1. Can you give a brief description of Exploratory Data Analysis and feature engineering? 2. We are trying to predict customer cancellations. Is this problem a classification problem or regression problem? 3. What is regularisation? How is it done? 4. In a regression output – each of the coefficients have estimates and p-values – can you briefly explain what those mean and how you might use that information? 5. What are precision and recall? Explain with example
Comment s'est déroulé votre erasmus?
Mainly questions about my previous education and projects I had been involved. I was asked to present a bit of the project I was working on.
Not too difficult questions, just my availability and my educational background
A python home assigment, mainly about analyzing data using pandas
It was largely based on explaining your past projects and how you go about solving problems. Not a very technical interview, was more about seeing how well you'd fit in
Round1 - Mostly resume discussion and a few puzzles were asked. Round 2- Director took the interview asked questions on past work.
Basic SQL questions, signal processing/analysis, linear regression, ML techniques.
What is your background experience?
Asked much about ml algorithms, feature engineering steps, model building, deep learning activation functions, neural networks, python basics
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