Nagarro Data Scientist Interview Questions, Process, and Tips for Experienced

Q1. Model evaluation and performance metrices
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Q2. Explaination of bagging and boosting techniques
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Bagging and boosting are ensemble techniques used to improve the accuracy of machine learning models.
• Bagging involves training multiple models on different subsets of the training data and then combining their predictions through voting or averaging.

• Boosting involves iteratively training models on the same data, with each subsequent model focusing on the samples that the previous models misclassified.

• Bagging reduces va...read more
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Q1. NLP and Computer Vision based Question were asked.
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Q1. General Questions on Machine Learning and deployments technique
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Q1. Salary Negotiations and joining discussion
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Q1. Question on NER models
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Q2. Question from project
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Q1. Critical thinking questions
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Q2. Case studies based questions
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NER training using deep learning

Q1. Describe the approach taken for assignment
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I approach assignments by breaking them down into smaller tasks, setting deadlines, and regularly checking progress.
• Break down the assignment into smaller tasks to make it more manageable

• Set deadlines for each task to stay on track

• Regularly check progress to ensure everything is on schedule

• Seek feedback from colleagues or supervisors to improve the quality of work
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Q2. Scenario based questions
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Q1. Question related to CNN in details, Different ML algorithms, Questions related to pandas
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Q1. Question related to MLOps, GitHub
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Q1. Salary expectations
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Q1. Fundamentals of ML, classical ML
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Fundamentals of classical machine learning
• Classical machine learning involves algorithms that learn from data and make predictions or decisions.

• Common algorithms include linear regression, decision trees, support vector machines, and k-nearest neighbors.

• Key concepts include training data, testing data, model evaluation, and hyperparameter tuning.

• Classical ML is often used for tasks like classification, regression, clus
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Q1. Research paper discussion
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