SQL Interview Questions which can be asked in a Data Analyst Interview.

1️⃣ What is difference between Primary key and Unique key?

◼Primary key- A column or set of columns which uniquely identifies each record in a table. It can't contain null values and only one primary key
can exist in a table.

◼Unique key-Similar to primary key it also uniquely identifies each record in a table and can contain null values.Multiple Unique key can exist in a table.

2️⃣ What is a Candidate key?

◼A key or set of keys that uniquely identifies each record in a table.It is a combination of Primary and Alternate key.

3️⃣ What is a Constraint?

◼Specific rule or limit that we define in our table. E.g - NOT NULL,AUTO INCREMENT

4️⃣ Can you differentiate between TRUNCATE and DELETE?

◼TRUNCATE is a DDL command. It deletes the entire data from a table but preserves the structure of table.It doesn't deletes the data row by row hence faster than DELETE command, while DELETE is a DML command and it deletes the entire data based on specified condition else deletes the entire data,also it deletes the data row by row hence slower than TRUNCATE command.

5️⃣ What is difference between 'View' and 'Stored Procedure'?

◼A View is a virtual table that gets data from the base table .It is basically a Select statement,while Stored Procedure is a sql statement or set of sql statement stored on database server.

6️⃣ What is difference between a Common Table Expression and temporary table?

◼CTE is a temporary result set that is defined within execution scope of a single SELECT ,DELETE,UPDATE statement while temporary table is stored in TempDB and gets deleted once the session expires.

7️⃣ Differentiate between a clustered index and a non-clustered index?

◼ A clustered index determines physical ordering of data in a table and a table can have only one clustered index while a non-clustered index is analogous to index of a book where index is stored at one place and data at other place and index will have pointers to storage location of the data,a table can have more than one non-clustered index.

8️⃣ Explain triggers ?

◼They are sql codes which are automatically executed in response to certain events on a table.They are used to maintain integrity of data.

Recent Interview Question for Data Analyst Role

Question 1) You have two tables:

Employee:-
Columns: EID (Employee ID), ESalary (Employee Salary)

empdetails:-
Columns: EID (Employee ID), EDOB (Employee Date of Birth)

Your task is to:
1) Identify all employees whose salary (ESalary) is an odd number?
2) Retrieve the date of birth (EDOB) for these employees from the empdetails table.

How would you write a SQL query to achieve this?

SELECT e.EID, ed.EDOB
FROM (
SELECT EID
FROM Employee
WHERE ESalary % 2 <> 0
) e
JOIN empdetails ed ON e.EID = ed.EID;

Explanation of the query :-

Filter Employees with Odd Salaries:

The subquery SELECT EID FROM Employee WHERE ESalary % 2 <> 0 filters out Employee IDs (EID) where the salary (ESalary) is an odd number. The modulo operator % checks if ESalary divided by 2 leaves a remainder (<>0).

Merge with empdetails:

The main query then takes the filtered Employee IDs from the subquery and performs a join with the empdetails table using the EID column. This retrieves the date of birth (EDOB) for these employees.

Hope this helps you 😊

Data Analyst Interview Questions with Answers 👇👇


Self-Introduction (2-3 minutes)


"Hello, my name is Rahul Sharma, and I'm excited to be here today. With a degree in Computer Science, I've developed strong analytical skills and a passion for data analysis. Over the past 2-3 years, I've worked as a Data Analyst, primarily focusing on data visualization, SQL development, and business intelligence. My expertise includes SQL Server, Power BI, and data modeling."


Explain Your Last Project (5-7 minutes)


"In my previous role at ABC Corporation, I worked on a project to analyze customer purchasing behavior. The goal was to identify trends and preferences, informing marketing strategies.


"My responsibilities included:


•⁠ ⁠Data extraction from SQL Server
•⁠ ⁠Data visualization using Power BI
•⁠ ⁠Data modeling and normalization
•⁠ ⁠Stakeholder communication


"Some challenges I faced included:


•⁠ ⁠Handling large datasets
•⁠ ⁠Ensuring data quality and accuracy
•⁠ ⁠Meeting tight deadlines


"To overcome these challenges, I:


•⁠ ⁠Optimized SQL queries for faster data retrieval
•⁠ ⁠Implemented data validation checks
•⁠ ⁠Collaborated closely with stakeholders"


Challenges You Faced (3-5 minutes)


"Two significant challenges I faced were:


1.⁠ ⁠Data quality issues due to inconsistent formatting.


Resolution: I developed a data cleaning noscript using SQL and implemented data validation checks.


