Mu Sigma Data Analyst Intern Interview Questions and Answers

Q1. What is rdms? what are the objects in database? difference between olap and oltp? what is view? what is index? what are functions and stored procedures? what are constrains? what are foreign keys?
Ans. 
RDBMS is a relational database management system. Objects in a database include tables, views, indexes, functions, stored procedures, constraints, and foreign keys. OLAP is for data analysis while OLTP is for transaction processing.
• RDBMS stands for Relational Database Management System

• Objects in a database include tables, views, indexes, functions, stored procedures, constraints, and foreign keys

• OLAP (Online Analytical...read more
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Q1. About company? about yourself?
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Q1. How will you find loyal customers for a store like DMart , SmartBazar
Ans. 
Utilize customer transaction data and behavior analysis to identify loyal customers for DMart and SmartBazar.
• Use customer transaction history to identify frequent shoppers

• Analyze customer behavior patterns such as repeat purchases and average spend

• Implement loyalty programs to incentivize repeat purchases

• Utilize customer feedback and reviews to gauge loyalty

• Segment customers based on their shopping habits and preferenc
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Q2. Who is more valuable a customer who is making small transactions everyday or the customer who makes big transactions in a month
Ans. 
It depends on the business model and goals of the company.
• Small transactions everyday can lead to consistent revenue streams and customer engagement.

• Big transactions in a month can indicate high purchasing power and potential for larger profits.

• Consider customer lifetime value, retention rates, and overall business strategy when determining value.
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Q3. What will you do as a data scientist if the sales of a store is declining
Ans. 
I would conduct a thorough analysis of the sales data to identify trends and potential causes of the decline.
• Review historical sales data to identify patterns or seasonality

• Conduct customer surveys or interviews to gather feedback

• Analyze competitor data to understand market dynamics

• Implement predictive modeling to forecast future sales

• Collaborate with marketing team to develop targeted strategies
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Q4. We have to bundle the items together in the units of 2-3 as a single units like chips of 3 packets together. how to identify which items to bundle and number of units. Create a machine learning model for i...read more
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Q5. You are working in a project, where your approach towards problem is more innovative while the rest of the team is following conventional approach. how will you convince them to follow your approach.
Ans. 
I would showcase the potential benefits and results of my innovative approach to convince the team.
• Highlight the advantages of the innovative approach such as improved efficiency, accuracy, or cost-effectiveness.

• Provide real-world examples or case studies where similar innovative approaches have led to successful outcomes.

• Encourage open discussion and collaboration within the team to explore the potential of combining ...read more
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1. A store has promotional offers how will you analyse that offers are working in their favour.
2. What data will you require if you want to predict the sales of the chocolate in a store.
3. Why data is distributed normally in linear regression.
4. Difference between linear and logistic regression
5. A person who is senior to you and you are working on the same project. But that person has very bad reputation of misbehaving and being rude to people. And he is doing same with you. What will you do?

Q1. Explain the RAG pipeline?
Ans. 
RAG pipeline is a data processing pipeline used in data science to categorize data into Red, Amber, and Green based on certain criteria.
• RAG stands for Red, Amber, Green which are used to categorize data based on certain criteria

• Red category typically represents data that needs immediate attention or action

• Amber category represents data that requires monitoring or further investigation

• Green category represents data that...read more
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Q2. Explain Confusion metrics
Ans. 
Confusion metrics are used to evaluate the performance of a classification model by comparing predicted values with actual values.
• Confusion matrix is a table that describes the performance of a classification model.

• It consists of four different metrics: True Positive, True Negative, False Positive, and False Negative.

• These metrics are used to calculate other evaluation metrics like accuracy, precision, recall, and F1 s...read more
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