EXL Data Analyst Interview Experience:


SQL Questions

1. You have a table Transactions with columns TransactionID, CustomerID, Date, and Amount. Write a query to calculate the cumulative revenue per customer for each month in the last year.
2. A table Production contains columns PlantID, Date, and Output. Write a query to identify the plants that consistently exceeded their daily average output for at least 20 days in a given month.
3. In a table EmployeeAttendance with columns EmployeeID, Date, and Status (values: ‘Present’, ‘Absent’), write a query to find employees with the highest consecutive absences in the last quarter.
4. What are the pros and cons of using indexes in SQL, and when would you avoid using them?
5. Explain the differences between window functions and aggregate functions with examples.

Python Questions

6. Write a Python script to merge multiple CSV files from a directory into a single file and perform basic data cleaning.
7. Given a list of dictionaries, write a Python program to group the data by a specific key and calculate summary statistics for the grouped data.
8. Explain the difference between a list, a tuple, and a dictionary in Python, and provide examples of their usage.
9. Write a Python function to automate the generation of monthly reports from a dataset stored in an Excel file.

Power BI Questions

10. How would you create a dashboard in Power BI to track the operational efficiency of production plants?
11. Explain how you would handle a situation where the data source refresh in Power BI is causing delays.
12. What is the difference between row-level security and role-level security in Power BI?
13. How would you use Power BI to visualize trends and outliers in daily sales data?
14. Discuss how you would create a calculated measure to show YoY (Year-over-Year) growth in Power BI.

General Questions

15. Share an example where your data-driven insights helped solve a business problem or improve a process.
16. How do you prioritize tasks and manage deadlines in a high-pressure environment?

Myntra interview questions for Data Analyst

1. You have a dataset with missing values. How would you use a combination of Pandas and NumPy to fill missing values based on the mean of the column?
2. How would you create a new column in a Pandas DataFrame by normalizing an existing numeric column using NumPy’s np.min() and np.max()?
3. Explain how to group a Pandas DataFrame by one column and apply a NumPy function, like np.std() (standard deviation), to each group.
4. How can you convert a time-series column in a Pandas DataFrame to NumPy’s datetime format for faster time-based calculations?
5. How would you identify and remove outliers from a Pandas DataFrame using NumPy’s Z-score method (scipy.stats.zscore)?
6. How would you use NumPy’s percentile() function to calculate specific quantiles for a numeric column in a Pandas DataFrame?
7. How would you use NumPy's polyfit() function to perform linear regression on a dataset stored in a Pandas DataFrame?
8. How can you use a combination of Pandas and NumPy to transform categorical data into dummy variables (one-hot encoding)?
9. How would you use both Pandas and NumPy to split a dataset into training and testing sets based on a random seed?
10. How can you apply NumPy's vectorize() function on a Pandas Series for better performance?
11. How would you optimize a Pandas DataFrame containing millions of rows by converting columns to NumPy arrays? Explain the benefits in terms of memory and speed.
12. How can you perform complex mathematical operations, such as matrix multiplication, using NumPy on a subset of a Pandas DataFrame?
13. Explain how you can use np.select() to perform conditional column operations in a Pandas DataFrame.
14. How can you handle time series data in Pandas and use NumPy to perform statistical analysis like rolling variance or covariance?
15. How can you integrate NumPy's random module (np.random) to generate random numbers and add them as a new column in a Pandas DataFrame?
16. Explain how you would use Pandas' applymap() function combined with NumPy’s vectorized operations to transform all elements in a DataFrame.
17. How can you apply mathematical transformations (e.g., square root, logarithm) from NumPy to specific columns in a Pandas DataFrame?
18. How would you efficiently perform element-wise operations between a Pandas DataFrame and a NumPy array of different dimensions?
19. How can you use NumPy functions like np.linalg.inv() or np.linalg.det() for linear algebra operations on numeric columns of a Pandas DataFrame?
20. Explain how you would compute the covariance matrix between multiple numeric columns of a DataFrame using NumPy.
21. What are the key differences between a Pandas DataFrame and a NumPy array? When would you use one over the other?
22. How can you convert a NumPy array into a Pandas DataFrame, and vice versa? Provide an example.