1.⁠ ⁠Performance issues with Power BI reports.


Resolution: I optimized data models, reduced data redundancy, and leveraged Power BI's built-in performance optimization features."


Your Roles and Responsibilities (3-5 minutes)


"As a Data Analyst at ABC Corporation, my primary responsibilities included:


•⁠ ⁠Data extraction and analysis
•⁠ ⁠Data visualization and reporting
•⁠ ⁠Stakeholder communication and presentation
•⁠ ⁠Data modeling and normalization


"I worked closely with cross-functional teams to ensure data-driven insights informed business decisions."


2 Issues You Got Stuck and How You Resolved (5-7 minutes)


"Two issues I got stuck on were:


1.⁠ ⁠Optimizing a slow-running SQL query.


Resolution: I analyzed the query execution plan, applied indexing strategies, and rewrote the query to reduce join operations.


1.⁠ ⁠Troubleshooting Power BI visualization issues.


Resolution: I adjusted data model settings, validated data integrity, and leveraged Power BI's community forums for support."


How Did You Do Optimization (3-5 minutes)


"To optimize query performance:


•⁠ ⁠I analyzed query execution plans
•⁠ ⁠Applied indexing strategies
•⁠ ⁠Rewrote queries to reduce join operations
•⁠ ⁠Utilized data caching

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5⃣ frequently Asked SQL Interview Questions with Answers in data analyst interviews

📍1. Write a SQL query to find the average purchase amount for each customer. Assume you have two tables: Customers (CustomerID, Name) and Orders (OrderID, CustomerID, Amount).

SELECT c.CustomerID, c. Name, AVG(o.Amount) AS AveragePurchase
FROM Customers c
JOIN Orders o ON c.CustomerID = o.CustomerID
GROUP BY c.CustomerID, c. Name;

📍2. Write a query to find the employee with the minimum salary in each department from a table Employees with columns EmployeeID, Name, DepartmentID, and Salary.

SELECT e1.DepartmentID, e1.EmployeeID, e1 .Name, e1.Salary
FROM Employees e1
WHERE Salary = (SELECT MIN(Salary) FROM Employees e2 WHERE e2.DepartmentID = e1.DepartmentID);

📍3. Write a SQL query to find all products that have never been sold. Assume you have a table Products (ProductID, ProductName) and a table Sales (SaleID, ProductID, Quantity).

SELECT p.ProductID, p.ProductName
FROM Products p
LEFT JOIN Sales s ON p.ProductID = s.ProductID
WHERE s.ProductID IS NULL;

📍4. Given a table Orders with columns OrderID, CustomerID, OrderDate, and a table OrderItems with columns OrderID, ItemID, Quantity, write a query to find the customer with the highest total order quantity.

SELECT o.CustomerID, SUM(oi.Quantity) AS TotalQuantity
FROM Orders o
JOIN OrderItems oi ON o.OrderID = oi.OrderID
GROUP BY o.CustomerID
ORDER BY TotalQuantity DESC
LIMIT 1

;

📍5. Write a SQL query to find the earliest order date for each customer from a table Orders (OrderID, CustomerID, OrderDate).

SELECT CustomerID, MIN(OrderDate) AS EarliestOrderDate
FROM Orders
GROUP BY CustomerID

Hope it helps :)

SQL table interview questions:

1. What is a DUAL table and why do we need it?
- it is a special table which gets created automatically when we install Oracle database. It can be used to select pseudo columns, perform calculations and also as sequence generator etc.

2. How many columns and rows are present in DUAL table?
- one column & one row by default.

3. Can we insert more rows in to DUAL table?
- Yes.

4. What's the easiest wah to backup a table / how can we create a table based on existing table?
- CREATE TABLE SALES_COPY AS SELECT * FROM SALES.

5. Can we drop all the columns from a table?
- No.

6. What is the difference between count(1) and count(*)?
- Both are same. Both consume same amount of resources, Both perform same operation

Most Important Python Topics for Data Analyst Interview:

#Basics of Python:

1. Data Types

2. Lists

3. Dictionaries

4. Control Structures:

- if-elif-else

- Loops

5. Functions

6. Practice basic FAQs questions, below mentioned are few examples:

- How to reverse a string in Python?

- How to find the largest/smallest number in a list?

- How to remove duplicates from a list?

- How to count the occurrences of each element in a list?

- How to check if a string is a palindrome?

