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Madras Institute of Hotel Management and Catering Technology Data Scientist Interview Questions and Answers

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Interview questions from similar companies

Data Scientist Interview Questions

Infogain user image Anonymous

posted on 23 Aug 2024

Interview experience
4
Good
Difficulty level
Moderate
Duration
2-4 weeks
Result
Selected Selected

I applied via Recruitment Consulltant and was interviewed in Jul 2024.

3 Interview Rounds

1

Coding Test Round

1.Describe your project.
2.Explain machine learning concept.

2

Case Study Round

Presentation based question on case study

3

HR Round (1 Question)

  • Q1. Tell something which you haven't put in your resume
Interview experience
5
Excellent
Difficulty level
-
Duration
-
Result
-

1 Interview Round

1

Technical Round (2 Questions)

  • Q1. How do you define model Gini?
  • Q2. How to you train XG boost model

Skills evaluated in this interview

Data Scientist Interview Questions

Infogain user image Anonymous

posted on 28 Jul 2023

Interview experience
4
Good
Difficulty level
Moderate
Duration
2-4 weeks
Result
-

I applied via Naukri.com and was interviewed in Jun 2023.

3 Interview Rounds

1

Resume Shortlist Round

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
2

Technical Round (1 Question)

  • Q1. Details about Projects worked on and coding
3

One-on-one Round (1 Question)

  • Q1. Detailed projects explanation , conceptual questions about ml DL NLP tensorflow
Interview experience
5
Excellent
Difficulty level
-
Duration
-
Result
-

2 Interview Rounds

1

Aptitude Test Round

Many Mcq,s.Similar to cat exam

2

Case Study Round

Ml case study . Eg loan default prediction

Data Scientist Interview Questions

Infogain user image Anonymous

posted on 23 Aug 2024

Interview experience
4
Good
Difficulty level
Moderate
Duration
2-4 weeks
Result
Selected Selected

I applied via Recruitment Consulltant and was interviewed in Jul 2024.

3 Interview Rounds

1

Coding Test Round

1.Describe your project.
2.Explain machine learning concept.

2

Case Study Round

Presentation based question on case study

3

HR Round (1 Question)

  • Q1. Tell something which you haven't put in your resume
Interview experience
4
Good
Difficulty level
Hard
Duration
Less than 2 weeks
Result
Selected Selected

I applied via Campus Placement and was interviewed before Jul 2023.

3 Interview Rounds

1

Aptitude Test Round

Medium General Aptitude questions and technical(Big Data, Python etc.)

2

Technical Round (1 Question)

  • Q1. ML Algorithms (SVM, Random forest, bagging boosting, ridge, etc)
3

Technical Round (1 Question)

  • Q1. Deep equations and understading of DL and ML Algorithms

Skills evaluated in this interview

Interview experience
1
Bad
Difficulty level
-
Duration
-
Result
-

2 Interview Rounds

1

Group Discussion Round

About the new technologies

2

Coding Test Round

Array questions and

Interview experience
5
Excellent
Difficulty level
-
Duration
-
Result
-

1 Interview Round

1

Technical Round (2 Questions)

  • Q1. How do you define model Gini?
  • Q2. How to you train XG boost model

Skills evaluated in this interview

Data Scientist Interview Questions

Infogain user image Anonymous

posted on 28 Jul 2023

Interview experience
4
Good
Difficulty level
Moderate
Duration
2-4 weeks
Result
-

I applied via Naukri.com and was interviewed in Jun 2023.

3 Interview Rounds

1

Resume Shortlist Round

Pro Tip by AmbitionBox:
Don’t add your photo or details such as gender, age, and address in your resume. These details do not add any value.
View all tips
2

Technical Round (1 Question)

  • Q1. Details about Projects worked on and coding
3

One-on-one Round (1 Question)

  • Q1. Detailed projects explanation , conceptual questions about ml DL NLP tensorflow
Interview experience
5
Excellent
Difficulty level
-
Duration
-
Result
-

2 Interview Rounds

1

Aptitude Test Round

Many Mcq,s.Similar to cat exam

2

Case Study Round

Ml case study . Eg loan default prediction

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