You can find the answers here

Hope this helps you 😊

Interview guide for Data Analyst Role

When interviewing for a Data Analyst role as a fresher, you’ll likely encounter questions that focus on your understanding of data analysis concepts, technical skills, and problem-solving abilities. Here’s a comprehensive list of commonly asked interview questions:

1. General and Behavioral Questions

• Tell me about yourself.
• Why do you want to become a Data Analyst?
• What do you know about our company and why do you want to work here?
• Describe a time when you solved a problem using data.
• How do you prioritize tasks and manage deadlines?
• Tell me about a time when you worked in a team to complete a project.

2. Technical Questions

• What are the different types of joins in SQL? (Expect variations of SQL questions)
• How would you handle missing or inconsistent data?
• What is normalization? Why is it important?
• Explain the difference between primary keys and foreign keys in a database.
• What are the most common data types in SQL?
• How do you perform data cleaning in Excel?

3. Analytical Skills and Problem-Solving

• How would you find outliers in a dataset?
• How would you approach analyzing a dataset with 1 million rows?
• If given two datasets, how would you combine them?
• What steps would you take if your results didn’t match stakeholders’ expectations?
• How would you identify trends or patterns in a dataset?

4. Excel-Related Questions

• What are pivot tables and how do you use them?
• Explain VLOOKUP and HLOOKUP.
• How would you handle large datasets in Excel?
• What is the use of conditional formatting?
• How would you create a dashboard in Excel?
• How can you create a custom formula in Excel?

5. SQL Questions

• Write a SQL query to find the second highest salary in a table.
• What is the difference between WHERE and HAVING clauses?
• How would you optimize a slow-running query?
• What is the difference between UNION and UNION ALL?
• What is a subquery, and when would you use it?

6. Statistics and Data Analysis

• Explain the difference between mean, median, and mode.
• What is standard deviation, and why is it important?
• What is regression analysis? Can you explain linear regression?
• What is correlation, and how is it different from causation?
• What are some key metrics you would track for a marketing campaign?

7. Data Visualization and Tools

• What tools have you used for data visualization?
• Explain a situation where you used charts to tell a story.
• What is your experience with tools like Tableau or Power BI?
• How would you decide which chart type to use for visualizing data?
• Have you ever created a dashboard? If yes, what were the key features?

8. Python/R (If mentioned on your resume)

• What libraries do you use in Python for data analysis?
• How would you import a dataset and perform basic analysis in Python?
• What are some common data manipulation functions in pandas?
• How do you handle missing values in Python?

9. Scenario-Based Questions

• Imagine you are given a dataset of customer purchases; how would you segment the customers?
• You are given sales data for the past five years. What steps would you take to forecast the next year’s sales?
• If you find conflicting data in a report, how would you handle the situation?
• Describe a project where you identified key insights using data.

10. Aptitude or Logical Questions

• Some companies also include questions testing your quantitative aptitude, logical reasoning, and pattern recognition to gauge problem-solving skills.

Tips to Prepare:

1. Strengthen your Basics: Brush up on SQL, Excel, and statistical concepts.
2. Mock Interviews: Practice explaining your thought process for data problems.
3. Projects: Be ready to discuss any projects or internships you’ve done.
4. Stay Current: Read about trends in data analysis and business intelligence.

Hope this helps you 😊

Meesho Data Analyst interview experience (0-3) -


Power BI Questions:

1. Explain the concept of context transition in DAX and provide an example.
2. How would you optimize a complex Power BI report for faster performance?
3. Describe the process of creating and using calculation groups in Power BI.
4. Explain how you would handle large datasets in Power BI without compromising performance.
5. What is a composite model in Power BI, and how can it be used effectively?
6. How does the USERELATIONSHIP function work, and when would you use it?
7. Describe how to use Power Query M language for advanced data transformations.
8. Explain the difference between CROSSFILTER and TREATAS in DAX.

