Carbynetech Data Scientist Interview Questions and Answers

Q1. Metrics and related questions
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Test was conducted on datacamp assessments. Overall, there were three tests.
1. Stats test
2. ML test
3. Python/coding test

Q1. Questions on ML techniques and practices, how to handle large data in python, lots of logical questions and handling overfitting, underfitting, etc in model building.
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Q1. Machine learning algorithms.
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Machine learning algorithms are tools used to analyze data, identify patterns, and make predictions without being explicitly programmed.
• Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.

• Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.

• These algorithms require training data to learn patte...read more
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Q2. Credit risk life cycle
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Q3. Pandas related questions
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Q1. Steps of developing a credit risk model
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Developing a credit risk model involves several steps to assess the likelihood of a borrower defaulting on a loan.
• 1. Define the problem and objectives of the credit risk model.

• 2. Gather relevant data such as credit history, income, debt-to-income ratio, etc.

• 3. Preprocess the data by handling missing values, encoding categorical variables, and scaling features.

• 4. Select a suitable machine learning algorithm such as logi...read more
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Q2. Pandas related questions
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Q3. Bagging and Boosting
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Q1. Explain AIC and BIC
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AIC and BIC are statistical measures used for model selection in the context of regression analysis.
• AIC (Akaike Information Criterion) is used to compare the goodness of fit of different models. It penalizes the model for the number of parameters used.

• BIC (Bayesian Information Criterion) is similar to AIC but penalizes more heavily for the number of parameters, making it more suitable for model selection when the focus...read more
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Q2. Difference between xgboost and lightgbm
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XGBoost is a popular gradient boosting library while LightGBM is a faster and more memory-efficient alternative.
• XGBoost is known for its accuracy and performance on structured/tabular data.

• LightGBM is faster and more memory-efficient, making it suitable for large datasets.

• LightGBM uses a histogram-based algorithm for splitting whereas XGBoost uses a level-wise tree growth strategy.
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Q3. Bagging and boosting
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