India Nippon Electricals Data Scientist Interview Questions and Answers

Q1. Can you explain your project in detail?
Ans. 
Developed a predictive model to forecast customer churn for a telecommunications company.
• Used machine learning algorithms such as logistic regression and random forest.

• Performed data preprocessing, feature engineering, and model evaluation.

• Collaborated with business stakeholders to understand requirements and present findings.

• Achieved 85% accuracy in predicting customer churn.

• Implemented the model into the company's CR
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Q2. What skill-related questions did you encounter during the interview that were based on your resume?
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Q1. What is the explanation of the role?
Ans. 
Data Analysts are responsible for collecting, processing, and analyzing data to help organizations make informed decisions.
• Collect and organize data from various sources

• Clean and preprocess data to ensure accuracy

• Analyze data using statistical techniques and tools

• Create visualizations and reports to present findings

• Provide insights and recommendations based on data analysis

• Collaborate with stakeholders to understand bu
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Q2. What are your salary expectations?
Ans. 
I am looking for a competitive salary based on my experience, skills, and the market rate for Data Analyst roles.
• Research the average salary range for Data Analyst positions in the specific location and industry

• Consider your level of experience, education, and skills when determining your salary expectations

• Be prepared to negotiate based on the benefits package offered by the employer

• Provide a salary range rather than ...read more
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Basic qaunts, logical, excel

Q1. Sql, excel, tableau questions
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Q1. About yourself in detail
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Q2. Hr related questions, salary expectation, stregth, weakness,
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Q1. Deep Dive into Power Bi projects Basic Sql queries Python certification if any Explain the projects with deep dive into analytics
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Q1. Difference between CNN and MLP
Ans. 
CNN is used for image recognition while MLP is used for general classification tasks.
• CNN uses convolutional layers to extract features from images while MLP uses fully connected layers.

• CNN is better suited for tasks that require spatial understanding like object detection while MLP is better for tabular data.

• CNN has fewer parameters than MLP due to weight sharing in convolutional layers.

• CNN can handle input of varying
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Q1. About Resume and basic ML
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