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Great Learning Data Scientist Interview Questions, Process, and Tips

Updated 27 Oct 2024

Top Great Learning Data Scientist Interview Questions and Answers

View all 11 questions

Great Learning Data Scientist Interview Experiences

4 interviews found

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
-
Result
No response

I was interviewed in Sep 2024.

Round 1 - Technical 

(2 Questions)

  • Q1. Greatset number from an array
  • Ans. 

    Find the greatest number from an array of strings.

    • Convert the array of strings to an array of integers.

    • Use a sorting algorithm to sort the array in descending order.

    • Return the first element of the sorted array as the greatest number.

  • Answered by AI
  • Q2. Questions related to Hypothesis testing

Skills evaluated in this interview

Interview experience
1
Bad
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
No response

I applied via Monster and was interviewed in Oct 2023. There were 5 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 Resume tips
Round 2 - Coding Test 

Python Coding Test to test general knowledge on progamming

Round 3 - One-on-one 

(1 Question)

  • Q1. Explain Bias-Variance Tradeoff
  • Ans. 

    Bias-variance tradeoff is the balance between model complexity and generalization error.

    • Bias refers to error from erroneous assumptions in the learning algorithm, leading to underfitting.

    • Variance refers to error from sensitivity to fluctuations in the training data, leading to overfitting.

    • Increasing model complexity reduces bias but increases variance, while decreasing complexity increases bias but reduces variance.

    • The...

  • Answered by AI
Round 4 - HR 

(1 Question)

  • Q1. Why should we hire you
Round 5 - HR 

(1 Question)

  • Q1. Final round discussion on package and benefits

Skills evaluated in this interview

Data Scientist Interview Questions Asked at Other Companies

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Q5. coding question of finding index of 2 nos. having total equal to ... read more
Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

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

Round 1 - Technical 

(4 Questions)

  • Q1. Interpretation of classification metrics like accuracy, precision, recall
  • Ans. 

    Classification metrics like accuracy, precision, and recall are used to evaluate the performance of a classification model.

    • Accuracy measures the overall correctness of the model's predictions.

    • Precision measures the proportion of true positive predictions out of all positive predictions.

    • Recall measures the proportion of true positive predictions out of all actual positive instances.

  • Answered by AI
  • Q2. Difference between bagging and boosting
  • Ans. 

    Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.

    • Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

    • Boosting involves training multiple models sequentially, where each subsequent model c...

  • Answered by AI
  • Q3. Difference between R-squared and Adjusted R-squared
  • Ans. 

    R-squared measures the proportion of variance explained by the model, while Adjusted R-squared adjusts for the number of predictors in the model.

    • R-squared is the proportion of variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, with 1 indicating a perfect fit.

    • Adjusted R-squared penalizes the addition of unnecessary predictors to the model, providing a more accur...

  • Answered by AI
  • Q4. Feature Selection Techniques
  • Ans. 

    Feature selection techniques help in selecting the most relevant features for building predictive models.

    • Filter methods: Select features based on statistical measures like correlation, chi-squared test, etc.

    • Wrapper methods: Use a specific model to evaluate the importance of features by adding or removing them iteratively.

    • Embedded methods: Feature selection is integrated into the model training process, like LASSO regre...

  • Answered by AI
Round 2 - Technical 

(4 Questions)

  • Q1. Various types of joins in SQL
  • Ans. 

    Various types of joins in SQL include inner join, outer join, left join, right join, and full join.

    • Inner join: Returns rows when there is a match in both tables.

    • Outer join: Returns all rows when there is a match in one of the tables.

    • Left join: Returns all rows from the left table and the matched rows from the right table.

    • Right join: Returns all rows from the right table and the matched rows from the left table.

    • Full joi...

  • Answered by AI
  • Q2. SQL query on self join
  • Q3. Bias and Variance Tradeoff
  • Q4. Model interpretability

Skills evaluated in this interview

I applied via Company Website and was interviewed before Sep 2021. 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 Resume tips
Round 2 - Coding Test 

The coding test was a Hackerank test with 3 python and 2 SQL questions.

Round 3 - Technical 

(3 Questions)

  • Q1. This is a technical test with questions from Machine Learning and statistics.
  • Q2. What is a Central Limit Theorem?
  • Ans. 

    Central Limit Theorem states that the sampling distribution of the mean of any independent, random variable will be normal or nearly normal.

    • The theorem applies to large sample sizes.

    • It is a fundamental concept in statistics.

    • It is used to estimate population parameters from sample statistics.

    • It is important in hypothesis testing and confidence intervals.

    • Example: If we take a large number of samples of the same size from...

  • Answered by AI
  • Q3. Can you explain gradient descent?
  • Ans. 

    Gradient descent is an iterative optimization algorithm used to minimize a cost function by adjusting model parameters.

    • Gradient descent is used in machine learning to optimize models.

    • It works by iteratively adjusting model parameters to minimize a cost function.

    • The algorithm calculates the gradient of the cost function and moves in the direction of steepest descent.

    • There are different variants of gradient descent, such...

  • Answered by AI
Round 4 - Technical 

(4 Questions)

  • Q1. This was related to Projects, what projects did you work on and domain-related questions
  • Q2. What is Image segmentation?
  • Ans. 

    Image segmentation is the process of dividing an image into multiple segments or regions.

    • It is used in computer vision to identify and separate objects or regions of interest in an image.

    • It can be done using various techniques such as thresholding, clustering, edge detection, and region growing.

    • Applications include object recognition, medical imaging, and autonomous vehicles.

