Pratt & Whitney Data Scientist Interview Questions and Answers

Q1. Picks one of the Projects to discuss the structure and methodology.
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Q2. Reverse a String
Ans. 
Reverse a given string
• Use built-in functions like reverse() or slice() in Python

• Iterate through the string in reverse order and append characters to a new string

• Convert the string to an array of characters, reverse the array, and join it back into a string
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Q3. Basic ML Questions about Resume Projects
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Q4. What is Regularization?
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Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty term to the loss function.
• Regularization helps to reduce the complexity of the model by penalizing large coefficients.

• It adds a penalty term to the loss function, such as L1 (Lasso) or L2 (Ridge) regularization.

• Regularization helps to improve the generalization of the model by discouraging overfitting.

• Examples of re...read more
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Q5. Basics about Forescasting.
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Hackerank Assignment

Q1. Coding and ML questions
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Q1. About projects and questions related to that
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Basic coding questions

Q1. Explain in detail about current project? Reason for choosing the algorithm, evaluation metrics and analysis of data
Ans. 
Developed a predictive model for customer churn using logistic regression
• Chose logistic regression for its interpretability and ability to handle binary outcomes

• Evaluated model performance using accuracy, precision, recall, and F1 score

• Analyzed customer data including demographics, purchase history, and customer service interactions
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Q2. Python and sql coding questions - basic
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Q3. Linear regression, xgboost, precision, recall, F1 Score,Correlation
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Python programming for 45 for minutes

Q1. What are the hyperparameters of SVM
Ans. 
Hyperparameters of SVM include C, kernel, gamma, degree, and coef0.
• C: Regularization parameter that controls the trade-off between achieving a low error on the training data and minimizing model complexity.

• Kernel: Specifies the type of hyperplane used to separate the data.

• Gamma: Kernel coefficient for 'rbf', 'poly', and 'sigmoid' kernels.

• Degree: Degree of the polynomial kernel function.

• Coef0: Independent term in kernel...read more
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Q2. How to tune that hyperparameters
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Hyperparameters can be tuned using techniques like grid search, random search, and Bayesian optimization.
• Grid search: Exhaustively search through a specified subset of hyperparameters.

• Random search: Randomly sample hyperparameter combinations.

• Bayesian optimization: Use probabilistic models to predict the performance of different hyperparameter configurations.
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Q1. Basics of linear regression
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Q1. Machine learning in depth
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Q1. The hr scheduled a call with the analytics manager ,who asked about my experience and general details ,some background about projects and previous work ,why this job etc .30-40 minutes conversation.
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Q2. Just a casual brief conversation
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Q1. Second round was with a senior analyst ,who asked 5-6 sql related questions , problems to solve etc ,tables to create ,joins ,window functions etc basic sql
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Q2. Very friendly interviewer , you are allowed to google the syntax
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Q1. Another round with the analytics head , case study ,problem solving ,logical reasoning etc.
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