McKinsey & Company Data Scientist Interview Questions and Answers

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Q1. Salary expectation
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Machine Learning, Metrics

Q1. Project Description and basic concepts related to data science and machine learning.
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Q2. Describe project u have done. Which algorithm is your favourite?
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Q1. What is homoscedasticity
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Homoscedasticity refers to the assumption that the variance of errors is constant across all levels of the independent variable.
• Homoscedasticity is a key assumption in linear regression analysis.

• It indicates that the residuals (errors) have constant variance.

• If the residuals exhibit a pattern where the spread of points increases or decreases as the predicted values increase, it violates the assumption of homoscedastici...read more
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Q2. What is biasing and what is overfitting and underfitting
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Biasing is the error due to overly simplistic assumptions in the learning algorithm. Overfitting is when a model is too complex and fits the training data too closely, leading to poor generalization. Underfitting is when a model is too simple to capture the underlying structure of the data.
• Biasing occurs when a model has high error on both training and test data due to oversimplified assumptions.

• Overfitting happens whe...read more
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Q1. Tell me about your project and your role into it
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Q2. What do you understand by Deep learning neural networks
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Deep learning neural networks are a type of artificial neural network with multiple layers, used for complex pattern recognition.
• Deep learning neural networks consist of multiple layers of interconnected nodes, allowing for more complex patterns to be learned.

• They are capable of automatically learning features from data, eliminating the need for manual feature engineering.

• Examples include Convolutional Neural Networks ...read more
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Q1. Speaking in English
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Q2. Reading Writing
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Q1. Where do you want to join?
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Q2. What is your expected ctc?
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I am looking for a competitive salary based on industry standards and my experience.
• Research industry standards for Data Scientist salaries

• Consider my level of experience and skills when determining salary expectations

• Be open to negotiation based on the overall compensation package offered
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Q1. What you can do as a Data Scientist
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Q2. Tell me your strengths and weaknesses
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Q1. Python programming.(List) SQL. Statistics.(Basics like CLT and Hypothesis Testing).
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Q2. Question related to time series analysis. Program to detect outlier in time series.
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Q1. What is neutral network? Explain back propagation. Explain difference in CNN and RNN Live coding questions were also asked
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Neural network is a computational model inspired by the way the human brain works, used for machine learning tasks.
• Neural network is a series of algorithms that attempts to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

• Backpropagation is a technique used to train neural networks by updating the weights of the network to minimize the difference between...read more
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Q1. Explain pipeline flow
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Pipeline flow is the process of moving data through a series of interconnected stages or steps in a systematic manner.
• Pipeline flow involves the sequential movement of data from one stage to another, with each stage performing a specific task or transformation.

• It helps in automating and streamlining the data processing process, making it more efficient and scalable.

• Examples of pipeline flow include data preprocessing, ...read more
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Q2. Where we are using
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