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Mu Sigma Trainee Scientist Interview Questions and Answers
Mu Sigma Trainee Scientist Interview Experiences
2 interviews found
Selected
I applied via campus placement at Lovely Professional University (LPU) and was interviewed in Apr 2024. There were 2 interview rounds.
Contains questions of quant reasoning mental ability
(4 Questions)
- Q1. Situation based questns
- Q2. Python code for data frames
- Ans.
Python code for data frames in pandas library
Import pandas library
Create a data frame using pd.DataFrame()
Access and manipulate data using various methods like loc, iloc, and groupby
- Q3. Sql order of execution
- Ans.
SQL order of execution determines the sequence in which different clauses are processed in a query.
SQL order of execution: FROM -> WHERE -> GROUP BY -> HAVING -> SELECT -> ORDER BY.
Joins are processed before WHERE clause.
Aggregate functions are processed after WHERE clause but before SELECT clause.
Subqueries are processed from innermost to outermost.
- Q4. Sql question on groupby having and order by
Skills evaluated in this interview
Netla Preethi
Selected
I applied via campus placement at Sastra University and was interviewed before Mar 2023. There were 3 interview rounds.
There were some basic questions related to time, distance and also asked to write about a topic
They'll give you a topic and ask you to speak
(2 Questions)
- Q1. They ask you questions based on your resume, some SQL questions, guesstimates
- Q2. Joins in SQL, order of execution
- Ans.
Joins in SQL are used to combine rows from two or more tables based on a related column between them.
Joins are executed after the FROM clause and before the WHERE clause in SQL queries.
Common types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
Example: SELECT * FROM table1 INNER JOIN table2 ON table1.column = table2.column;
Interview Preparation Tips
Skills evaluated in this interview
Interview questions from similar companies
I applied via Naukri.com and was interviewed in Dec 2024. There was 1 interview round.
(2 Questions)
- Q1. Explain any ML model.
- Q2. Create Dataframe from two lists.
Interview Preparation Tips
- Python
- pandas
- ML
I applied via Referral and was interviewed in Aug 2024. There were 2 interview rounds.
(5 Questions)
- 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
- 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.
- 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
- 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...
- 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 ...
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?
Interview Preparation Tips
Skills evaluated in this interview
I applied via campus placement at National Institute of Technology (NIT), Warangal
1 hour aptitude test
(1 Question)
- Q1. What is one hot encoding
(1 Question)
- Q1. What is your long term goal
Data Scientist Interview Questions & Answers
Acuity Knowledge Partners
Anonymous
posted on 11 Sep 2024
I applied via Company Website and was interviewed in Aug 2024. There was 1 interview round.
(2 Questions)
- 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...
- 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...
Skills evaluated in this interview
DSA and ML, AI, Coding question
(1 Question)
- Q1. Case study which was easy
(1 Question)
- Q1. In depth questions on ML
Selected
I applied via Recruitment Consulltant and was interviewed in Apr 2024. There was 1 interview round.
SQL, Python coding …
Selected
I applied via Referral and was interviewed in May 2024. There were 3 interview rounds.
I was asked to write SQL queries for 3rd highest salary of the employee, some name filtering, group by tasks.
Python code to find the index of the maximum number without using numpy.
(1 Question)
- Q1. Explain the Project undertaken during the research and follow-up questions
(1 Question)
- Q1. Write pandas query to separate the names as first and last name from the full name. Drop the duplicate columns and also the missing values. Write output for the Python code. Write SQL query to retrieve t...
- Ans.
Answering questions related to data science concepts and techniques.
Recall is the ratio of correctly predicted positive observations to the total actual positives. Precision is the ratio of correctly predicted positive observations to the total predicted positives.
To reduce variance in an ensemble model, techniques like bagging, boosting, and stacking can be used. Bagging involves training multiple models on different ...
Interview Preparation Tips
- Python
- Pandas
- SQL
- Machine Learning
Skills evaluated in this interview
Selected
I applied via Referral and was interviewed before May 2023. There were 2 interview rounds.
(1 Question)
- Q1. Sql, Python programming Questions
(1 Question)
- Q1. Retail, CPG based case study questions like offer allocation method for loyal customers
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