SQL Questions:

1. How would you optimize a slow-running query with multiple joins?
2. What is a recursive CTE, and can you provide an example of when to use it?
3. Explain the difference between clustered and non-clustered indexes and when to use each.
4. Write a query to find the second highest salary in each department.
5. How would you detect and resolve deadlocks in SQL?
6. Explain window functions and provide examples of ROW_NUMBER, RANK, and DENSE_RANK.
7. Describe the ACID properties in database transactions and their significance.
8. Write a query to calculate a running total with partitions based on specific conditions.

You can read detailed article with answers here

Hope this helps you 😊

Interview list for Data Analytics Roles

SQL Essentials:
- SELECT statements including WHERE, ORDER BY, GROUP BY, HAVING
- Basic JOINS: INNER, LEFT, RIGHT, FULL
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries, Common Table Expressions (WITH clause)
- CASE statements, advanced JOIN techniques, and Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK)

Excel Proficiency:
- Cell operations, formulas (SUMIFS, COUNTIFS, AVERAGEIFS, LOOKUPS)
- PivotTables, PivotCharts, Data validation, What-if analysis
- Advanced formulas, Data Model & Power Pivot

Power BI Skills:
- Data modeling (importing data, managing relationships)
- Data transformation with Power Query, DAX for calculated columns/measures
- Creating interactive reports and dashboards, visualizations

Data Warehousing:
-Concepts of OLAP vs. OLTP
-Star and Snowflake schema designs
-ETL processes: Extract, Transform, Load
-Data lake vs. data warehouse

Cloud Computing for Data Analytics:
-Benefits of cloud services (AWS, Azure, Google Cloud)
-Data storage solutions: S3, Azure Blob Storage, Google Cloud Storage
-Cloud-based data analytics tools: BigQuery, Redshift, Snowflake
-Cost management and optimization strategies

Python Programming:
- Basic syntax, control structures, data structures (lists, dictionaries)
- Pandas & NumPy for data manipulation: DataFrames, Series, groupby
-plotting with Matplotlib, Seaborn for visualization

Statistics Fundamentals:
- Mean, Median, Mode, Standard Deviation, Variance
- Probability distributions, Hypothesis Testing, P-values
- Confidence Intervals, Correlation, Simple Linear Regression

I have curated top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope this helps you 😊

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗸𝗻𝗼𝘄 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝗶𝗻 𝗮 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄?

𝗕𝗮𝘀𝗶𝗰 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻

-Brief introduction about yourself.

-Explanation of how you developed an interest in learning Power BI despite having a chemical background.


𝗧𝗼𝗼𝗹𝘀 𝗣𝗿𝗼𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆

-Discussion about the tools you are proficient in.

-Detailed explanation of a project that demonstrated your proficiency in these tools.

𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻

Explain about any Data Analytics Project you did, below are some follow-up questions for sales related data analysis project

Follow-up Question:

Was there any improvement in sales after building the report?

Provide a clear before and after scenario in sales post-report creation.

What areas did you identify where the company was losing sales, and what were your recommendations?

- How do you check the quality of data when it's given to you?
Explain your methods for ensuring data quality.

- How do you handle null values? Describe your approach to managing null values in datasets.


𝗦𝗤𝗟 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀

-Explain the order in which SQL clauses are executed.

-Write a query to find the percentage of the 18-year-old population.
Details: You are given two tables:
Table 1: Contains states and their respective populations.
Table 2: Contains three columns (state, gender, and population of 18-year-olds).

-Explain window functions and how to rank values in SQL.

- Difference between JOIN and UNION.

-How to return unique values in SQL.

𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀

-Solve a puzzle involving 3 gallons of water in one jar and 2 gallons in another to get exactly 4 gallons.
Step-by-step solution for the water puzzle.

- What skills have you learned on your own? Discuss the skills you self-taught and their impact on your career.

-Describe cases when you showcased team spirit.

-⭐ 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗔𝗽𝗽 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻
Scenario: Choose any social media app (I choose Discord).
Question: What function/feature would you add to the Discord app, and how would you track its success?

