
2.What in Excel is a macro?
An Excel macro is an algorithm or a group of steps that helps automate an operation by capturing and replaying the steps needed to finish it. Once the steps have been saved, you may construct a Macro that the user can alter and replay as often as they like.
Macro is excellent for routine work because it also gets rid of mistakes. Consider the scenario when an account manager needs to share reports about staff members who owe the company money. If so, it can be automated by utilising a macro and making small adjustments each month as necessary.
3.Gantt chart in Tableau
A Tableau Gantt chart illustrates the duration of events as well as the progression of value across the period. Along with the time axis, it has bars. The Gantt chart is primarily used as a project management tool, with each bar representing a project job.
4.In Microsoft Excel, how do you create a drop-down list?
Start by selecting the Data tab from the ribbon.
Select Data Validation from the Data Tools group.
Go to Settings > Allow > List next.
Choose the source you want to offer in the form of a list array.
1. What do Tableau's sets and groups mean?
Data is grouped using sets and groups according to predefined criteria. The primary distinction between the two is that although a set can have only two options—either in or out—a group can divide the dataset into several groups. A user should decide which group or sets to apply based on the conditions.
2.What in Excel is a macro?
An Excel macro is an algorithm or a group of steps that helps automate an operation by capturing and replaying the steps needed to finish it. Once the steps have been saved, you may construct a Macro that the user can alter and replay as often as they like.
Macro is excellent for routine work because it also gets rid of mistakes. Consider the scenario when an account manager needs to share reports about staff members who owe the company money. If so, it can be automated by utilising a macro and making small adjustments each month as necessary.
3.Gantt chart in Tableau
A Tableau Gantt chart illustrates the duration of events as well as the progression of value across the period. Along with the time axis, it has bars. The Gantt chart is primarily used as a project management tool, with each bar representing a project job.
4.In Microsoft Excel, how do you create a drop-down list?
Start by selecting the Data tab from the ribbon.
Select Data Validation from the Data Tools group.
Go to Settings > Allow > List next.
Choose the source you want to offer in the form of a list array.
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✅ 🚀 Power BI Interview Questions (For Analyst/BI Roles)
1️⃣ Explain DAX CALCULATE() Function
Used to modify the filter context of a measure.
✅ Example:
2️⃣ What is ALL() function in DAX?
Removes filters — useful for calculating totals regardless of filters.
3️⃣ How does FILTER() differ from CALCULATE()?
FILTER returns a table; CALCULATE modifies context using that table.
4️⃣ Difference between SUMX and SUM?
SUMX iterates over rows, applying an expression; SUM just totals a column.
5️⃣ Explain STAR vs SNOWFLAKE Schema
- Star: denormalized, simple
- Snowflake: normalized, complex relationships
6️⃣ What is a Composite Model?
Allows combining Import + DirectQuery sources in one report.
7️⃣ What are Virtual Tables in DAX?
Tables created in memory during calculation — not physical.
8️⃣ What is the difference between USERNAME() and USERPRINCIPALNAME()?
Used for dynamic RLS.
- USERNAME(): Local machine login
- USERPRINCIPALNAME(): Cloud identity (email)
9️⃣ Explain Time Intelligence Functions
Examples:
-
Used for date-based calculations.
🔟 Common DAX Optimization Tips
- Avoid complex nested functions
- Use variables (VAR)
- Reduce row context with calculated columns
1️⃣1️⃣ What is Incremental Refresh?
Only refreshes new/changed data – improves performance in large datasets.
1️⃣2️⃣ What are Parameters in Power BI?
User-defined inputs to make reports dynamic and reusable.
1️⃣3️⃣ What is a Dataflow?
Reusable ETL layer in Power BI Service using Power Query Online.
1️⃣4️⃣ Difference Between Live Connection vs DirectQuery vs Import
- Import: Fast, offline
- DirectQuery: Real-time, slower
- Live Connection: Full model lives on SSAS
1️⃣5️⃣ Advanced Visuals Use Cases
- Decomposition Tree for root cause analysis
- KPI Cards for performance metrics
- Paginated Reports for printable tables
👍 Tap for more!
1️⃣ Explain DAX CALCULATE() Function
Used to modify the filter context of a measure.
