McKinsey & Company Data Science Intern 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 xgboost algorithm
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XGBoost is a powerful machine learning algorithm known for its speed and performance in handling large datasets.
• XGBoost stands for eXtreme Gradient Boosting

• It is an implementation of gradient boosted decision trees designed for speed and performance

• XGBoost is widely used in machine learning competitions and real-world applications

• It can handle missing data, regularization, parallel processing, and custom optimization o...read more
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Q1. Ml based questions, linear regression, classifications, regularizations, clustering based questions
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Q2. Coding test based on a data frame
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Q1. Techno managerial round
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Q2. Lots of ML use case based questions
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Q1. Interview with Director
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Q2. Questions based on projects done on cv
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Combination logic on python

Q1. Explain classification
Ans. 
Classification is a machine learning technique used to categorize data into different classes or categories based on past observations.
• Classification involves training a model on labeled data to predict the class of new, unseen data points.

• Common algorithms for classification include logistic regression, decision trees, support vector machines, and k-nearest neighbors.

• Examples of classification tasks include spam email...read more
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Machine Learning, Metrics

Q1. 1. What is the role of beta value in Logistic regression? 2. What is bias variance trade off? 3. How did you decide on the list of variables that would be used in a model?
Ans. 
Beta value in logistic regression measures the impact of independent variables on the log odds of the dependent variable.
• Beta value indicates the strength and direction of the relationship between the independent variables and the log odds of the dependent variable.

• A positive beta value suggests that as the independent variable increases, the log odds of the dependent variable also increase.

• A negative beta value sugges...read more
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1. You are the data scientist of a digital store. You have to recommend top 10 products to a customer. What variables and techniques will you use to recommend the top 10 products?

Q1. What is Linear regression
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Q2. What li list and tuple
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Q3. Previous company project experience
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DSA was asked. And general coding language questions were asked. Previous experience based questions were asked.

Machine Learning, Generative AI, Deep learning interview questions. 2 Coding problems based on Algorithms.

Q1. One systems design question was there.
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Q2. Situations based problems was there.
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Q3. Questions on Past experiences.
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