#Pandas:

1. Pandas Data Structures (Series, DataFrame)

2. Creating and Manipulating DataFrames

3. Filtering and Selecting Data

4. Grouping and Aggregating Data

5. Handling Missing Values

6. Merging and Joining DataFrames

7. Adding and Removing Columns

8. Exploratory Data Analysis (EDA):

- Denoscriptive Statistics

- Data Visualization with Pandas (Line Plots, Bar Plots, Histograms)

- Correlation and Covariance

- Handling Duplicates

- Data Transformation

#Numpy:

1. NumPy Arrays

2. Array Operations:

- Creating Arrays

- Slicing and Indexing

- Arithmetic Operations

Integration with Other Libraries:

1. Basic Data Visualization with Pandas (Line Plots, Bar Plots)

Key Concepts to Revise:

1. Data Manipulation with Pandas and NumPy

2. Data Cleaning Techniques

3. File Handling (reading and writing CSV files, JSON files)

4. Handling Missing and Duplicate Values

5. Data Transformation (scaling, normalization)

6. Data Aggregation and Group Operations

7. Combining and Merging Datasets

Hope this helps you 😊

Preparing for an online data analyst interview? Here’s a complete guide to ensure you’re ready to impress:

1. Mental Preparation
Visualize Success: Imagine yourself confidently answering questions and solving problems.
Stay Calm: Practice relaxation techniques like deep breathing or meditation to manage interview stress.
Set Clear Goals: Define what you aim to achieve and focus on showcasing your strengths.

2. Technical Setup
Check Your Equipment: Test your computer, camera, microphone, and internet connection to avoid technical glitches.
Platform Familiarity: Familiarize yourself with the video conferencing tool (Zoom, Teams, etc.) and ensure it’s updated.
Professional Background: Choose a clean, well-lit space or use a virtual background if necessary.

3. Environment
Quiet Space: Select a quiet room free from interruptions and let others know about your interview schedule.
Lighting and Camera: Position your camera at eye level and ensure you’re well-lit from the front to avoid shadows.

4. Interview Preparation
Review Key Concepts: Brush up on SQL, data manipulation, and visualization tools relevant to the role.
Practice with Online Tools: Get comfortable with online whiteboards or screen-sharing features if they’ll be used.
Prepare Your Questions: Develop insightful questions about the role, team, and company.

5. Day Before the Interview
Test Your Setup: Conduct a trial run with a friend or family member to ensure everything works smoothly.
Organize Documents: Have your resume, cover letter, and any required documents easily accessible on your computer.
Dress Professionally: Choose professional attire to set the right tone and boost your confidence.

6. Interview Day
Log in Early: Join the meeting a few minutes early to resolve any last-minute issues and show punctuality.
Engage Actively: Maintain eye contact by looking at the camera, and engage thoughtfully with the interviewer.

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5 frequently Asked SQL Interview Questions with Answers in Data Engineering interviews:
𝐃𝐢𝐟𝐟𝐢𝐜𝐮𝐥𝐭𝐲 - 𝐌𝐞𝐝𝐢𝐮𝐦

⚫️Determine the Top 5 Products with the Highest Revenue in Each Category.
Schema: Products (ProductID, Name, CategoryID), Sales (SaleID, ProductID, Amount)

WITH ProductRevenue AS (
SELECT p.ProductID,
p.Name,
p.CategoryID,
SUM(s.Amount) AS TotalRevenue,
RANK() OVER (PARTITION BY p.CategoryID ORDER BY SUM(s.Amount) DESC) AS RevenueRank
FROM Products p
JOIN Sales s ON p.ProductID = s.ProductID
GROUP BY p.ProductID, p.Name, p.CategoryID
)
SELECT ProductID, Name, CategoryID, TotalRevenue
FROM ProductRevenue
WHERE RevenueRank <= 5;

⚫️ Identify Employees with Increasing Sales for Four Consecutive Quarters.
Schema: Sales (EmployeeID, SaleDate, Amount)

WITH QuarterlySales AS (
SELECT EmployeeID,
DATE_TRUNC('quarter', SaleDate) AS Quarter,
SUM(Amount) AS QuarterlyAmount
FROM Sales
GROUP BY EmployeeID, DATE_TRUNC('quarter', SaleDate)
),
SalesTrend AS (
SELECT EmployeeID,
Quarter,
QuarterlyAmount,
LAG(QuarterlyAmount, 1) OVER (PARTITION BY EmployeeID ORDER BY Quarter) AS PrevQuarter1,
LAG(QuarterlyAmount, 2) OVER (PARTITION BY EmployeeID ORDER BY Quarter) AS PrevQuarter2,
LAG(QuarterlyAmount, 3) OVER (PARTITION BY EmployeeID ORDER BY Quarter) AS PrevQuarter3
FROM QuarterlySales
)
SELECT EmployeeID, Quarter, QuarterlyAmount
FROM SalesTrend
WHERE QuarterlyAmount > PrevQuarter1 AND PrevQuarter1 > PrevQuarter2 AND PrevQuarter2 > PrevQuarter3;