✅ Example:
CALCULATE(SUM(Sales[Amount]), Region = "West")2️⃣ What is ALL() function in DAX?
Removes filters — useful for calculating totals regardless of filters.
3️⃣ How does FILTER() differ from CALCULATE()?
FILTER returns a table; CALCULATE modifies context using that table.
4️⃣ Difference between SUMX and SUM?
SUMX iterates over rows, applying an expression; SUM just totals a column.
5️⃣ Explain STAR vs SNOWFLAKE Schema
- Star: denormalized, simple
- Snowflake: normalized, complex relationships
6️⃣ What is a Composite Model?
Allows combining Import + DirectQuery sources in one report.
7️⃣ What are Virtual Tables in DAX?
Tables created in memory during calculation — not physical.
8️⃣ What is the difference between USERNAME() and USERPRINCIPALNAME()?
Used for dynamic RLS.
- USERNAME(): Local machine login
- USERPRINCIPALNAME(): Cloud identity (email)
9️⃣ Explain Time Intelligence Functions
Examples:
-
TOTALYTD(), DATESINPERIOD(), SAMEPERIODLASTYEAR()Used for date-based calculations.
🔟 Common DAX Optimization Tips
- Avoid complex nested functions
- Use variables (VAR)
- Reduce row context with calculated columns
1️⃣1️⃣ What is Incremental Refresh?
Only refreshes new/changed data – improves performance in large datasets.
1️⃣2️⃣ What are Parameters in Power BI?
User-defined inputs to make reports dynamic and reusable.
1️⃣3️⃣ What is a Dataflow?
Reusable ETL layer in Power BI Service using Power Query Online.
1️⃣4️⃣ Difference Between Live Connection vs DirectQuery vs Import
- Import: Fast, offline
- DirectQuery: Real-time, slower
- Live Connection: Full model lives on SSAS
1️⃣5️⃣ Advanced Visuals Use Cases
- Decomposition Tree for root cause analysis
- KPI Cards for performance metrics
- Paginated Reports for printable tables
👍 Tap for more!
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Submission deadline: 5th February 2026
Grand Finale: 16th February 2026, New Delhi
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Participate in the national AI hackathon under the India AI Impact Summit 2026
Submission deadline: 5th February 2026
Grand Finale: 16th February 2026, New Delhi
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:-
https://pdlink.in/4qQfAOM
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Power BI Scenario based Questions 👇👇
📈 Scenario 1:Question: Imagine you need to visualize year-over-year growth in product sales. What approach would you take to calculate and present this information effectively in Power BI?
Answer: To visualize year-over-year growth in product sales, I would first calculate the sales for each product for the current year and the previous year using DAX measures in Power BI. Then, I would create a line chart visual where the x-axis represents the months or quarters, and the y-axis represents the sales amount. I would plot two lines on the chart, one for the current year's sales and one for the previous year's sales, allowing stakeholders to easily compare the growth trends over time.
🔄 Scenario 2: Question: You're working with a dataset that requires extensive data cleaning and transformation before analysis. Describe your process for cleaning and preparing the data in Power BI, ensuring accuracy and efficiency.
Answer: For cleaning and preparing the dataset in Power BI, I would start by identifying and addressing missing or duplicate values, outliers, and inconsistencies in data formats. I would use Power Query Editor to perform data cleaning operations such as removing null values, renaming columns, and applying transformations like data type conversion and standardization. Additionally, I would create calculated columns or measures as needed to derive new insights from the cleaned data.
🔌 Scenario 3: Question: Your organization wants to incorporate real-time data updates into their Power BI reports. How would you set up and manage live data connections in Power BI to ensure timely insights?
Answer: To incorporate real-time data updates into Power BI reports, I would utilize Power BI's streaming datasets feature. I would set up a data streaming connection to the source system, such as a database or API, and configure the dataset to receive real-time data updates at specified intervals. Then, I would design reports and visuals based on the streaming dataset, enabling stakeholders to view and analyze the latest data as it is updated in real-time.
⚡ Scenario 4: Question: You've noticed that your Power BI reports are taking longer to load and refresh than usual. How would you diagnose and address performance issues to optimize report performance?