- Rate yourself on Excel, SQL, and Python out of 10.

- What are your strengths in data analytics?

Like if it helps :)

SQL Interview Questions (0-5 Year Experience)!!

Are you preparing for a SQL interview?

Here are some essential SQL concepts to review:

𝐁𝐚𝐬𝐢𝐜 𝐒𝐐𝐋 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬:

1. What is SQL, and why is it important in data analytics?
2. Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN.
3. What is the difference between WHERE and HAVING clauses?
4. How do you use GROUP BY and HAVING in a query?
5. Write a query to find duplicate records in a table.
6. How do you retrieve unique values from a table using SQL?
7. Explain the use of aggregate functions like COUNT(), SUM(), AVG(), MIN(), and MAX().
8. What is the purpose of a DISTINCT keyword in SQL?

𝐈𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞 𝐒𝐐𝐋:

1. Write a query to find the second-highest salary from an employee table.
2. What are subqueries and how do you use them?
3. What is a Common Table Expression (CTE)? Give an example of when to use it.
4. Explain window functions like ROW_NUMBER(), RANK(), and DENSE_RANK().
5. How do you combine results of two queries using UNION and UNION ALL?
6. What are indexes in SQL, and how do they improve query performance?
7. Write a query to calculate the total sales for each month using GROUP BY.

𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐒𝐐𝐋:

1. How do you optimize a slow-running SQL query?
2. What are views in SQL, and when would you use them?
3. What is the difference between a stored procedure and a function in SQL?
4. Explain the difference between TRUNCATE, DELETE, and DROP commands.
5. What are windowing functions, and how are they used in analytics?
6. How do you use PARTITION BY and ORDER BY in window functions?
7. How do you handle NULL values in SQL, and what functions help with that (e.g., COALESCE, ISNULL)?

Here you can find essential SQL Interview Resources👇
https://t.iss.one/mysqldata

Like this post if you need more 👍❤️

Hope it helps :)

✨The STAR method is a powerful technique used to answer behavioral interview questions effectively.

It helps structure responses by focusing on Situation, Task, Action, and Result. For analytics professionals, using the STAR method ensures that you demonstrate your problem-solving abilities, technical skills, and business acumen in a clear and concise way.

Here’s how the STAR method works, tailored for an analytics interview:

📍 1. Situation
Describe the context or challenge you faced. For analysts, this might be related to data challenges, business processes, or system inefficiencies. Be specific about the setting, whether it was a project, a recurring task, or a special initiative.

Example: “At my previous role as a data analyst at XYZ Company, we were experiencing a high churn rate among our subscription customers. This was a critical issue because it directly impacted revenue.”*

📍 2. Task
Explain the responsibilities you had or the goals you needed to achieve in that situation. In analytics, this usually revolves around diagnosing the problem, designing experiments, or conducting data analysis.

Example: “I was tasked with identifying the factors contributing to customer churn and providing actionable insights to the marketing team to help them improve retention.”*

📍 3. Action
Detail the specific actions you took to address the problem. Be sure to mention any tools, software, or methodologies you used (e.g., SQL, Python, data #visualization tools, #statistical #models). This is your opportunity to showcase your technical expertise and approach to problem-solving.

Example: “I collected and analyzed customer data using #SQL to extract key trends. I then used #Python for data cleaning and statistical analysis, focusing on engagement metrics, product usage patterns, and customer feedback. I also collaborated with the marketing and product teams to understand business priorities.”*

📍 4. Result
Highlight the outcome of your actions, especially any measurable impact. Quantify your results if possible, as this demonstrates your effectiveness as an analyst. Show how your analysis directly influenced business decisions or outcomes.

