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Info Edge Data Science Intern Interview Questions and Answers

Updated 11 Jun 2023

Info Edge Data Science Intern Interview Experiences

1 interview found

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via campus placement at Indian Institute of Technology (IIT), Kharagpur and was interviewed before Jun 2022. There were 6 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - Technical 

(1 Question)

  • Q1. 1. CLT, Linear regression assumptions
Round 3 - Technical 

(1 Question)

  • Q1. Covarience and Correlation
Round 4 - Coding Test 

Python ML short project to categories individuals based on salary

Round 5 - Technical 

(1 Question)

  • Q1. R2 and Adjusted-R2
Round 6 - HR 

(1 Question)

  • 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

  • Answered by AI

Skills evaluated in this interview

Interview questions from similar companies

Interview experience
4
Good
Difficulty level
-
Process Duration
Less than 2 weeks
Result
Selected Selected
Round 1 - One-on-one 

(3 Questions)

  • 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 ...

  • Answered by AI
  • 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...

  • Answered by AI
  • 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 ...

  • Answered by AI
Round 2 - HR 

(2 Questions)

  • 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 ...

  • Answered by AI
  • 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

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Coding Test 

Basic DSA questions will be asked Leetcode Easy to medium

Round 2 - Technical 

(2 Questions)

  • 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...

  • Answered by AI
  • 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

  • Answered by AI

Skills evaluated in this interview

I was interviewed in Dec 2021.

Round 1 - Coding Test 

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 2 - Video Call 

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 3 - Video Call 

(1 Question)

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?
Round 4 - Video Call 

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.

Interview Preparation Tips

Professional and academic backgroundI applied for the job as Data Science Intern in BangaloreEligibility criteriaNo criteriaUber interview preparation:Topics to prepare for the interview - Machine Learning Algorithms, SQL Queries, Python, Data Mining, Data Visualization, Descriptive and Inferential Statistics, Random Variables and Probability Distributions.Time required to prepare for the interview - 3.5 monthsInterview preparation tips for other job seekers

Tip 1 : Practice SQL and python coding questions using online coding platforms.
Tip 2 : Get in-depth theoretical knowledge about all the machine learning algorithms.
Tip 3 : Strengthen your statistics understanding.

Application resume tips for other job seekers

Tip 1 : Highlight your skills properly
Tip 2 : Have thorough understanding about everything written in the resume

Final outcome of the interviewSelected
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Case Study 

Business Related case study, signed NDA

Round 2 - Technical 

(2 Questions)

  • Q1. Count number of Parameters in BERT
  • Q2. 3 sum array problem

Interview Preparation Tips

Interview preparation tips for other job seekers - NA
Interview experience
3
Average
Difficulty level
Easy
Process Duration
4-6 weeks
Result
-

I applied via Referral and was interviewed before Oct 2023. There were 2 interview rounds.

Round 1 - One-on-one 

(1 Question)

  • Q1. Explain past projects
Round 2 - One-on-one 

(1 Question)

  • 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%'.

  • Answered by AI
Interview experience
3
Average
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Referral and was interviewed in Sep 2023. There was 1 interview round.

Round 1 - Technical 

(1 Question)

  • 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

I applied via Recruitment Consulltant and was interviewed in Jun 2022. There were 2 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - Technical 

(2 Questions)

  • 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;

  • Answered by AI
  • 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

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Questions will be asked about machine learning models and SQL query.

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
-
Process Duration
Less than 2 weeks
Result
Selected Selected
Round 1 - One-on-one 

(3 Questions)

  • 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 ...

  • Answered by AI
  • 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...

  • Answered by AI
  • 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 ...

  • Answered by AI
Round 2 - HR 

(2 Questions)

  • 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 ...

  • Answered by AI
  • 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

  • Answered by AI

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Selected Selected

I was interviewed in Dec 2023.

Round 1 - Aptitude Test 

English, number system, grammar

Round 2 - Coding Test 

Python , data science, machine learning

Round 3 - Assignment 

Python machine learning, natural language precossing

Round 4 - HR 

(4 Questions)

  • Q1. Python , Data Science, SQL
  • 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...

  • Answered by AI
  • Q3. Data science algorithms , and theory
  • Q4. Sql querys, cluases
Round 5 - Group Discussion 

Indian environment, village, college days

Round 6 - HR 

(1 Question)

  • Q1. Python, data science

Info Edge Interview FAQs

How many rounds are there in Info Edge Data Science Intern interview?
Info Edge interview process usually has 6 rounds. The most common rounds in the Info Edge interview process are Technical, Coding Test and HR.
What are the top questions asked in Info Edge Data Science Intern interview?

Some of the top questions asked at the Info Edge Data Science Intern interview -

  1. Two good and two bad things you thinks about Data scie...read more
  2. 1. CLT, Linear regression assumpti...read more
  3. Covarience and Correlat...read more

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Info Edge Data Science Intern Interview Process

based on 1 interview

Interview experience

5
  
Excellent
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