TCS Data Scientist Interview Questions, Process, and Tips

Basic coding questions

Q1. What is F1-score
Ans. 
F1-score is a measure of a model's accuracy that considers both precision and recall.
• F1-score is the harmonic mean of precision and recall.

• It ranges from 0 to 1, where 1 is the best possible F1-score.

• F1-score is useful when you want to balance precision and recall in your model evaluation.
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Q2. What are different ML algorithms?
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Different ML algorithms include linear regression, decision trees, random forests, support vector machines, and neural networks.
• Linear regression: used for predicting continuous values based on input features.

• Decision trees: used for classification and regression tasks by splitting data into branches based on feature values.

• Random forests: ensemble method using multiple decision trees for improved accuracy.

• Support vect...read more
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Q1. Overfitting vs Underfitting
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Overfitting occurs when a model learns the training data too well, while underfitting occurs when a model fails to capture the underlying patterns in the data.
• Overfitting: Model is too complex and learns noise in the training data.

• Underfitting: Model is too simple and fails to capture the underlying patterns.

• Overfitting can lead to poor generalization and high variance.

• Underfitting can lead to high bias and poor perfor...read more
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Q2. ML models in resume
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ML models should be included in a Data Scientist's resume.
• Include a section in your resume highlighting the ML models you have worked with.

• Mention the specific ML algorithms and techniques you have used.

• Provide examples of projects where you have successfully applied ML models.

• Highlight any notable achievements or results obtained using ML models.

• Demonstrate your understanding of model evaluation and validation techniq
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Q3. Recall vs Precision
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Recall and Precision are evaluation metrics used in classification tasks to measure the performance of a model.
• Recall measures the ability of a model to find all the relevant instances in a dataset.

• Precision measures the ability of a model to correctly identify only the relevant instances.

• Recall and Precision are often used together to evaluate the trade-off between completeness and correctness in a model's predictions...read more
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Q1. Why did you choose recall for a particular ML model?
Ans. 
Recall was chosen for the ML model to prioritize minimizing false negatives.
• Chose recall to focus on identifying all relevant cases, even if it means more false positives

• In scenarios where missing a positive case is more costly than incorrectly labeling a negative case

• Commonly used in medical diagnosis to ensure all potential cases are identified
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Basically, it was a managerial round

Q1. What is low bias and high variance
Ans. 
Low bias and high variance refer to the trade-off between model complexity and generalization ability.
• Low bias refers to a model that makes strong assumptions about the data, leading to high accuracy on training data but potentially poor performance on unseen data.

• High variance refers to a model that is very sensitive to small fluctuations in the training data, leading to overfitting and poor generalization.

• Finding the...read more
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