Example: “As a result of my analysis, we discovered that customers were disengaging due to a lack of certain product features. My insights led to a targeted marketing campaign and product improvements, reducing churn by 15% over the next quarter.”*

Example STAR Answer for an Analytics Interview Question:
Question: *"Tell me about a time you used data to solve a business problem."*

Answer (STAR format): 
🔻*S*: “At my previous company, our sales team was struggling with inconsistent performance, and management wasn’t sure which factors were driving the variance.” 
🔻*T*: “I was assigned the task of conducting a detailed analysis to identify key drivers of sales performance and propose data-driven recommendations.” 
🔻*A*: “I began by collecting sales data over the past year and segmented it by region, product line, and sales representative. I then used Python for #statistical #analysis and developed a regression model to determine the key factors influencing sales outcomes. I also visualized the data using #Tableau to present the findings to non-technical stakeholders.” 
🔻*R*: “The analysis revealed that product mix and regional seasonality were significant contributors to the variability. Based on my findings, the company adjusted their sales strategy, leading to a 20% increase in sales efficiency in the next quarter.”

Hope this helps you 😊

Infosys is hiring 20,000 freshers in various fields and here is a complete guide to crack  this interview

1. Understand the Interview Structure

Infosys fresher recruitment usually has three main stages:

• Aptitude Test (Written Exam)
• Technical Interview
• HR Interview

2. Aptitude Test Preparation

The first stage typically includes questions on logical reasoning, quantitative aptitude, and verbal ability. Prepare the following:

• Quantitative Aptitude: Topics include time & work, percentages, profit & loss, probability, permutations & combinations, and number series.
• Logical Reasoning: Focus on puzzles, blood relations, data interpretation, and syllogisms.
• Verbal Ability: This includes reading comprehension, sentence correction, error spotting, synonyms/antonyms, and fill-in-the-blanks.

Resources:

• Books: RS Aggarwal’s Quantitative Aptitude for quantitative topics.
• Websites: Platforms like IndiaBix or Testbook provide practice questions.

Tips:

• Practice regularly under timed conditions.
• Use mock tests to improve speed and accuracy.
• Focus on weak areas after taking a few practice tests.

3. Technical Interview Preparation

In this round, Infosys assesses your understanding of basic programming, algorithms, data structures, and other core subjects. Here’s how to prepare:

• Programming Languages: Have a solid foundation in at least one programming language (C, C++, Java, Python).
• Data Structures & Algorithms: Study key topics like arrays, linked lists, stacks, queues, trees, and sorting algorithms.
• DBMS, Operating Systems & Networks: Be prepared for basic questions on SQL, normalization, joins, process management, and networking protocols.

Sample Questions:
• How would you reverse a string in your preferred language?
• Explain the difference between a stack and a queue.
• What is a deadlock, and how can it be avoided?

Resources:
• GeeksforGeeks and LeetCode for coding practice and theory.
• Books like Cracking the Coding Interview by Gayle Laakmann McDowell.

Tips:
• Focus on problem-solving skills and code optimization.
• Be ready to explain your approach in technical questions.

4. Coding Round (If applicable)
Some Infosys roles might require you to go through a coding round. Practice coding problems related to arrays, strings, recursion, dynamic programming, and greedy algorithms.

Tools:
• HackerRank, CodeChef, and Codeforces are good platforms to practice coding challenges.
• Focus on coding efficiency and edge case handling.

5. HR Interview Preparation
In the HR round, you will be evaluated on your personality, communication skills, and cultural fit. Common questions include:
• Tell me about yourself.
• Why do you want to join Infosys?
• What are your strengths and weaknesses?

Tips:
• Prepare a structured self-introduction.
• Research Infosys’ values, projects, and recent developments to show enthusiasm for the company.
• Be honest but strategic with your answers regarding strengths and weaknesses.

6. Mock Interviews and Soft Skills
• Mock Interviews: Participate in mock interviews to simulate the real environment.
• Soft Skills: Work on clear communication and positive body language. Infosys looks for candidates who can explain technical concepts clearly.

7. Common Mistakes to Avoid
• Lack of Practice: Not practicing enough aptitude or coding questions can lead to poor performance in tests.
• Unclear Communication: Even if you know the solution, being unable to explain it well in technical interviews can hurt your chances.
• Overlooking HR Round: Many candidates prepare for technical rounds and ignore HR. Remember, HR rounds can be just as important.