⚫️ List Customers Who Made Purchases in Each of the Last Three Years.
Schema: Orders (OrderID, CustomerID, OrderDate)

WITH YearlyOrders AS (
SELECT CustomerID,
EXTRACT(YEAR FROM OrderDate) AS OrderYear
FROM Orders
GROUP BY CustomerID, EXTRACT(YEAR FROM OrderDate)
),
RecentYears AS (
SELECT DISTINCT OrderYear
FROM Orders
WHERE OrderDate >= CURRENT_DATE - INTERVAL '3 years'
),
CustomerYearlyOrders AS (
SELECT CustomerID,
COUNT(DISTINCT OrderYear) AS YearCount
FROM YearlyOrders
WHERE OrderYear IN (SELECT OrderYear FROM RecentYears)
GROUP BY CustomerID
)
SELECT CustomerID
FROM CustomerYearlyOrders
WHERE YearCount = 3;


⚫️ Find the Third Lowest Price for Each Product Category.
Schema: Products (ProductID, Name, CategoryID, Price)

WITH RankedPrices AS (
SELECT CategoryID,
Price,
DENSE_RANK() OVER (PARTITION BY CategoryID ORDER BY Price ASC) AS PriceRank
FROM Products
)
SELECT CategoryID, Price
FROM RankedPrices
WHERE PriceRank = 3;

⚫️ Identify Products with Total Sales Exceeding a Specified Threshold Over the Last 30 Days.
Schema: Sales (SaleID, ProductID, SaleDate, Amount)

WITH RecentSales AS (
SELECT ProductID,
SUM(Amount) AS TotalSales
FROM Sales
WHERE SaleDate >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY ProductID
)
SELECT ProductID, TotalSales
FROM RecentSales
WHERE TotalSales > 200;

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Almost everyone knows that these are the tools a Data Analyst works with:

➡️ SQL
➡️ Excel
➡️ Power BI/Tableau
➡️ Python

But people getting started with analytics are confused about the preferences of picking these tools.

There are various kinds of data analytics roles available in the market :

➡️ BI + SQL: Will primarily be involved in BI development.

➡️ SQL + Excel: Will primarily work on Excel reporting.

➡️ SQL + Python: Will primarily do data analysis using python.

Now, If you are getting started with learning analytics, choose any one role that interests you the most and focus on completing the primary tools that the role requires. Learn them VERY WELL.

Learn any of the above combinations that interests you first and then start looking out for opportunities which ask for these primary tools and simultaneously start learning the basics of the 3rd tool.

You don't have to focus on being good with each and every tool but being good with any of the above combinations always works.

Hope this helps you 😊

Most Asked SQL Interview Questions at MAANG Companies🔥🔥

Preparing for an SQL Interview at MAANG Companies? Here are some crucial SQL Questions you should be ready to tackle:

1. How do you retrieve all columns from a table?

SELECT * FROM table_name;

2. What SQL statement is used to filter records?

SELECT * FROM table_name
WHERE condition;

The WHERE clause is used to filter records based on a specified condition.

3. How can you join multiple tables? Describe different types of JOINs.

SELECT columns
FROM table1
JOIN table2 ON table1.column = table2.column
JOIN table3 ON table2.column = table3.column;

Types of JOINs:

1. INNER JOIN: Returns records with matching values in both tables

SELECT * FROM table1
INNER JOIN table2 ON table1.column = table2.column;

2. LEFT JOIN: Returns all records from the left table & matched records from the right table. Unmatched records will have NULL values.

SELECT * FROM table1
LEFT JOIN table2 ON table1.column = table2.column;

3. RIGHT JOIN: Returns all records from the right table & matched records from the left table. Unmatched records will have NULL values.

SELECT * FROM table1
RIGHT JOIN table2 ON table1.column = table2.column;

4. FULL JOIN: Returns records when there is a match in either left or right table. Unmatched records will have NULL values.

SELECT * FROM table1
FULL JOIN table2 ON table1.column = table2.column;

4. What is the difference between WHERE & HAVING clauses?

WHERE: Filters records before any groupings are made.

SELECT * FROM table_name
WHERE condition;

HAVING: Filters records after groupings are made.

