Razorpay Data Science Intern Interview Questions and Answers

Q1. Tell me about yourself and the current projects you are working on.
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Q1. Sql Questions on regex, json , window functions were there.
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Q1. Questions on current project, Tableau related question were there.
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Q1. Brief me about yourself
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Q1. What is your experience as a data analyst?
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Sql, python, statistical questions

Q1. How daily work looks like?
Ans. 
Daily work involves analyzing data, creating reports, and communicating findings to stakeholders.
• Reviewing and cleaning data sets

• Creating visualizations and dashboards

• Conducting statistical analysis

• Communicating insights to non-technical stakeholders
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Q2. Most ise tools?
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Some of the most commonly used tools for data analysis include Excel, Python, R, Tableau, and SQL.
• Excel is widely used for data manipulation and analysis.

• Python and R are popular programming languages for data analysis and machine learning.

• Tableau is a powerful data visualization tool.

• SQL is essential for querying databases and extracting data.
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4 sql tasks on 3 tables. Joins, subqueries

Q1. What techonologies do you know?
Ans. 
I am proficient in SQL, Python, R, Tableau, and Excel for data analysis and visualization.
• SQL

• Python

• R

• Tableau

• Excel
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SQL Coding Tests. You get 3 tables and they ask you to find customer with certain conditions. Not hard

Q1. Implement lag() and lead() without using lag() or lead()
Ans. 
Implement lag() and lead() functions without using lag() or lead()
• To implement lag(), shift the values in the array by one position to the right

• To implement lead(), shift the values in the array by one position to the left

• For example, to implement lag() on [1, 2, 3, 4], you can shift the values to get [null, 1, 2, 3]

• For lead(), you can shift the values to get [2, 3, 4, null]
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Q2. How will you go about optimising a query?
Ans. 
Optimising a query involves identifying bottlenecks and improving efficiency.
• Identify slow-performing queries using tools like query execution plans.

• Optimize database indexes to speed up query performance.

• Limit the number of columns and rows returned in the query.

• Avoid using SELECT * and instead specify only the necessary columns.

• Use appropriate data types and avoid unnecessary data conversions.

• Consider denormalizing d...read more
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Q3. If the 3rd position in a string contains 'b' replace it with 'v'
Ans. 
Replace 'b' with 'v' in the 3rd position of each string in an array
• Iterate through each string in the array

• Check if the 3rd position contains 'b'

• If yes, replace 'b' with 'v'
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Q1. SQL Joins and remove duplicates
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Q2. Function to create a transaction bin column
Ans. 
Create a function to generate a transaction bin column based on transaction amounts.
• Create bins based on transaction amounts (e.g. $0-$100, $101-$200, etc.)

• Use pandas cut() function in Python to create bins

• Assign bin labels to the transactions based on the bin ranges
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Q1. SQL case study for data
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Q2. Find nth salary
Ans. 
To find the nth salary in a dataset
• Sort the dataset in descending order based on salary

• Remove duplicates if necessary

• Return the salary at index n-1
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