AmbitionBox
Interview Questions
- Reviews
- Salaries
- Interview Questions
- About Company
- Benefits
- Jobs
- Office Photos
- Community
Add office photos
i
Great
Learning
Compare
Filter interviews by
Great Learning Senior Data Scientist Interview Questions and Answers
7 Interview questions
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
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 overf...
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
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 ...
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 f...
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...
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...
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
I applied via Naukri.com and was interviewed before Sep 2021. There were 2 interview rounds.
Question was moderate.based on logical reasoning and math.
Coding test question based on sql and python.
Interview Preparation Tips
I applied via LinkedIn and was interviewed before Sep 2021. There were 3 interview rounds.
(1 Question)
- Q1. DS and algorithm questions
(1 Question)
- Q1. IOS /Skillset questions some tricky questions
(1 Question)
- Q1. Personality and Compensation discussion
Interview Preparation Tips
Interview Questionnaire
2 Questions
- Q1. Google sheet
- Q2. Sql
Interview Preparation Tips
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
Selected
I applied via Referral and was interviewed before Aug 2023. There was 1 interview round.
(2 Questions)
- Q1. Tell me something about yourself
- Ans.
I am a data analyst with a strong background in statistics and programming, passionate about deriving insights from data.
I have a Bachelor's degree in Statistics and experience in data visualization tools like Tableau
I am proficient in programming languages such as Python and SQL
I have worked on projects involving predictive modeling and data mining techniques
- Q2. Some question related to Python
(1 Question)
- Q1. Tell me about your self
- Ans.
I am a dedicated and experienced professional with a strong background in project management and team leadership.
Over 8 years of experience in project management
Proven track record of successfully leading cross-functional teams
Strong communication and problem-solving skills
Certified Project Management Professional (PMP)
Previously managed a project that resulted in a 20% increase in efficiency
Interview Preparation Tips
Selected
I appeared for an interview in Nov 2024, where I was asked the following questions.
- Q1. What is SEO and how does it help content optimization?
- Ans.
SEO, or Search Engine Optimization, enhances online content visibility, driving organic traffic through improved search rankings.
Keyword Research: Identifying relevant keywords helps tailor content to what users are searching for, e.g., using 'best running shoes' in a blog post.
On-Page Optimization: This includes optimizing title tags, meta descriptions, and headers to improve search engine understanding of the content...
- Q2. What's the purpose of keywords in content?
- Ans.
Keywords enhance content visibility, improve SEO, and help target specific audiences by aligning with search intent.
SEO Optimization: Keywords are crucial for search engine optimization, helping content rank higher in search results. For example, using 'best running shoes' can attract targeted traffic.
Audience Targeting: By incorporating relevant keywords, content can be tailored to meet the needs and interests of spec...
Great Learning Interview FAQs
Tell us how to improve this page.
Great Learning Interviews By Designations
- Great Learning Learning Consultant Interview Questions
- Great Learning Senior Learning Consultant Interview Questions
- Great Learning Data Scientist Interview Questions
- Great Learning Sales Executive Interview Questions
- Great Learning Program Manager Interview Questions
- Great Learning Project Manager Interview Questions
- Great Learning Accountant Interview Questions
- Great Learning Assistant Manager Interview Questions
- Show more
Interview Questions for Popular Designations
- Senior Software Engineer Interview Questions
- Data Analyst Interview Questions
- Senior Associate Interview Questions
- Senior Consultant Interview Questions
- Data Scientist Interview Questions
- Senior Analyst Interview Questions
- Senior Data Analyst Interview Questions
- Decision Scientist Interview Questions
- Show more
Interview Questions from Similar Companies
Great Learning Senior Data Scientist Reviews and Ratings
based on 2 reviews
Rating in categories
|
Program Manager
370
salaries
|  ₹5.9 L/yr - ₹12.1 L/yr |
|
Senior Learning Consultant
316
salaries
| ₹4.8 L/yr - ₹13.5 L/yr |
|
Learning Consultant
281
salaries
| ₹3.8 L/yr - ₹10.2 L/yr |
|
Data Scientist
120
salaries
| ₹6.5 L/yr - ₹15.8 L/yr |
|
Senior Program Manager
110
salaries
| ₹8 L/yr - ₹14.8 L/yr |
Whitehat jr
Unacademy
Extramarks Education
upGrad
- Home >
- Interviews >
- Great Learning Interview Questions