SELECT column, COUNT(*)
FROM table_name
GROUP BY column
HAVING COUNT(*) > value;

5. How do you calculate average, sum, minimum & maximum values in a column?

Average: SELECT AVG(column_name) FROM table_name;

Sum: SELECT SUM(column_name) FROM table_name;

Minimum: SELECT MIN(column_name) FROM table_name;

Maximum: SELECT MAX(column_name) FROM table_name;

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Must Study: Key Questions for Data Analysts 4.0

Advanced SQL
1. How do you handle hierarchical data and perform recursive queries in SQL?
2. What are common techniques for SQL performance tuning beyond indexing?
3. How do you implement SQL transactions and ensure atomicity in complex queries?

Excel Advanced
1. How do you use Power Pivot to manage and analyze large datasets in Excel?
2. What are the best practices for creating and using Excel macros for automation?
3. How do you leverage Excel’s advanced charting tools for dynamic data visualization?

Power BI
1. How do you use Power Query to merge and transform data from multiple sources?
2. What are the key differences between calculated columns and measures in Power BI?
3. How do you design effective Power BI dashboards for executive reporting?

Python
1. How do you use Python’s pandas library for advanced data manipulation and analysis?
2. What are the best practices for deploying machine learning models using Python?
3. How do you perform time series analysis and forecasting with Python?

Data Visualization
1. How do you ensure your visualizations are accessible to people with visual impairments?
2. What are effective methods for visualizing multivariate data?
3. How do you use storytelling techniques to make your data visualizations more engaging?

Soft Skills
1. How do you handle conflicts and disagreements within a data team or with stakeholders?
2. What strategies do you use to effectively present complex data insights to a broad audience?
3. How do you stay updated with the latest trends and tools in data analytics?

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Final Preparation Guide for Data Analytics Interviews: (IMP)

➡Key SQL Concepts:
- Master SELECT statements, focusing on WHERE, ORDER BY, GROUP BY, and HAVING clauses.
- Understand the basics of JOINS: INNER, LEFT, RIGHT, FULL.
- Get comfortable with aggregate functions like COUNT, SUM, AVG, MAX, and MIN.
- Study subqueries and Common Table Expressions.
- Explore advanced topics like CASE statements, complex JOIN strategies, and Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK).

➡Python for Data Analysis:
- Review the basics of Python syntax, control structures, and data structures (lists, dictionaries).
- Dive into data manipulation using Pandas and NumPy, covering DataFrames, Series, and group by operations.
- Learn basic plotting techniques with Matplotlib and Seaborn for data visualization.

➡ Excel Skills:
- Practice cell operations and essential formulas like SUMIFS, COUNTIFS, and AVERAGEIFS.
- Familiarize yourself with PivotTables, PivotCharts, data validation, and What-if analysis.
- Explore advanced formulas and work with the Data Model & Power Pivot.

➡ Power BI Proficiency:
- Focus on data modeling, including importing data and managing relationships.
- Learn data transformation techniques with Power Query and use DAX for calculated columns and measures.
- Create interactive reports and dashboards, and work on visualizations.

➡ Basic Statistics:
- Understand fundamental concepts like Mean, Median, Mode, Standard Deviation, and Variance.
- Study probability distributions, Hypothesis Testing, and P-values.
- Learn about Confidence Intervals, Correlation, and Simple Linear Regression.

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DATA ANALYST Interview Questions (0-3 yr) (SQL, Power BI)

👉 Power BI:

Q1: Explain step-by-step how you will create a sales dashboard from scratch.

Q2: Explain how you can optimize a slow Power BI report.

Q3: Explain Any 5 Chart Types and Their Uses in Representing Different Aspects of Data.

👉SQL:

Q1: Explain the difference between RANK(), DENSE_RANK(), and ROW_NUMBER() functions using example.

Q2 – Q4 use Table: employee (EmpID, ManagerID, JoinDate, Dept, Salary)

Q2: Find the nth highest salary from the Employee table.

Q3: You have an employee table with employee ID and manager ID. Find all employees under a specific manager, including their subordinates at any level.

Q4: Write a query to find the cumulative salary of employees department-wise, who have joined the company in the last 30 days.

Q5: Find the top 2 customers with the highest order amount for each product category, handling ties appropriately. Table: Customer (CustomerID, ProductCategory, OrderAmount)

👉Behavioral:

Q1: Why do you want to become a data analyst and why did you apply to this company?

Q2: Describe a time when you had to manage a difficult task with tight deadlines. How did you handle it?

