Info Edge 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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Round duration - 60 minutes
Round difficulty - Easy

Timing: 8:00-9:00 PM
There was 1 MCQ and 3 SQL queries. The platform was easy to use and navigate.

Round duration - 30 minutes
Round difficulty - Medium

Timing: 11 AM-11:30 AM
The interviewer was very kind and helpful. He helped me by giving hints whenever I was stuck.

Round duration - 30 minutes
Round difficulty - Medium

Timing: 12:15 PM to 12:45 PM.
The interviewer was very helpful and friendly. He was more interested in my approach rather than the final answer.

Q1. What new feature would you like to add to Uber?
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Round duration - 30 minutes
Round difficulty - Hard

Timing: 3:00PM to 3:30PM
This was Hiring Manager round. He asked me case study type of questions.

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. Explain past projects
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Q1. Explain resume points
Ans. 
Resume points are concise descriptions of your work experience, skills, and achievements listed on your resume.
• Resume points should be clear, specific, and quantifiable.

• Use action verbs to start each point, such as 'developed', 'implemented', 'analyzed'.

• Include relevant metrics or results to demonstrate impact, such as 'increased sales by 20%' or 'reduced processing time by 30%'.
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Q1. Asked firstly some question over my resume then 2 case study type problems where i have to improve the recommdation system for zomato, and finally one coding question based on two pointer
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Q1. Write a SQL query to find all duplicate emails in a table named person
Ans. 
SQL query to find duplicate emails in a table named person
• Use GROUP BY and HAVING clause to group emails and count their occurrences

• Select only those emails which have count greater than 1

• Example: SELECT email, COUNT(*) FROM person GROUP BY email HAVING COUNT(*) > 1;
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Q2. Given a weather table, write a sql query to find all date's ids with higher temperature compared to it's previous dates
Ans. 
SQL query to find date ids with higher temperature compared to previous dates in weather table
• Use self join to compare temperature of current date with previous dates

• Order the table by date to ensure correct comparison

• Select date ids where temperature is higher than previous dates
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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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