Answer: If Power BI reports are experiencing performance issues, I would first identify potential bottlenecks by analyzing factors such as data volume, query complexity, and visual design. Then, I would optimize report performance by applying techniques such as data model optimization, query optimization, and visualization best practices.
📈 Scenario 1:Question: Imagine you need to visualize year-over-year growth in product sales. What approach would you take to calculate and present this information effectively in Power BI?
Answer: To visualize year-over-year growth in product sales, I would first calculate the sales for each product for the current year and the previous year using DAX measures in Power BI. Then, I would create a line chart visual where the x-axis represents the months or quarters, and the y-axis represents the sales amount. I would plot two lines on the chart, one for the current year's sales and one for the previous year's sales, allowing stakeholders to easily compare the growth trends over time.
🔄 Scenario 2: Question: You're working with a dataset that requires extensive data cleaning and transformation before analysis. Describe your process for cleaning and preparing the data in Power BI, ensuring accuracy and efficiency.
Answer: For cleaning and preparing the dataset in Power BI, I would start by identifying and addressing missing or duplicate values, outliers, and inconsistencies in data formats. I would use Power Query Editor to perform data cleaning operations such as removing null values, renaming columns, and applying transformations like data type conversion and standardization. Additionally, I would create calculated columns or measures as needed to derive new insights from the cleaned data.
🔌 Scenario 3: Question: Your organization wants to incorporate real-time data updates into their Power BI reports. How would you set up and manage live data connections in Power BI to ensure timely insights?
Answer: To incorporate real-time data updates into Power BI reports, I would utilize Power BI's streaming datasets feature. I would set up a data streaming connection to the source system, such as a database or API, and configure the dataset to receive real-time data updates at specified intervals. Then, I would design reports and visuals based on the streaming dataset, enabling stakeholders to view and analyze the latest data as it is updated in real-time.
⚡ Scenario 4: Question: You've noticed that your Power BI reports are taking longer to load and refresh than usual. How would you diagnose and address performance issues to optimize report performance?
Answer: If Power BI reports are experiencing performance issues, I would first identify potential bottlenecks by analyzing factors such as data volume, query complexity, and visual design. Then, I would optimize report performance by applying techniques such as data model optimization, query optimization, and visualization best practices.
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🐼 Pandas Interview Question (Data Analyst)
Q. How do you find missing values in a Pandas DataFrame and count them column-wise?
✅ Answer
df.isna().sum()
Explanation:
isna() / isnull() detects missing values
sum() gives the count for each column
💡 Pro tip:
Total missing values in the DataFrame:
df.isna().sum().sum()
👍 React to this post if you want more daily interview questions on Pandas, SQL & Data Analytics. 🚀
Q. How do you find missing values in a Pandas DataFrame and count them column-wise?
✅ Answer
df.isna().sum()
Explanation:
isna() / isnull() detects missing values
sum() gives the count for each column
💡 Pro tip:
Total missing values in the DataFrame:
df.isna().sum().sum()
👍 React to this post if you want more daily interview questions on Pandas, SQL & Data Analytics. 🚀
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📊 Pandas Interview Question (Frequently Asked!)
❓ Interviewers love to ask this:
“Your dataset has duplicate records. How will you handle them in Pandas?”
✅ Answer:
➡️ Use df.duplicated() to identify duplicate rows.
➡️ Use df.drop_duplicates() to remove them cleanly.
➡️ You can also target specific columns using the subset parameter.
👍 React if you want more frequently asked Pandas, SQL, PowerBI interview questions for Data Analyst roles!
❓ Interviewers love to ask this:
“Your dataset has duplicate records. How will you handle them in Pandas?”
✅ Answer:
➡️ Use df.duplicated() to identify duplicate rows.
➡️ Use df.drop_duplicates() to remove them cleanly.
➡️ You can also target specific columns using the subset parameter.
👍 React if you want more frequently asked Pandas, SQL, PowerBI interview questions for Data Analyst roles!
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1. Danny’s Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/
2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/
3. Foodie Fie
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Analytics based on customer activities with the digital bank
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Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf
6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG
7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7
8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8
Join for more: https://news.1rj.ru/str/sqlanalyst
1. Danny’s Diner:
Restaurant analytics to understand the customer orders pattern.
