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Great Learning Senior Data Scientist Interview Questions, Process, and Tips
Great Learning Senior Data Scientist Interview Experiences
1 interview found
Anonymous
I applied via Referral and was interviewed in Oct 2021. There were 5 interview rounds.
Interview Questionnaire
9 Questions
- Q1. About the pervious Project?
- Q2. How ensemble techniques works?
- Ans.
Ensemble techniques combine multiple models to improve prediction accuracy.
Ensemble techniques can be used with various types of models, such as decision trees, neural networks, and support vector machines.
Common ensemble techniques include bagging, boosting, and stacking.
Bagging involves training multiple models on different subsets of the data and combining their predictions through averaging or voting.
Boosting invol...
- Q3. Types of ensemble techniques?
- Ans.
Ensemble techniques combine multiple models to improve prediction accuracy.
Bagging: Bootstrap Aggregating
Boosting: AdaBoost, Gradient Boosting
Stacking: Meta-model combines predictions of base models
Voting: Combining predictions of multiple models by majority voting
- Q4. Explain bagging
- Ans.
Bagging is a technique used in machine learning to improve the stability and accuracy of a model by combining multiple models.
Bagging stands for Bootstrap Aggregating.
It involves creating multiple subsets of the original dataset by randomly sampling with replacement.
Each subset is used to train a separate model, and the final prediction is the average of all the predictions made by each model.
Bagging reduces overfittin...
- Q5. Explain bosting?
- Ans.
Boosting is an ensemble learning technique that combines multiple weak models to create a strong model.
Boosting iteratively trains weak models on different subsets of data
Each subsequent model focuses on the misclassified data points of the previous model
Final prediction is made by weighted combination of all models
Examples include AdaBoost, Gradient Boosting, XGBoost
- Q6. Difference between bias and variance
- Ans.
Bias is error due to erroneous assumptions in the learning algorithm. Variance is error due to sensitivity to small fluctuations in the training set.
Bias is the difference between the expected prediction of the model and the correct value that we are trying to predict.
Variance is the variability of model prediction for a given data point or a value which tells us spread of our data.
High bias can cause an algorithm to m...
- Q7. Classification techniques?
- Ans.
Classification techniques are used to categorize data into different classes or groups based on certain features or attributes.
Common classification techniques include decision trees, logistic regression, k-nearest neighbors, and support vector machines.
Classification can be binary (two classes) or multi-class (more than two classes).
Evaluation metrics for classification include accuracy, precision, recall, and F1 scor...
- Q8. Explain about random forest
- Ans.
Random forest is an ensemble learning method for classification, regression and other tasks.
Random forest builds multiple decision trees and combines their predictions to improve accuracy.
It uses bagging technique to create multiple subsets of data and features for each tree.
Random forest reduces overfitting and is robust to outliers and missing values.
It can handle high-dimensional data and is easy to interpret featur...
- Q9. Many question on SQL and Python
Interview Preparation Tips
Skills evaluated in this interview
Top trending discussions
Interview questions from similar companies
I applied via Referral and was interviewed in Sep 2020. There was 1 interview round.
Interview Questionnaire
1 Question
- Q1. Salary package
Interview Preparation Tips
Interview Questionnaire
2 Questions
- Q1. Google sheet
- Q2. Sql
Interview Preparation Tips
Selected
I applied via Walk-in and was interviewed in Mar 2024. There were 2 interview rounds.
Any general knowledge topic
(6 Questions)
- Q1. Tell me about yourself
- Ans. My Name is Sujitha Rajendran.I have completed Master of Business Economics at Ethiraj College for women. I am from Namakkal district and searching for job opportunities now. My Father is a business man. My Mother is a home maker and My Brother is a Software Engineer
- Q2. Strength and weakness
- Q3. Short term and long term goal
- Ans.
Short term goal is to enhance data analysis skills, long term goal is to become a data science expert.
Short term goal: Improve proficiency in SQL, Python, and data visualization tools
Long term goal: Obtain advanced certifications in machine learning and AI
Short term goal: Complete online courses on statistical analysis and data cleaning
Long term goal: Lead data science projects and mentor junior analysts
- Q4. Why did you chosen this company?
- Ans. My Strength is that I am not only a hard working person but also a smart working person. I am a quick learner and self motivational person. I am a good team player, good listener and good in time management
- Q5. Why we want to hire you?
- Q6. Salary discussion
Interview Preparation Tips
- Data Science
I applied via Company Website and was interviewed in Jun 2024. There were 2 interview rounds.
2.5 Hours 2 Coding que and sql query and topin tech platform
(2 Questions)
- Q1. Based on boolean matrix
- Q2. Find k closest node of given given node in a BST.
- Ans.
Find k closest nodes to a given node in a BST.
Perform an inorder traversal of the BST to get a sorted list of nodes.
Use a priority queue to keep track of the k closest nodes based on their absolute difference with the target node.
Populate the priority queue with the first k nodes from the inorder traversal.
For each subsequent node, calculate its absolute difference with the target node and compare it with the top eleme...
Skills evaluated in this interview
SQL, Analytical, Maths
(2 Questions)
- Q1. SQL qsn on some data
- Q2. Qsn on LLMs in company
I applied via Naukri.com and was interviewed in Mar 2024. There were 2 interview rounds.
SQL two problem statements to be solved in 30 mins
Python two problem statements to be solved in 30 mins
Around 15 MCQ on stats and aptitude
(1 Question)
- Q1. Python, Excel, SQL and aptitude
Selected
I applied via Approached by Company and was interviewed before Jan 2023. There were 2 interview rounds.
Case study on lesson planning
(1 Question)
- Q1. Manager round either over zoom or face to face
I applied via Recruitment Consulltant and was interviewed in Oct 2022. There were 4 interview rounds.
(4 Questions)
- Q1. Ask some questions about my self and previously what i did and some questions related to company...
- Q2. Tell me about your self
- Q3. What are the skills you have
- Q4. Why are you choose this company
They share some apptitude questions and communication related questions to answer them...
Take a one topic from my self to discuss with other to communicate....how easily
Interview Preparation Tips
- Python
- MySQL
- Machine Learning
- Tableau
- Excel
I applied via Naukri.com and was interviewed in Jan 2024. There were 2 interview rounds.
Verbal, logical, Quantative test
(1 Question)
- Q1. Self introduction
Interview Preparation Tips
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Great Learning Senior Data Scientist Reviews and Ratings
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