E42 Data Scientist Interview Questions and Answers

Forecasting problem - Predict daily sku level sales

Q1. What is difference between bias and variance
Ans. 
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
• Bias is the error introduced by approximating a real-world problem, leading to underfitting.

• Variance is the error introduced by modeling the noise in the training data, leading to overfitting.

• High bias can cause a model to miss relevant relationships between features and target variable.

• High variance can cause a model to ...read more
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Q2. Parametric vs non parametruc model
Ans. 
Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
• Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.

• Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.

• Examples of parametric models inc...read more
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Q1. Mostly about projects ml, nlp, python code would be asked
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Q1. It would be focused on technical stuff in detail ml, nlp, deployments etc
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Q1. Organisation fit
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Q1. What is overfitting in machine learning?
Ans. 
Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
• Overfitting happens when a model is too complex and captures noise in the training data.

• It leads to poor performance on unseen data as the model fails to generalize well.

• Techniques to prevent overfitting include cross-validation, regularization, and early stopping.

• ...read more
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Q2. Overfitting accurs when a model learns the details.......etc
Ans. 
Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.
• Overfitting happens when a model is too complex and captures noise in the training data.

• It leads to poor generalization and high accuracy on training data but low accuracy on new data.

• Techniques to prevent overfitting include cross-validation, regularization, and...read more
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Forecasting problem - Predict daily sku level sales

Q1. What is difference between bias and variance
Ans. 
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
• Bias is the error introduced by approximating a real-world problem, leading to underfitting.

• Variance is the error introduced by modeling the noise in the training data, leading to overfitting.

• High bias can cause a model to miss relevant relationships between features and target variable.

• High variance can cause a model to ...read more
Answered by AI
Add your answer
Q2. Parametric vs non parametruc model
Ans. 
Parametric models make strong assumptions about the form of the underlying data distribution, while non-parametric models do not.
• Parametric models have a fixed number of parameters, while non-parametric models have a flexible number of parameters.

• Parametric models are simpler and easier to interpret, while non-parametric models are more flexible and can capture complex patterns in data.

• Examples of parametric models inc...read more
Answered by AI
Add your answer

Q1. Machine Learning
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Q1. Machine Learning
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Approach check for multiple case studies

Q1. Mostly about projects ml, nlp, python code would be asked
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Q1. It would be focused on technical stuff in detail ml, nlp, deployments etc
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Q1. Organisation fit
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