Science Interview Questions

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First two rounds: CNN, Transfer Learning, Bagging, Boosting and project related questions. I was given a task of implementing some techniques to improve the classification accuracy of a product classification model. Rest of the rounds: Data Structures, Algorithms and basics of distributed systems.
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Member Technical Staff (Data Science)

Interviewed at NetApp

3.8
May 23, 2018

First two rounds: CNN, Transfer Learning, Bagging, Boosting and project related questions. I was given a task of implementing some techniques to improve the classification accuracy of a product classification model. Rest of the rounds: Data Structures, Algorithms and basics of distributed systems.

jkk 1. What is linear regression? Explain steps for modelling 2. What do you mean by R square and adjusted R square? 3. What is logistic regression? Explain steps for modelling 4. Forecasting methods like time series, ARIMA etc. 5. Basics of Tableau 6. Basic SQL queries 7. libraries of python and basic coding skills (hardly any questions related to this are asked) 8. Any analytics projects you have worked on or competitions you have participated in?
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Data Science Intern

Interviewed at NetApp

3.8
Oct 28, 2021

jkk 1. What is linear regression? Explain steps for modelling 2. What do you mean by R square and adjusted R square? 3. What is logistic regression? Explain steps for modelling 4. Forecasting methods like time series, ARIMA etc. 5. Basics of Tableau 6. Basic SQL queries 7. libraries of python and basic coding skills (hardly any questions related to this are asked) 8. Any analytics projects you have worked on or competitions you have participated in?

Given financial data from credit card users. How would you model and predict which costumers are likely not to pay their credit card debt? How would you present your results to an executive with zero quantitative knowledge?
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Data Science Consultant

Interviewed at Management Solutions

3.9
Mar 14, 2019

Given financial data from credit card users. How would you model and predict which costumers are likely not to pay their credit card debt? How would you present your results to an executive with zero quantitative knowledge?

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