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Excel interview questions for both data analysts and business analysts

1) What are the basic functions of Microsoft Excel?
2) Explain the difference between a workbook and a worksheet.
3) How would you freeze panes in Excel?
4) Can you name some common keyboard shortcuts in Excel?
5) What is the purpose of VLOOKUP and HLOOKUP?
7) How do you remove duplicate values in Excel?
8) Explain the steps to filter data in Excel.
9) What is the significance of the "IF" function in Excel, and can you provide an example of its use?
10) How would you create a pivot table in Excel?
11) Explain the use of the CONCATENATE function in Excel.
12) How do you create a chart in Excel?
13) Explain the difference between a line chart and a scatter plot.
14) What is conditional formatting, and how can it be applied in Excel?
15) How would you create a dynamic chart that updates with new data?
16) What is the INDEX-MATCH function, and how is it different from VLOOKUP?
17) Can you explain the concept of "PivotTables" and when you would use them?
18) How do you use the "COUNTIF" and "SUMIF" functions in Excel?
19) Explain the purpose of the "What-If Analysis" tools in Excel.
20) What are array formulas, and can you provide an example of their use?

Business Analysis Specific:

1) How would you analyze a set of sales data to identify trends and insights?
2) Explain how you might use Excel to perform financial modeling.
3) What Excel features would you use for forecasting and budgeting?
4) How do you handle large datasets in Excel, and what tools or techniques do you use for optimization?
5) What are some common techniques for cleaning and validating data in Excel?
6) How do you identify and handle errors in a dataset using Excel?

Scenario-based Questions:

1) Imagine you have a dataset with missing values. How would you approach this problem in Excel?
2) You are given a dataset with multiple sheets. How would you consolidate the data for analysis?

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🚨Here is a comprehensive list of #interview questions that are commonly asked in job interviews for Data Scientist, Data Analyst, and Data Engineer positions:


➡️ Data Scientist Interview Questions


Technical Questions

1) What are your preferred programming languages for data science, and why?

2) Can you write a Python noscript to perform data cleaning on a given dataset?

3) Explain the Central Limit Theorem.

4) How do you handle missing data in a dataset?

5) Describe the difference between supervised and unsupervised learning.

6) How do you select the right algorithm for your model?


Questions Related To Problem-Solving and Projects

7) Walk me through a data science project you have worked on.

8) How did you handle data preprocessing in your project?

9) How do you evaluate the performance of a machine learning model?

10) What techniques do you use to prevent overfitting?


➡️Data Analyst Interview Questions


Technical Questions

1) Write a SQL query to find the second highest salary from the employee table.

2) How would you optimize a slow-running query?

3) How do you use pivot tables in Excel?

4) Explain the VLOOKUP function.

5) How do you handle outliers in your data?

6) Describe the steps you take to clean a dataset.


Analytical Questions

7) How do you interpret data to make business decisions?

8) Give an example of a time when your analysis directly influenced a business decision.

9) What are your preferred tools for data analysis and why?

10) How do you ensure the accuracy of your analysis?


➡️Data Engineer Interview Questions


Technical Questions

1) What is your experience with SQL and NoSQL databases?

2) How do you design a scalable database architecture?

3) Explain the ETL process you follow in your projects.

4) How do you handle data transformation and loading efficiently?

5) What is your experience with Hadoop/Spark?

6) How do you manage and process large datasets?


Questions Related To Problem-Solving and Optimization

7) Describe a data pipeline you have built.

8) What challenges did you face, and how did you overcome them?

9) How do you ensure your data processes run efficiently?

10) Describe a time when you had to optimize a slow data pipeline.

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A - Always check your assumptions
B - Backup your data
C - Check your code

D - Do you know your data?
E - Evaluate your results
F - Find the anomalies

G - Get help when you need it
H - Have a backup plan
I - Investigate your outliers

J - Justify your methods
K - Keep your data clean
L - Let your data tell a story

M - Make your visualizations impactful
N - No one knows everything
O - Outline your analysis

P - Practice good documentation
Q - Quality control is key
R - Review your work

S - Stay organized
T - Test your assumptions
U - Use the right tools

V - Verify your results
W - Write clear and concise reports
X - Xamine for gaps in data

Y - Yield to the evidence
Z - Zero in on your findings

If you can master the ABCs of data analysis, you will be well on your way to being a successful Data Analyst.

𝐇𝐨𝐰 𝐭𝐨 𝐏𝐫𝐞𝐩𝐚𝐫𝐞 𝐭𝐨 𝐁𝐞𝐜𝐨𝐦𝐞 𝐚 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭

𝟏. 𝐄𝐱𝐜𝐞𝐥- Learn formulas, Pivot tables, Lookup, VBA Macros.