Link: https://8weeksqlchallenge.com/case-study-1/
2. Pizza Runner
Pizza shop analytics to optimize the efficiency of the operation
Link: https://8weeksqlchallenge.com/case-study-2/
3. Foodie Fie
Subnoscription-based food content platform
Link: https://lnkd.in/gzB39qAT
4. Data Bank: That’s money
Analytics based on customer activities with the digital bank
Link: https://lnkd.in/gH8pKPyv
5. Data Mart: Fresh is Best
Analytics on Online supermarket
Link: https://lnkd.in/gC5bkcDf
6. Clique Bait: Attention capturing
Analytics on the seafood industry
Link: https://lnkd.in/ggP4JiYG
7. Balanced Tree: Clothing Company
Analytics on the sales performance of clothing store
Link: https://8weeksqlchallenge.com/case-study-7
8. Fresh segments: Extract maximum value
Analytics on online advertising
Link: https://8weeksqlchallenge.com/case-study-8
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📊 Pandas Interview Question (Frequently Asked!)
❓ Interviewers love to ask this:
“Your dataset has duplicate records. How will you handle them in Pandas?”
✅ Answer:
➡️ Use df.duplicated() to identify duplicate rows.
➡️ Use df.drop_duplicates() to remove them cleanly.
➡️ You can also target specific columns using the subset parameter.
👍 React if you want more frequently asked Pandas, SQL, PowerBI interview questions for Data Analyst roles!
❓ Interviewers love to ask this:
“Your dataset has duplicate records. How will you handle them in Pandas?”
✅ Answer:
➡️ Use df.duplicated() to identify duplicate rows.
➡️ Use df.drop_duplicates() to remove them cleanly.
➡️ You can also target specific columns using the subset parameter.
👍 React if you want more frequently asked Pandas, SQL, PowerBI interview questions for Data Analyst roles!
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📌 SQL Interview Question (Must-Know)
Question:
You have a table orders with the following columns:
order_id, customer_id, order_date, order_amount
👉 Write an SQL query to find the total order amount for each customer who has placed more than 3 orders.
✅ Solution:
SELECT
customer_id,
SUM(order_amount) AS total_order_amount
FROM orders
GROUP BY customer_id
HAVING COUNT(order_id) > 3;
🧠 Explanation:
GROUP BY customer_id → groups orders per customer
SUM(order_amount) → calculates total spending
HAVING COUNT(order_id) > 3 → filters customers with more than 3 orders
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Question:
You have a table orders with the following columns:
order_id, customer_id, order_date, order_amount
👉 Write an SQL query to find the total order amount for each customer who has placed more than 3 orders.
✅ Solution:
SELECT
customer_id,
SUM(order_amount) AS total_order_amount
FROM orders
GROUP BY customer_id
HAVING COUNT(order_id) > 3;
🧠 Explanation:
GROUP BY customer_id → groups orders per customer
SUM(order_amount) → calculates total spending
HAVING COUNT(order_id) > 3 → filters customers with more than 3 orders
👍 React with 🔥 or 👍 if this helped
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✅ Top 10 Excel Interview Questions & Answers 📊💼
1️⃣ What is Excel and why is it used?
Excel is a spreadsheet program used for organizing, analyzing, and storing data in tabular form. It's widely used for data analysis, reporting, and financial modeling.
2️⃣ Key Excel components?
- Ribbon: Main menu
- Worksheet: A single sheet
- Workbook: A collection of worksheets
- Cell: Intersection of a row and column
3️⃣ What are Excel Functions?
Predefined formulas that perform specific calculations (e.g., SUM, AVERAGE, IF, VLOOKUP).
4️⃣ VLOOKUP vs. INDEX/MATCH?
- VLOOKUP: Searches for a value in the first column and returns a corresponding value.
- INDEX/MATCH: More flexible and overcomes VLOOKUP limitations, better for larger datasets.
5️⃣ What are Pivot Tables?
Interactive tables that summarize and analyze large datasets, allowing you to easily rearrange and filter data.
6️⃣ Conditional Formatting?
Applies formatting (e.g., colors, icons) to cells based on specific criteria, making it easier to identify trends and outliers.
7️⃣ How to remove duplicates?
Use the "Remove Duplicates" feature in the Data tab to eliminate redundant rows based on selected columns.
