In a data scientist interview, expect employers to ask questions that assess your data modeling, problem-solving, and programming skills. Be prepared to answer general questions that test your knowledge of statistics and data science. You should also be ready to answer open-ended questions that test your creativity, communication skills, and formal education in data modeling and programming.
write a code in R/SQL: Given a table with three column, (id, category, value) and each id has 3 or less category (price, size, color). Now, how can I find those id's for which the value of two or more category matches to one another? For eg: ID1 (price 10, size M, color Red), ID2 (price 10, Size L, Color Red) , ID3 (price 15, size L, color Red) Then the output should be two rows: ID1 ID2 and ID2 ID3
- What is over-fitting? How do you avoid it? - What types of regularization do we have? Which one is simpler to use? L1 or L2? - Explain decision trees? What are different metrics to classify dataset? - What is bagging? - We have two models, one with 85% accuracy, one 82%. Which one do you pick? - What is p-value and how can we use it?
Assumption of Linear Regression, etc.
Interviewer: What is the technique to evaluate Logistic regression. I answered Cross entropy. Interviewer: no
career goals
Given measurements of acceleration taken from a wristband, with second by second acceleration in the X, Y, Z axis, how would you predict if the person wearing the wrist is sitting, walking or just standing?
Describe a time you had a problem at work and how did you solve it?
Mettez vous à jour des procédures?
From the csv files, how is "A" improved over "B"? Do you have any other findings?
How would you respond to being asked to lead a group project you don't have time for?
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