𝟐. 𝐒𝐐𝐋- Joins, Windows, CTE is the most important

𝟑. 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈- Power Query Editor(PQE), DAX, MCode, RLS

𝟒. 𝐏𝐲𝐭𝐡𝐨𝐧- Basics & Libraries(mainly pandas, numpy, matplotlib and seaborn libraries)

5. Practice SQL and Python questions on platforms like 𝐇𝐚𝐜𝐤𝐞𝐫𝐑𝐚𝐧𝐤 or 𝐖𝟑𝐒𝐜𝐡𝐨𝐨𝐥𝐬.

6. Know the basics of denoscriptive statistics(mean, median, mode, Probability, normal, binomial, Poisson distributions etc).

7. Learn to use 𝐀𝐈/𝐂𝐨𝐩𝐢𝐥𝐨𝐭 𝐭𝐨𝐨𝐥𝐬 like GitHub Copilot or Power BI's AI features to automate tasks, generate insights, and improve your projects(Most demanding in Companies now)

8. Get hands-on experience with one cloud platform: 𝐀𝐳𝐮𝐫𝐞, 𝐀𝐖𝐒, 𝐨𝐫 𝐆𝐂𝐏

9. Work on at least two end-to-end projects.

10. Prepare an ATS-friendly resume and start applying for jobs.

11. Prepare for interviews by going through common interview questions on Google and YouTube.

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Data Analyst Interview!

𝐑𝐨𝐮𝐧𝐝 1: Technical Round - 15 mins
1. Tell me about yourself
2. Tell me about your experience
3. What is VLookup, when we are using VLookup what do we have to check before applying?
4. Are you familiar with dashboards and generating reports
5. How do you generate reports generally
6. How to delete duplicates in Power BI
7. In Power BI do you know how to draw all charts
8. Do you have any questions?

𝐑𝐨𝐮𝐧𝐝 2: Manager Round - 30 mins
1. Tell me about yourself
2. Tell me about our Organization
3. Tell me about your work experience
4. To whom do you report usually
5. Why do you choose this role
6. Why this organization only
7. Why do you think you will be suitable for this role
8. Do you have any questions

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Statistical interview questions for entry-level data analyst roles in an MNC.

1. Explain the difference between mean, median, and mode. When would you use each?
2. How do you calculate the variance and standard deviation of a dataset?
3. What is skewness and kurtosis? How do they help in understanding data distribution?
4. What is the central limit theorem, and why is it important in statistics?
5. Describe different types of probability distributions (e.g., normal, binomial, Poisson).
6. Explain the difference between a population and a sample. Why is sampling important?
7. What are null and alternative hypotheses? How do you formulate them?
8. Describe the steps in conducting a hypothesis test.
9. What is a p-value? How do you interpret it in the context of a hypothesis test?
10. When would you use a t-test versus a z-test?
11. Explain how you would conduct an independent two-sample t-test. What assumptions must be met?
12. Describe a scenario where you would use a paired sample t-test.
13. What is ANOVA, and how does it differ from a t-test?
14. Explain how you would interpret the results of a one-way ANOVA.
15. Describe a situation where you might use a two-way ANOVA.
16. What is a chi-square test for independence? When would you use it?
17. How do you interpret the results of a chi-square goodness-of-fit test?
18. Explain the assumptions and limitations of chi-square tests.
19. What is the difference between simple linear regression and multiple regression?
20. How do you assess the goodness-of-fit of a regression model?
21. Explain multicollinearity and how you would detect and handle it in a regression model.
22. What is the difference between correlation and causation?
23. How do you interpret the Pearson correlation coefficient?
24. When would you use Spearman rank correlation instead of Pearson correlation?
25. What are some common methods for forecasting time series data?
26. Explain the components of a time series (trend, seasonality, residuals).
27. How would you handle missing data in a time series dataset?
28. Describe your approach to exploratory data analysis (EDA).
29. How do you handle outliers in a dataset?
30. Explain the steps you would take to validate the results of your analysis.
31. Give an example of how you have used statistical analysis to solve a real-world problem

Hope this helps you 😊

Amazon Data Analyst Interview Questions for 1-3 years of experience role :-

A. SQL:

1. You have two tables: Employee and Department.
- Employee Table Columns: Employee_id, Employee_Name, Department_id, Salary
- Department Table Columns: Department_id, Department_Name, Location

Write an SQL query to find the name of the employee with the highest salary in each location.