8. Key Resources
• Aptitude: RS Aggarwal for Quantitative Aptitude.
• Coding: HackerRank, LeetCode.
• Technical Knowledge: GeeksforGeeks for theory and coding questions.
• Mock Tests: Websites like IndiaBix provide Infosys-specific mock tests and previous year papers.

Hope this helps you 😊

Important Interview Questions

1. What is a window function in SQL? How is it different from aggregate functions?
2. Explain the use of the OVER() clause in window functions.
3. What is the purpose of the PARTITION BY clause in window functions?
4. What is the role of the ORDER BY clause in a window function?
5. What is the difference between ROW_NUMBER(), RANK(), and DENSE_RANK() window functions?
6. How do window functions differ from group functions like GROUP BY?
7. Can you use window functions with an ORDER BY clause outside of the OVER() clause? Why or why not?
8. Write a query using the ROW_NUMBER() function to assign sequential numbers to rows in a result set.
9. How does the NTILE() function work in SQL? What is its use case?
10. What is the difference between LAG() and LEAD() window functions?

Hope this helps you 😊

Myntra interview questions for Data Analyst 2024.

1. You have a dataset with missing values. How would you use a combination of Pandas and NumPy to fill missing values based on the mean of the column?
2. How would you create a new column in a Pandas DataFrame by normalizing an existing numeric column using NumPy’s np.min() and np.max()?
3. Explain how to group a Pandas DataFrame by one column and apply a NumPy function, like np.std() (standard deviation), to each group.
4. How can you convert a time-series column in a Pandas DataFrame to NumPy’s datetime format for faster time-based calculations?
5. How would you identify and remove outliers from a Pandas DataFrame using NumPy’s Z-score method (scipy.stats.zscore)?
6. How would you use NumPy’s percentile() function to calculate specific quantiles for a numeric column in a Pandas DataFrame?
7. How would you use NumPy's polyfit() function to perform linear regression on a dataset stored in a Pandas DataFrame?
8. How can you use a combination of Pandas and NumPy to transform categorical data into dummy variables (one-hot encoding)?
9. How would you use both Pandas and NumPy to split a dataset into training and testing sets based on a random seed?
10. How can you apply NumPy's vectorize() function on a Pandas Series for better performance?
11. How would you optimize a Pandas DataFrame containing millions of rows by converting columns to NumPy arrays? Explain the benefits in terms of memory and speed.
12. How can you perform complex mathematical operations, such as matrix multiplication, using NumPy on a subset of a Pandas DataFrame?
13. Explain how you can use np.select() to perform conditional column operations in a Pandas DataFrame.
14. How can you handle time series data in Pandas and use NumPy to perform statistical analysis like rolling variance or covariance?
15. How can you integrate NumPy's random module (np.random) to generate random numbers and add them as a new column in a Pandas DataFrame?
16. Explain how you would use Pandas' applymap() function combined with NumPy’s vectorized operations to transform all elements in a DataFrame.
17. How can you apply mathematical transformations (e.g., square root, logarithm) from NumPy to specific columns in a Pandas DataFrame?
18. How would you efficiently perform element-wise operations between a Pandas DataFrame and a NumPy array of different dimensions?
19. How can you use NumPy functions like np.linalg.inv() or np.linalg.det() for linear algebra operations on numeric columns of a Pandas DataFrame?
20. Explain how you would compute the covariance matrix between multiple numeric columns of a DataFrame using NumPy.
21. What are the key differences between a Pandas DataFrame and a NumPy array? When would you use one over the other?
22. How can you convert a NumPy array into a Pandas DataFrame, and vice versa? Provide an example.

You can find the answers here

Hope this helps you 😊

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 script 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

Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope this helps you 😊

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):

- Descriptive 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.

Data Analytics Resources
👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Hope this helps you 😊

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;

Here you can find essential Interview Resources👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Like this post if you need more 👍❤️

Hope it helps :)

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 😊