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360DigiTMG Data Scientist Interview Questions, Process, and Tips

Updated 4 Apr 2024

360DigiTMG Data Scientist Interview Experiences

1 interview found

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via LinkedIn and was interviewed in Mar 2024. There was 1 interview round.

Round 1 - HR 

(4 Questions)

  • Q1. Use R as a calculator to compute the following values. After you do so, cut and paste your input and output from R to Word. Add numbering in Word to identify each part of each problem.
  • Ans. 

    Using R as a calculator to compute values for a Data Scientist interview question.

    • Use R's console to input mathematical expressions and compute values.

    • Make sure to follow the order of operations (PEMDAS) when entering expressions.

    • Use functions like 'sqrt()' for square roots and 'exp()' for exponentiation.

    • Remember to assign variables using the '<-' operator before using them in calculations.

  • Answered by AI
  • Q2. Assign 10:50 to d, use R to compute the following statistics of d
  • Ans. 

    Compute statistics of a given time value in R

    • Use lubridate package to work with time data in R

    • Calculate summary statistics like mean, median, min, max, and standard deviation

    • Convert the time value to a time object before performing calculations

  • Answered by AI
  • Q3. Use R to create the following two matrices and do the indicated matrix multiplication.
  • Ans. 

    Using R to create two matrices and perform matrix multiplication.

    • Create two matrices using matrix() function in R.

    • Use %*% operator for matrix multiplication.

    • Ensure the dimensions of the matrices are compatible for multiplication.

  • Answered by AI
  • Q4. Run the following kNN classifier for the iris data. Can you interpret the output?
  • Ans. 

    The kNN classifier is run on the iris data to make predictions based on nearest neighbors.

    • kNN classifier is a type of supervised machine learning algorithm that can be used for classification tasks.

    • The output will be the predicted class labels for the iris data based on the nearest neighbors.

    • Interpreting the output involves understanding how the algorithm has classified the data points.

  • Answered by AI

Interview Preparation Tips

Topics to prepare for 360DigiTMG Data Scientist interview:
  • Data Science
Interview preparation tips for other job seekers - Take deep knowledge

Skills evaluated in this interview

Interview questions from similar companies

Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. What is overfitting in machine learning?
  • Ans. 

    Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.

    • Overfitting happens when a model is too complex and captures noise in the training data.

    • It leads to poor performance on unseen data as the model fails to generalize well.

    • Techniques to prevent overfitting include cross-validation, regularization, and early stopping.

    • ...

  • Answered by AI
  • Q2. Overfitting accurs when a model learns the details.......etc
  • Ans. 

    Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.

    • Overfitting happens when a model is too complex and captures noise in the training data.

    • It leads to poor generalization and high accuracy on training data but low accuracy on new data.

    • Techniques to prevent overfitting include cross-validation, regularization, and...

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Research the company before interview.
Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • Q1. What is overfitting in machine learning?
  • Ans. 

    Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.

    • Overfitting happens when a model is too complex and captures noise in the training data.

    • It leads to poor performance on unseen data as the model fails to generalize well.

    • Techniques to prevent overfitting include cross-validation, regularization, and early stopping.

    • ...

  • Answered by AI
  • Q2. Overfitting accurs when a model learns the details.......etc
  • Ans. 

    Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.

    • Overfitting happens when a model is too complex and captures noise in the training data.

    • It leads to poor generalization and high accuracy on training data but low accuracy on new data.

    • Techniques to prevent overfitting include cross-validation, regularization, and...

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Research the company before interview.

360DigiTMG Interview FAQs

How many rounds are there in 360DigiTMG Data Scientist interview?
360DigiTMG interview process usually has 1 rounds. The most common rounds in the 360DigiTMG interview process are HR.
What are the top questions asked in 360DigiTMG Data Scientist interview?

Some of the top questions asked at the 360DigiTMG Data Scientist interview -

  1. Use R as a calculator to compute the following values. After you do so, cut and...read more
  2. Run the following kNN classifier for the iris data. Can you interpret the outp...read more
  3. Assign 10:50 to d, use R to compute the following statistics o...read more

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360DigiTMG Data Scientist Salary
based on 14 salaries
₹2.3 L/yr - ₹7.2 L/yr
66% less than the average Data Scientist Salary in India
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360DigiTMG Data Scientist Reviews and Ratings

based on 5 reviews

3.3/5

Rating in categories

5.0

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3.5

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2.8

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3.0

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3.5

Company culture

2.8

Promotions/Appraisal

2.8

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