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.

1. Define the term 'Data Wrangling.

Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.

2. What are the best methods for data cleaning?

Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.


3. Explain the Type I and Type II errors in Statistics?

In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.

A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.

4. How do you make a dropdown list in MS Excel?

First, click on the Data tab that is present in the ribbon.Under the Data Tools group, select Data Validation.Then navigate to Settings > Allow > List.Select the source you want to provide as a list array.

5. State some ways to improve the performance of Tableau?

Use an Extract to make workbooks run faster.
Reduce the scope of data to decrease the volume of data.
Reduce the number of marks on the view to avoid information overload.
Hide unused fields.
Use Context filters.
Use indexing in tables and use the same fields for filtering.
Remove unnecessary calculations and sheets.

1. What are Query and Query language?

A query is nothing but a request sent to a database to retrieve data or information. The required data can be retrieved from a table or many tables in the database.

Query languages use various types of queries to retrieve data from databases. SQL, Datalog, and AQL are a few examples of query languages; however, SQL is known to be the widely used query language.



2. What are Superkey and candidate key?

A super key may be a single or a combination of keys that help to identify a record in a table. Know that Super keys can have one or more attributes, even though all the attributes are not necessary to identify the records.

A candidate key is the subset of Superkey, which can have one or more than one attributes to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.


3. What do you mean by buffer pool and mention its benefits?

A buffer pool in SQL is also known as a buffer cache. All the resources can store their cached data pages in a buffer pool. The size of the buffer pool can be defined during the configuration of an instance of SQL Server.
The following are the benefits of a buffer pool:

Increase in I/O performance
Reduction in I/O latency
Increase in transaction throughput
Increase in reading performance


4. What is the difference between Zero and NULL values in SQL?

When a field in a column doesn’t have any value, it is said to be having a NULL value. Simply put, NULL is the blank field in a table. It can cancel be considered as an unassigned, unknown, or unavailable value. On the contrary, zero is a number, and it is an available, assigned, and known value.

Here are some commonly asked SQL interview questions along with brief answers:

1. What is SQL?
   - SQL stands for Structured Query Language, used for managing and manipulating relational databases.

2. What are the types of SQL commands?
   - SQL commands can be broadly categorized into four types: Data Definition Language (DDL), Data Manipulation Language (DML), Data Control Language (DCL), and Transaction Control Language (TCL).

3. What is the difference between CHAR and VARCHAR data types?
   - CHAR is a fixed-length character data type, while VARCHAR is a variable-length character data type. CHAR will always occupy the same amount of storage space, while VARCHAR will only use the necessary space to store the actual data.

4. What is a primary key?
   - A primary key is a column or a set of columns that uniquely identifies each row in a table. It ensures data integrity by enforcing uniqueness and can be used to establish relationships between tables.

5. What is a foreign key?
   - A foreign key is a column or a set of columns in one table that refers to the primary key in another table. It establishes a relationship between two tables and ensures referential integrity.

6. What is a JOIN in SQL?
   - JOIN is used to combine rows from two or more tables based on a related column between them. There are different types of JOINs, including INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

7. What is the difference between INNER JOIN and OUTER JOIN?
   - INNER JOIN returns only the rows that have matching values in both tables, while OUTER JOIN (LEFT, RIGHT, FULL) returns all rows from one or both tables, with NULL values in columns where there is no match.

8. What is the difference between GROUP BY and ORDER BY?
   - GROUP BY is used to group rows that have the same values into summary rows, typically used with aggregate functions like SUM, COUNT, AVG, etc., while ORDER BY is used to sort the result set based on one or more columns.

9. What is a subquery?
   - A subquery is a query nested within another query, used to return data that will be used in the main query. Subqueries can be used in SELECT, INSERT, UPDATE, and DELETE statements.

10. What is normalization in SQL?
    - Normalization is the process of organizing data in a database to reduce redundancy and dependency. It involves dividing large tables into smaller tables and defining relationships between them to improve data integrity and efficiency.

Around 90% questions will be asked from sql in data analytics interview, so please make sure to practice SQL skills using websites like stratascratch. ☺️💪

Planning for Data Science or Data Engineering Interview.

Focus on SQL & Python first. Here are some important questions which you should know.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.

