TCS Data Scientist Interview Questions, Process, and Tips

Q1. What is confusion matrix?
Ans. 
Confusion matrix is a table used to evaluate the performance of a classification model.
• It is a 2x2 matrix that shows the counts of true positive, true negative, false positive, and false negative predictions.

• It is used to calculate metrics like accuracy, precision, recall, and F1 score.

• Example: TP=100, TN=50, FP=10, FN=5.
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Q2. Explain similarity matrix algo?
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Similarity matrix algo is a method to quantify the similarity between data points in a dataset.
• It calculates the similarity between each pair of data points in a dataset and represents it in a matrix form.

• Common similarity measures used include cosine similarity, Euclidean distance, and Jaccard similarity.

• The diagonal of the matrix usually contains 1s as each data point is perfectly similar to itself.

• The values in the ...read more
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Q1. Why should we select you?
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Q2. What are your expectations from the org?
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Q1. Explain ur projects in Detail
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Q2. Steps involved in Machine Learning Problem Statement
Ans. 
Steps involved in Machine Learning Problem Statement
• Define the problem statement and goals

• Collect and preprocess data

• Select a machine learning model

• Train the model on the data

• Evaluate the model's performance

• Fine-tune the model if necessary

• Deploy the model for predictions
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Q1. Asked from resume about RAG
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Q1. Retraining GEN AI model
Ans. 
Retraining GEN AI model involves updating the model with new data to improve its accuracy and performance.
• Retraining is necessary to keep the model up-to-date with new information.

• New data is used to fine-tune the model's parameters and improve its predictions.

• Retraining may involve adjusting hyperparameters, adding more layers, or changing the architecture.

• Examples: retraining a language model with new text data, retr...read more
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Q2. DEployment of Model in MLFlow
Ans. 
MLFlow allows for easy deployment of machine learning models.
• MLFlow provides a simple way to deploy models using the mlflow models serve command.

• Models can be deployed locally or to a cloud-based server for production use.

• MLFlow also supports model versioning and tracking for easy management of deployed models.
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Duration 1 Hr. Difficulty- Medium.

Q1. Introduce yourself.
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Q2. Explain your final year project.
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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?
Ans. 
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
Ans. 
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
Ans. 
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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