Redwood Algorithms Data Science Intern Interview Questions and Answers for Freshers

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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Q1. What is a logistic regression model?
Ans. 
Logistic regression is a statistical model used to predict the probability of a binary outcome based on one or more predictor variables.
• Logistic regression is used when the dependent variable is binary (0/1, True/False, Yes/No, etc.)

• It estimates the probability that a given input belongs to a particular category.

• The model calculates the odds of the event happening.

• It uses a logistic function to map the input values to ...read more
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Q2. Explain the random forest model.
Ans. 
Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.
• Random forest is a type of ensemble learning method.

• It builds multiple decision trees during training.

• Each tree is built using a subset of the training data and a random subset of features.

• The final prediction is made by averaging the predictions of all the individual trees.

• Random...read more
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Q3. Explain decision trees
Ans. 
Decision trees are a popular machine learning algorithm used for classification and regression tasks.
• Decision trees are a flowchart-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.

• They are easy to interpret and visualize, making them popular for exploratory data analysis.

• Decision trees can handle both numerical ...read more
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Q1. How do you see yourself in next 5 years?
Ans. 
In the next 5 years, I see myself growing into a senior data scientist role, leading projects and mentoring junior team members.
• Continuing to enhance my skills in data analysis, machine learning, and programming languages such as Python and R

• Taking on more responsibilities in project management and client interactions

• Working towards becoming a subject matter expert in a specific industry or domain

• Mentoring and guiding ...read more
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Q2. What are you 5 years back.what is the difference?
Ans. 
I was a student pursuing my undergraduate degree in Computer Science.
• 5 years back, I was studying Computer Science in college.

• Now, I have completed my degree and gained experience in data science through internships and projects.

• I have developed strong analytical and programming skills over the past 5 years.

• I have also learned new technologies and tools in the field of data science.

• I have a better understanding of real
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English, number system, grammar

Python , data science, machine learning

Python machine learning, natural language precossing

Q1. Python , Data Science, SQL
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Q2. What is python basics, libraries
Ans. 
Python basics include syntax, data types, and control structures. Libraries like NumPy, Pandas, and Matplotlib enhance data analysis and visualization.
• Python basics cover syntax, variables, data types, and control structures.

• NumPy is a library for numerical computing, providing powerful array operations.

• Pandas is a library for data manipulation and analysis, offering data structures like DataFrames.

• Matplotlib is a libr...read more
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Q3. Data science algorithms , and theory
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Q4. Sql querys, cluases
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Indian environment, village, college days

Q1. Python, data science
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5 dsa questions and 5 aptitude questions given. DSA were of medium to hard based on dp