    • Examples include separating the foreground a...

  • Answered by AI
  • Q3. Questions about output shape when an convolution operation is performed using a filter.
  • Q4. How is object detection done using CNN?
  • Ans. 

    Object detection using CNN involves training a neural network to identify and locate objects within an image.

    • CNNs use convolutional layers to extract features from images

    • These features are then passed through fully connected layers to classify and locate objects

    • Common architectures for object detection include YOLO, SSD, and Faster R-CNN

  • Answered by AI
Round 5 - Case Study 

Analyze a scenario for the reduce in sales of a product in the end of the month.

Round 6 - One-on-one 

(1 Question)

  • Q1. One to One round with Manager, less technical but some case study and work culture related.

Interview Preparation Tips

Topics to prepare for Great Learning Data Scientist interview:
  • Machine Learning
  • Deep Learning
  • SQL
  • Python
  • Statistics
  • Case study
  • Scenario based questions
Interview preparation tips for other job seekers - Be prepared on technical as coding can be asked on any round depending on the requirement.
Always be prepared with the basics and understand your project completely.

Skills evaluated in this interview

Great Learning interview questions for designations

 Senior Data Scientist

 (1)

Interview questions from similar companies

Interview Questionnaire 

3 Questions

  • Q1. Tell me something about yourself?
  • Q2. What is your strengths and weaknesses?
  • Q3. What is your expected salary from our company?

Interview Preparation Tips

Interview preparation tips for other job seekers - Thank you for giving me an opportunity for introducing myself. My name is Akash Anadure. I am from pune. I have completed my graduation B.E Computer Science and engineering in PDA College of engineering Gulbarga. I have completed my
Interview experience
3
Average
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Naukri.com and was interviewed in Apr 2023. There were 3 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 Resume tips
Round 2 - HR 

(2 Questions)

  • Q1. The first round of interviews was smooth; I had to visit their office in Andheri and then have a face-to-face conversation with Neeraj sir (Hr). The questions were to introduce yourself, what is big data, ...
  • Q2. After answering, he seemed somehow satisfied, but later he was encouraging me to enroll in their course and enhance my skills. And I'm still getting their emails and promotional messages for the same.
Round 3 - Technical 

(2 Questions)

  • Q1. The second round was on the call, which was rescheduled thrice. Then finally I got a call where Ashish sir (Hr) asked me to introduce myself, what are T&Z testing, overfitting, K neighbour algorithm, can w...
  • Q2. The job position was for a fresher Data analyst, but they were asking questions related to Data scientist position. Basically, they were looking for a candidate who has hands-on experience with real-time d...

Interview Preparation Tips

Topics to prepare for Imarticus Learning Data Analyst interview:
  • Data Science
  • R
  • Python
  • Excel
Interview preparation tips for other job seekers - Learn more about Data science, the company is into placing students in MNC's if they are skillful.

Interview Questionnaire 

2 Questions

  • Q1. Google sheet
  • Q2. Sql

Interview Preparation Tips

Interview preparation tips for other job seekers - learn google sheet and sql basic formulas

Interview Questionnaire 

1 Question

  • Q1. Any type of questions in data field

I applied via Naukri.com and was interviewed before Sep 2021. There were 2 interview rounds.

Round 1 - Aptitude Test 

Question was moderate.based on logical reasoning and math.

Round 2 - Coding Test 

Coding test question based on sql and python.

Interview Preparation Tips

Interview preparation tips for other job seekers - if u have good knowledge on python ,machine learning and sql, then u can easily crack the exam.
Interview experience
1
Bad
Difficulty level
-
Process Duration
2-4 weeks
Result
-

I applied via LinkedIn and was interviewed in Feb 2023. There was 1 interview round.

Round 1 - One-on-one 

(2 Questions)

  • Q1. What's your experience?
  • Q2. Don't you have Data Science experience?
  • Ans. 

    No, I don't have direct Data Science experience.

    • However, I have worked with data extensively in my previous roles as a Data Analyst.

    • I have experience in data cleaning, data visualization, and statistical analysis.

    • I am also familiar with programming languages such as Python and R, which are commonly used in Data Science.

    • I am willing to learn and expand my skills in Data Science if given the opportunity.

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - No pre screening process. Directly recruiter asked for suitable time for an interview and it was scheduled and JD has not given. No proper response from the recruiting team.
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Great Learning Interview FAQs

How many rounds are there in Great Learning Data Scientist interview?
Great Learning interview process usually has 3-4 rounds. The most common rounds in the Great Learning interview process are Technical, One-on-one Round and HR.
How to prepare for Great Learning Data Scientist interview?
Go through your CV in detail and study all the technologies mentioned in your CV. Prepare at least two technologies or languages in depth if you are appearing for a technical interview at Great Learning. The most common topics and skills that interviewers at Great Learning expect are Python, Tableau, Automation, Data Analysis and Data Collection.
What are the top questions asked in Great Learning Data Scientist interview?

Some of the top questions asked at the Great Learning Data Scientist interview -

  1. How is object detection done using C...read more
  2. What is a Central Limit Theor...read more
  3. Can you explain gradient desce...read more

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Great Learning Data Scientist Interview Process

based on 3 interviews

Interview experience

3.7
  
Good
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Great Learning Data Scientist Salary
based on 95 salaries
₹6.5 L/yr - ₹17 L/yr
26% less than the average Data Scientist Salary in India
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3.6/5

Rating in categories

4.1

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3.7

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3.5

Salary

4.6

Job security

4.0

Company culture

2.8

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3.3

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