8️⃣ What are Excel Charts?
Visual representations of data (e.g., bar charts, line charts, pie charts) that help communicate trends and insights.
9️⃣ How to protect a worksheet?
Use the "Protect Sheet" feature in the Review tab to prevent unauthorized changes to the worksheet structure and content.
🔟 What are Macros?
Automated sequences of commands that can be recorded and replayed to perform repetitive tasks efficiently.
👍 React ❤️ if you found this helpful!
1️⃣ What is Excel and why is it used?
Excel is a spreadsheet program used for organizing, analyzing, and storing data in tabular form. It's widely used for data analysis, reporting, and financial modeling.
2️⃣ Key Excel components?
- Ribbon: Main menu
- Worksheet: A single sheet
- Workbook: A collection of worksheets
- Cell: Intersection of a row and column
3️⃣ What are Excel Functions?
Predefined formulas that perform specific calculations (e.g., SUM, AVERAGE, IF, VLOOKUP).
4️⃣ VLOOKUP vs. INDEX/MATCH?
- VLOOKUP: Searches for a value in the first column and returns a corresponding value.
- INDEX/MATCH: More flexible and overcomes VLOOKUP limitations, better for larger datasets.
5️⃣ What are Pivot Tables?
Interactive tables that summarize and analyze large datasets, allowing you to easily rearrange and filter data.
6️⃣ Conditional Formatting?
Applies formatting (e.g., colors, icons) to cells based on specific criteria, making it easier to identify trends and outliers.
7️⃣ How to remove duplicates?
Use the "Remove Duplicates" feature in the Data tab to eliminate redundant rows based on selected columns.
8️⃣ What are Excel Charts?
Visual representations of data (e.g., bar charts, line charts, pie charts) that help communicate trends and insights.
9️⃣ How to protect a worksheet?
Use the "Protect Sheet" feature in the Review tab to prevent unauthorized changes to the worksheet structure and content.
🔟 What are Macros?
Automated sequences of commands that can be recorded and replayed to perform repetitive tasks efficiently.
👍 React ❤️ if you found this helpful!
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📈 Want to Excel at Data Analytics? Master These Essential Skills! ☑️
Core Concepts:
• Statistics & Probability – Understand distributions, hypothesis testing
• Excel – Pivot tables, formulas, dashboards
Programming:
• Python – NumPy, Pandas, Matplotlib, Seaborn
• R – Data analysis & visualization
• SQL – Joins, filtering, aggregation
Data Cleaning & Wrangling:
• Handle missing values, duplicates
• Normalize and transform data
Visualization:
• Power BI, Tableau – Dashboards
• Plotly, Seaborn – Python visualizations
• Data Storytelling – Present insights clearly
Advanced Analytics:
• Regression, Classification, Clustering
• Time Series Forecasting
• A/B Testing & Hypothesis Testing
ETL & Automation:
• Web Scraping – BeautifulSoup, Scrapy
• APIs – Fetch and process real-world data
• Build ETL Pipelines
Tools & Deployment:
• Jupyter Notebook / Colab
• Git & GitHub
• Cloud Platforms – AWS, GCP, Azure
• Google BigQuery, Snowflake
Hope it helps :)
Core Concepts:
• Statistics & Probability – Understand distributions, hypothesis testing
• Excel – Pivot tables, formulas, dashboards
Programming:
• Python – NumPy, Pandas, Matplotlib, Seaborn
• R – Data analysis & visualization
• SQL – Joins, filtering, aggregation
Data Cleaning & Wrangling:
• Handle missing values, duplicates
• Normalize and transform data
Visualization:
• Power BI, Tableau – Dashboards
• Plotly, Seaborn – Python visualizations
• Data Storytelling – Present insights clearly
Advanced Analytics:
• Regression, Classification, Clustering
• Time Series Forecasting
• A/B Testing & Hypothesis Testing
ETL & Automation:
• Web Scraping – BeautifulSoup, Scrapy
• APIs – Fetch and process real-world data
• Build ETL Pipelines
Tools & Deployment:
• Jupyter Notebook / Colab
• Git & GitHub
• Cloud Platforms – AWS, GCP, Azure
• Google BigQuery, Snowflake
Hope it helps :)
❤2