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Here is a list of Important interview questions

SQL INTERVIEW QUESTIONS WITH IMPORTANT TOPICS
👇👇
https://t.iss.one/sqlspecialist/426

Data Analyst Interview Questions
👇👇
https://t.iss.one/DataAnalystInterview/69

Python Interview Questions and Answers
👇👇
https://t.iss.one/dsabooks/96

Data Science Interview Questions
👇👇
https://t.iss.one/datasciencefun/1058?single

Advanced Power BI Interview Questions
👇👇
https://t.iss.one/sqlspecialist/422

DSA INTERVIEW QUESTIONS
👇👇
https://t.iss.one/crackingthecodinginterview/77

Use Chat GPT to prepare for your next INTERVIEW
👇👇
https://t.iss.one/getjobss/1483

ENJOY LEARNING 👍👍

✔️📚A beginner's roadmap for learning SQL:

🔺Understand Basics:
Learn what SQL is and its purpose in managing relational databases.
Understand basic database concepts like tables, rows, columns, and relationships.

🔺Learn SQL Syntax:
Familiarize yourself with SQL syntax for common commands like SELECT, INSERT, UPDATE, DELETE.
Understand clauses like WHERE, ORDER BY, GROUP BY, and JOIN.

🔺Setup a Database:
Install a relational database management system (RDBMS) like MySQL, SQLite, or PostgreSQL.
Practice creating databases, tables, and inserting data.

🔺Retrieve Data (SELECT):
Learn to retrieve data from a database using SELECT statements.
Practice filtering data using WHERE clause and sorting using ORDER BY.

🔺Modify Data (INSERT, UPDATE, DELETE):
Understand how to insert new records, update existing ones, and delete data.
Be cautious with DELETE to avoid unintentional data loss.

🔺Working with Functions:
Explore SQL functions like COUNT, AVG, SUM, MAX, MIN for data analysis.
Understand string functions, date functions, and mathematical functions.

🔺Data Filtering and Sorting:
Learn advanced filtering techniques using AND, OR, and IN operators.
Practice sorting data using multiple columns.

🔺Table Relationships (JOIN):
Understand the concept of joining tables to retrieve data from multiple tables.
Learn about INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

🔺Grouping and Aggregation:
Explore GROUP BY clause to group data based on specific columns.
Understand aggregate functions for summarizing data (SUM, AVG, COUNT).

🔺Subqueries:
Learn to use subqueries to perform complex queries.
Understand how to use subqueries in SELECT, WHERE, and FROM clauses.

🔺Indexes and Optimization:
Gain knowledge about indexes and their role in optimizing queries.
Understand how to optimize SQL queries for better performance.

🔺Transactions and ACID Properties:
Learn about transactions and the ACID properties (Atomicity, Consistency, Isolation, Durability).
Understand how to use transactions to maintain data integrity.

🔺Normalization:
Understand the basics of database normalization to design efficient databases.
Learn about 1NF, 2NF, 3NF, and BCNF.

🔺Backup and Recovery:
Understand the importance of database backups.
Learn how to perform backups and recovery operations.

🔺Practice and Projects:
Apply your knowledge through hands-on projects.
Practice on platforms like LeetCode, HackerRank, or build your own small database-driven projects.

👀👍Remember to practice regularly and build real-world projects to reinforce your learning.

Happy Learning 🥳 📚

Q1: How would you analyze data to understand user connection patterns on a professional network?

Ans: I'd use graph databases like Neo4j for social network analysis. By analyzing connection patterns, I can identify influencers or isolated communities.

Q2: Describe a challenging data visualization you created to represent user engagement metrics.

Ans: I visualized multi-dimensional data showing user engagement across features, regions, and time using tools like D3.js, creating an interactive dashboard with drill-down capabilities.

Q3: How would you identify and target passive job seekers on LinkedIn?

Ans: I'd analyze user behavior patterns, like increased profile updates, frequent visits to job postings, or engagement with career-related content, to identify potential passive job seekers.

Q4: How do you measure the effectiveness of a new feature launched on LinkedIn?

Ans: I'd set up A/B tests, comparing user engagement metrics between those who have access to the new feature and a control group. I'd then analyze metrics like time spent, feature usage frequency, and overall platform engagement to measure effectiveness.

Here are 30 most asked SQL questions to clear your next interview -

➤ 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀

1. Calculate the moving average of sales for the past 3 months.
2. Assign a dense rank to employees based on their salary.
3. Retrieve the first and last order date for each customer.
4. Find the Nth highest salary for each department using window functions.
5. Determine the percentage of total sales contributed by each employee.