2. You have two tables: Orders and Customers.
- Orders Table Columns: Order_id, Customer_id, Order_Date, Amount
- Customers Table Columns: Customer_id, Customer_Name, Join_Date

Write an SQL query to calculate the total order amount for each customer who joined in the current year. The output should contain Customer_Name and the total amount.

B. Python:

1. Basic oral questions on NumPy (e.g., array creation, slicing, broadcasting) and Matplotlib (e.g., plot types, customization).

2. Basic oral questions on pandas (like: groupby, loc/iloc, merge & join, etc.)

2. Write the code in NumPy and Pandas to replicate the functionality of your answer to the second SQL question.

C. Leadership or Situational Questions:

(Based on the leadership principle of Bias for Action)

- Describe a situation where you had to make a quick decision with limited information. How did you proceed, and what was the outcome?

(Based on the leadership principle of Dive Deep)

- Can you share an example of a project where you had to delve deeply into the data to uncover insights or solve a problem? What steps did you take, and what were the results?

(Based on the leadership principle of Customer Obsession)

- Tell us about a time when you went above and beyond to meet a customer's needs or expectations. How did you identify their requirements, and what actions did you take to deliver exceptional service?

D. Excel:

Questions on advanced functions like VLOOKUP, XLookup, SUMPRODUCT, INDIRECT, TEXT functions, SUMIFS, COUNTIFS, LOOKUPS, INDEX & MATCH, AVERAGEIFS. Plus, some basic questions on pivot tables, conditional formatting, data validation, and charts.

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Some Imp Scenario Q & A for product based Company :
You are a data analyst at a logistics company. The company wants to analyze delivery performance and customer satisfaction. Your tasks are:
1. Identify late deliveries and their impact on customer satisfaction.
2. Calculate the average delivery time for each region.
3. Create a Power BI report to visualize delivery performance and identify areas for improvement

Answer:

SQL Queries to Retrieve Data
1. Identify Late Deliveries and Their Impact on Customer Satisfaction:
SELECT
d.DeliveryID,
d.CustomerID,
d.DeliveryDate,
d.ExpectedDeliveryDate,
d.DeliveryTime,
c.SatisfactionScore
FROM Deliveries d
JOIN Customers c ON d.CustomerID = c.CustomerID
WHERE d.DeliveryDate > d.ExpectedDeliveryDate;

2. Calculate the Average Delivery Time for Each Region:
SELECT
Region,
AVG(DATEDIFF(day, OrderDate, DeliveryDate)) AS AvgDeliveryTime
FROM Deliveries
GROUP BY Region;

3. Customer Satisfaction by Delivery Performance:
SELECT
DeliveryPerformance,
AVG(SatisfactionScore) AS AvgSatisfactionScore
FROM (
SELECT
d.CustomerID,
c.SatisfactionScore,
CASE
WHEN d.DeliveryDate <= d.ExpectedDeliveryDate THEN 'On Time'
ELSE 'Late'
END AS DeliveryPerformance
FROM Deliveries d
JOIN Customers c ON d.CustomerID = c.CustomerID
) AS DeliveryData
GROUP BY DeliveryPerformance;

Import Data into Power BI
1. Load Data:
Open Power BI Desktop.
Use the "Get Data" feature to connect to your SQL database.
Import the result sets from the SQL queries into Power BI.

2. Create Relationships (if necessary):
Ensure that the data tables are properly related, such as linking the Deliveries table to the Customers table.

Create Visualizations
1. Late Deliveries and Their Impact on Customer Satisfaction:
Create a table visual.
Drag DeliveryID, CustomerID, DeliveryDate, ExpectedDeliveryDate, DeliveryTime, and SatisfactionScore to the Values.

2. Average Delivery Time for Each Region:
Create a bar chart.
Drag Region to the Axis.
Drag AvgDeliveryTime to the Values.

3. Customer Satisfaction by Delivery Performance:
Create a bar chart.
Drag DeliveryPerformance to the Axis.
Drag AvgSatisfactionScore to the Values.

4. Overall Delivery Analysis:
Create a pie chart.
Drag Region to the Legend.
Drag AvgDeliveryTime to the Values.

Optimize Performance
1. Data Model Optimization:
Filter data to include only necessary columns and rows.
Use summarized tables to pre-aggregate data.

2. DAX Optimization:
Create measures for dynamic calculations.
Simplify DAX formulas to improve performance.

3. Visualization Optimization:
Limit the number of visuals per page.
Avoid excessive use of slicers or custom visuals that can impact performance.

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