SJRS College Intern Interview Questions and Answers

Few basic IQ questions/ Gmat style test

Duration is 60 mins and topics are aptitude, reasoning, verbal.

How online education help students

Q1. Tell me about yourself ?
Add your answer
Q2. What do you know about excelR?
Add your answer
Q3. Why do you choose excelR?
Add your answer

Q1. Tell me about your self briefly?
Ans. 
I am a motivated marketing student with a passion for creativity and strategic thinking.
• Currently pursuing a degree in Marketing

• Experience in social media marketing

• Strong analytical skills

• Creative thinker

• Excellent communication skills
Answered by AI
Add your answer
Q2. Where are you currently studying? Are you ok to with us?
Ans. 
I am currently studying at XYZ University and I am excited about the opportunity to intern with your company.
• Currently studying at XYZ University

• Excited about the opportunity to intern with the company
Answered by AI
Add your answer

Q1. Swap two numbers
Ans. 
To swap two numbers, store one number in a temporary variable, then assign the second number to the first, and finally assign the temporary variable to the second number.
• Store one number in a temporary variable

• Assign the second number to the first number

• Assign the temporary variable to the second number
Answered by AI
Add your answer
Q2. Print hello world
Ans. 
Printing hello world is a common introductory programming task.
• Use a print statement in the programming language of your choice.

• For example, in Python: print('Hello, World!')

• In Java: System.out.println('Hello, World!')
Answered by AI
Add your answer

Q1. Background, aspirations, strengths and weakness
Add your answer

Few basic IQ questions/ Gmat style test

Q1. Introduction why you are joining data science role what is the experience
Add your answer
Q2. Data science is most frequent technology And growing abroad for the future and take decision easy way
Add your answer

Why using pandas numpy matplot lib and seaborn library

I have done data science all assignment completely
like linear regression multiple linear regression logistics regression knn model random forest svm model decesion tree neural network text mining clustering method etc