Highbrow Data Science Analyst Interview Questions and Answers

Q1. What is bias v variance tradeoff
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Q2. When should bagging v boosting techniques be used
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Q1. What is the Naive Bayes Theorem?
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Q1. Implement backpropagation algorithm in python
Ans. 
Backpropagation algorithm is used to train neural networks by calculating gradients of the loss function with respect to the weights.
• Initialize weights randomly

• Forward pass to calculate predicted output

• Calculate loss using a loss function like mean squared error

• Backward pass to calculate gradients using chain rule

• Update weights using gradients and a learning rate
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Q2. How do you ensure that retrieval in RAG give the correct documents? Explain any project involving generative AI.
Ans. 
Ensuring correct document retrieval in RAG and discussing a project involving generative AI.
• Utilize a combination of retrieval and generative models in RAG to ensure correct document retrieval.

• Implement techniques such as fine-tuning the models, using diverse training data, and optimizing hyperparameters.

• Regularly evaluate the performance of the models through metrics like precision, recall, and F1 score.

• Provide exampl...read more
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More in-depth discussion about the projects. Why did you take certain decisions etc.

Q1. Basic questions in python
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Q2. Questions in Ml
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Good execellnt and well done

Q1. Tell me your self
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Q2. Tell me why cogniznt
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1. There were two coding problems from leetcode (1 easy and 1 mediam).
2. One was machine learning problem having hingh weightage. They given a data and we have to make a linear regression model on data and submit prediction.

Q1. It was a technical interview . Mostly focussed on data science machine learning alogorithms. Also do prepare case studies.
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Q2. Explain Random forest. What is gini impurity.
Ans. 
Random forest is an ensemble learning method that constructs a multitude of decision trees and outputs the mode of the classes. Gini impurity is a measure of impurity or randomness used in decision trees.
• Random forest is a collection of decision trees that are trained on different subsets of the data.

• Each decision tree in the forest is trained on a random subset of the features.

• The final prediction is made by taking th...read more
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Q3. One coding question (best time to buy and sell stock leetcode easy version).
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Q4. One case study data science senario based case study.
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