➤ 𝗖𝗼𝗺𝗺𝗼𝗻 𝗧𝗮𝗯𝗹𝗲 𝗘𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻𝘀 (𝗖𝗧𝗘)

1. Use a CTE to split a full name into first and last names.
2. Write a CTE to find the longest consecutive streak of sales for an employee.
3. Generate Fibonacci numbers up to a given limit using a recursive CTE.
4. Use a CTE to identify duplicate records in a table.
5. Find the total sales for each category and filter categories with sales greater than a threshold using a CTE.

➤ 𝗝𝗼𝗶𝗻𝘀 (𝗜𝗻𝗻𝗲𝗿, 𝗢𝘂𝘁𝗲𝗿, 𝗖𝗿𝗼𝘀𝘀, 𝗦𝗲𝗹𝗳)

1. Retrieve a list of customers who have placed orders and those who have not placed orders (Full Outer Join).
2. Find employees working on multiple projects using a self join.
3. Match orders with customers and also display unmatched orders (Left Join).
4. Generate a product pair list but exclude pairs with identical products (Cross Join with condition).
5. Retrieve employees and their managers using a self join.

➤ 𝗦𝘂𝗯𝗾𝘂𝗲𝗿𝗶𝗲𝘀

1. Find customers whose total order amount is greater than the average order amount.
2. Retrieve employees who earn the lowest salary in their department.
3. Identify products that have been ordered more than 10 times using a subquery.
4. Find regions where the maximum sales are below a given threshold.

➤ 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀

1. Calculate the median salary for each department.
2. Find the total sales for each month and rank them in descending order.
3. Count the number of distinct customers for each product.
4. Retrieve the top 5 regions by total sales.
5. Calculate the average order value for each customer.

➤ 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲

1. Write a query to find duplicate values in an indexed column.
2. Analyze the impact of adding a composite index on query performance.
3. Identify columns with high cardinality that could benefit from indexing
4. Compare query execution times before and after adding a clustered index.
5. Write a query that avoids the use of an index to test performance differences.

Many candidates get rejected in interviews due to one of the reasons listed below:

📌Poor Preparation – Walking into an interview without knowing about the company, its culture, or the role is like sitting for an exam without studying. It shows a lack of interest.

📌Weak Communication Skills – Even the best ideas can fail if you can’t communicate them effectively. Clear, confident, and concise answers are key.

📌Inappropriate Attire – First impressions matter, and dressing unprofessionally can send the wrong signal. Always align with the company’s dress code.

📌Overconfidence or Lack of Confidence – Being too arrogant or overly timid can both raise red flags. A balanced, professional attitude is what employers look for.

📌Not Asking Questions – Interviews are a two-way street. Failing to ask thoughtful questions can make you seem uninterested or unengaged.

📌Negative Comments About Previous Employers – Speaking ill of past experiences reflects poorly on your professionalism. Keep the conversation positive.

📌Focusing Only on Salary – While compensation is important, discussing it too soon or too much might make you seem less interested in the job itself.

By recognizing these common pitfalls and addressing them, you can significantly improve your chances of landing that dream job!

15 Steps to master Python Programming

Data Analyst Interview Questions

Data Analyst vs. Data Scientist - What's the Difference?

1. Data Analyst:
   - Role: Focuses on interpreting and analyzing data to help businesses make informed decisions.
   - Skills: Proficiency in SQL, Excel, data visualization tools (Tableau, Power BI), and basic statistical analysis.
   - Responsibilities: Data cleaning, performing EDA, creating reports and dashboards, and communicating insights to stakeholders.

2. Data Scientist:
   - Role: Involves building predictive models, applying machine learning algorithms, and deriving deeper insights from data.
   - Skills: Strong programming skills (Python, R), machine learning, advanced statistics, and knowledge of big data technologies (Hadoop, Spark).
   - Responsibilities: Data modeling, developing machine learning models, performing advanced analytics, and deploying models into production.

3. Key Differences:
   - Focus: Data Analysts are more focused on interpreting existing data, while Data Scientists are involved in creating new data-driven solutions.
   - Tools: Analysts typically use SQL, Excel, and BI tools, while Data Scientists work with programming languages, machine learning frameworks, and big data tools.
   - Outcomes: Analysts provide insights and recommendations, whereas Scientists build models that predict future trends and automate decisions.

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Top 5 data analysis interview questions with answers 😄👇

Question 1: How would you approach a new data analysis project?

