LTIMindtree Data Scientist Interview Questions, Process, and Tips

Expect technical questions as well as moderate level coding questions

Q1. Data science and MLOps concepts
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Q1. Behavioral and managerial rounds
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Q1. All about my previous projects and questions based on that. Also, they checked the knowledge of Generative AI & LLM.
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Q1. All about my previous projects and questions based on that. It was a techno-managerial round which was also focused on situation based scenarios. Taken by Head of AI/ML - CoE.
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Q1. Explain confusion matrix Define what is p value Explain k means vs decion tree
Ans. 
Confusion matrix is a table used to evaluate the performance of a classification model. P value is a measure of the strength of evidence against the null hypothesis. K-means is a clustering algorithm while decision tree is a classification algorithm.
• Confusion matrix is a 2x2 table that shows the true positive, true negative, false positive, and false negative values of a classification model.

• P value is the probability ...read more
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Q1. What is nlp and explan cnn
Ans. 
NLP stands for Natural Language Processing, while CNN refers to Convolutional Neural Networks.
• NLP is a branch of artificial intelligence that focuses on the interaction between computers and humans using natural language.

• CNN is a type of deep learning algorithm commonly used for image recognition and classification tasks.

• CNNs are also used in NLP for tasks like text classification and sentiment analysis.
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Q1. Explain the importance of Model Evaluation in the process of ML pipeline
Ans. 
Model evaluation is crucial in ML pipeline to assess the performance and generalization of the model.
• Helps in selecting the best model for the given problem by comparing different models based on metrics like accuracy, precision, recall, etc.

• Prevents overfitting by checking if the model is performing well on unseen data.

• Guides in fine-tuning hyperparameters to improve model performance.

• Enables understanding of model li...read more
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Q1. Python coding questions followed by machine learning , deep learning algorithms
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Case study was given to test coding and ML skills

Q1. Based on Case Study
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Q2. Based on Case Study
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Q1. Overall interview feedback
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Q2. Salary expectations
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