Redwood Algorithms Data Science Intern Interview Questions and Answers

Basic DSA questions will be asked Leetcode Easy to medium

Q1. BERT vs LSTM and their speed
Ans. 
BERT is faster than LSTM due to its transformer architecture and parallel processing capabilities.
• BERT utilizes transformer architecture which allows for parallel processing of words in a sentence, making it faster than LSTM which processes words sequentially.

• BERT has been shown to outperform LSTM in various natural language processing tasks due to its ability to capture long-range dependencies more effectively.

• For exa...read more
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Q2. What is multinomial Naive Bayes theorem
Ans. 
Multinomial Naive Bayes is a classification algorithm based on Bayes' theorem with the assumption of independence between features.
• It is commonly used in text classification tasks, such as spam detection or sentiment analysis.

• It is suitable for features that represent counts or frequencies, like word counts in text data.

• It calculates the probability of each class given the input features and selects the class with the
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5 dsa questions and 5 aptitude questions given. DSA were of medium to hard based on dp

Categories of the CAT exam include Quantitative Aptitude, Verbal Ability, Data Interpretation and Logical Reasoning, and Graphical questions.

Medium level. Focus on SQL subqueries application.

Q1. Statistical questions related to different hypothesis testing
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Q2. Questions related to different machine learning model
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Q1. 1. CLT, Linear regression assumptions
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Q1. Covarience and Correlation
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Python ML short project to categories individuals based on salary

Q1. R2 and Adjusted-R2
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Q1. Two good and two bad things you thinks about Data science
Ans. 
Good and bad aspects of Data Science
• Good: Data science helps in making informed decisions based on data-driven insights

• Good: Data science can uncover valuable patterns and trends in large datasets

• Bad: Data science can be time-consuming and resource-intensive

• Bad: Data science may face challenges with data privacy and ethical considerations
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Business Related case study, signed NDA

Q1. Count number of Parameters in BERT
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Q2. 3 sum array problem
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Q1. Tell me about your projects?
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Q2. How do you approach the project if you are using logistic regression model?
Ans. 
Approach involves data preprocessing, model training, evaluation, and interpretation.
• Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.

• Split the data into training and testing sets.

• Train the logistic regression model on the training data.

• Evaluate the model using metrics like accuracy, precision, recall, and F1 score.

• Interpret the model coefficients to under...read more
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Q1. What are you future goals?
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Q2. What would you do if your interested field doesnt have any work in the company?
Ans. 
I would seek opportunities to apply my skills in related fields within the company.
• Explore other departments or teams within the company that may have projects related to my field of interest

• Offer to collaborate with colleagues in different departments to bring a new perspective to their projects

• Seek out professional development opportunities to expand my skills and knowledge in related areas
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Questions related to basic coding were asked, and some background on projects and discussions alongside maths and statistics concepts

Q1. Questions related to projects done at my previous company
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Q1. Questions related to my work at previous company
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Q2. ML system design use case type of discussion
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