Ideal answer:
I would approach a new data analysis project by following these steps:
Understand the business goals. What is the purpose of the data analysis? What questions are we trying to answer?
Gather the data. This may involve collecting data from different sources, such as databases, spreadsheets, and surveys.
Clean and prepare the data. This may involve removing duplicate data, correcting errors, and formatting the data in a consistent way.
Explore the data. This involves using data visualization and statistical analysis to understand the data and identify any patterns or trends.
Build a model or hypothesis. This involves using the data to develop a model or hypothesis that can be used to answer the business questions.
Test the model or hypothesis. This involves using the data to test the model or hypothesis and see how well it performs.
Interpret and communicate the results. This involves explaining the results of the data analysis to stakeholders in a clear and concise way.

Question 2: What are some of the challenges you have faced in previous data analysis projects, and how did you overcome them?

Ideal answer:
One of the biggest challenges I have faced in previous data analysis projects is dealing with missing data. I have overcome this challenge by using a variety of techniques, such as imputation and machine learning.
Another challenge I have faced is dealing with large datasets. I have overcome this challenge by using efficient data processing techniques and by using cloud computing platforms.

Question 3: Can you describe a time when you used data analysis to solve a business problem?

Ideal answer:
In my previous role at a retail company, I was tasked with identifying the products that were most likely to be purchased together. I used data analysis to identify patterns in the purchase data and to develop a model that could predict which products were most likely to be purchased together. This model was used to improve the company's product recommendations and to increase sales.

Question 4: What are some of your favorite data analysis tools and techniques?

Ideal answer:
Some of my favorite data analysis tools and techniques include:
Programming languages such as Python and R
Data visualization tools such as Tableau and Power BI
Statistical analysis tools such as SPSS and SAS
Machine learning algorithms such as linear regression and decision trees

Question 5: How do you stay up-to-date on the latest trends and developments in data analysis?

Ideal answer:
I stay up-to-date on the latest trends and developments in data analysis by reading industry publications, attending conferences, and taking online courses. I also follow thought leaders on social media and subscribe to newsletters.

By providing thoughtful and well-informed answers to these questions, you can demonstrate to your interviewer that you have the analytical skills and knowledge necessary to be successful in the role.

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Quick checklist for you ✅

This is what all you require in Excel if you are planning to master Data Analytics

Basic Excel Skills
1. Excel Interface and Basics
• Ribbon, Menus, and Toolbars
• Workbook and Worksheet Navigation
• Data Entry and Formatting
2. Cell Referencing
• Absolute, Relative, and Mixed References
• Named Ranges

Data Cleaning and Preparation
3. Data Validation
• Drop-down Lists
• Custom Rules
4. Text Functions
• CONCATENATE, TEXTJOIN
• LEFT, RIGHT, MID
• TRIM, CLEAN
• FIND, SEARCH, SUBSTITUTE
5. Date and Time Functions
• TODAY, NOW
• DATE, DAY, MONTH, YEAR
• DATEDIF, NETWORKDAYS
6. Handling Errors
• IFERROR, ISERROR, ISBLANK

Data Analysis Functions
7. Logical Functions
• IF, AND, OR, NOT
8. Lookup and Reference Functions
• VLOOKUP, HLOOKUP
• INDEX, MATCH
• XLOOKUP (for modern Excel)
9. Statistical Functions
• AVERAGE, MEDIAN, MODE
• COUNT, COUNTA, COUNTIF, COUNTIFS
• RANK, LARGE, SMALL
10. Math Functions
• SUM, SUMIF, SUMIFS
• ROUND, ROUNDUP, ROUNDDOWN
• PRODUCT, AVERAGEIF

Data Visualization
11. Charts and Graphs
• Line, Bar, Column, and Pie Charts
• Scatter Plots and Bubble Charts
• Combo Charts
12. Conditional Formatting
• Color Scales
• Data Bars
• Custom Rules

Advanced Excel for Analytics
13. Pivot Tables and Pivot Charts
• Creating and Customizing Pivot Tables
• Grouping Data
• Calculated Fields and Items
14. Power Query
• Importing Data from External Sources
• Data Transformation and Shaping
15. Power Pivot
• Creating Relationships between Tables
• DAX (Data Analysis Expressions) Basics

Automation and Optimization
16. Macros and VBA Basics
• Recording Macros
• Editing VBA Code
17. What-If Analysis
• Goal Seek
• Data Tables
• Scenario Manager

Integration and Collaboration
18. Data Import and Export
• Importing Data from CSV, Text, and SQL
• Exporting Data to Other Formats
19. Collaboration Tools
• Sharing and Protecting Workbooks
• Track Changes

Problem-Solving Tools
20. Solver and Optimization
• Setting Up Solver
• Constraints and Optimization
21. Forecasting
• Trendlines
• Forecast Sheets
• Exponential Smoothing
ptimization