Data Associate Interview Questions

2,708 data associate interview questions shared by candidates

1. Complete project Description 2. Describe Naive Bayes classifier 3. Case study( A person salary dataset and different campaign dataset, Predict whether he will buy the product or not) What all features will you generate from the dataset?
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Associate Data Scientist

Interviewed at ZS Associates

3.5
Nov 2, 2019

1. Complete project Description 2. Describe Naive Bayes classifier 3. Case study( A person salary dataset and different campaign dataset, Predict whether he will buy the product or not) What all features will you generate from the dataset?

1st round online coding: python questions were easy. SQL questions though lengthy, were also easy. 2nd round Technical: Qns: 1) Explain projects briefly as mentioned in resume 2) Linear regression? Can we use classification with linear regression 3) Logistic regression, threshold score, ROC AUC curve, imbalanced classification 3) Clustering? Hierarchical , K-Means difference and explain 4) Random-forest, decision tree explain 5) Data analysis using pandas 6) Gave a problem case study and asked me to explain the steps taken: build hypothesis, what will be the impact 3rd round Technical: 1) Take one project and explain approach taken to solve in detail: business problem, data sources, pre-processing steps , feature engineering, algorithm used, evaluation metric 2) Random forest explain 3) different evaluation metrics used for classification - precision, recall 4th round HR general background check challenges faced and how did you resolve accomplishments in work
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Associate Data Scientist

Interviewed at MathCo

3.9
Nov 6, 2020

1st round online coding: python questions were easy. SQL questions though lengthy, were also easy. 2nd round Technical: Qns: 1) Explain projects briefly as mentioned in resume 2) Linear regression? Can we use classification with linear regression 3) Logistic regression, threshold score, ROC AUC curve, imbalanced classification 3) Clustering? Hierarchical , K-Means difference and explain 4) Random-forest, decision tree explain 5) Data analysis using pandas 6) Gave a problem case study and asked me to explain the steps taken: build hypothesis, what will be the impact 3rd round Technical: 1) Take one project and explain approach taken to solve in detail: business problem, data sources, pre-processing steps , feature engineering, algorithm used, evaluation metric 2) Random forest explain 3) different evaluation metrics used for classification - precision, recall 4th round HR general background check challenges faced and how did you resolve accomplishments in work

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