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Muhammad

بِسمِ اللَّهِ الرَّحمٰنِ الرَّحيمِ

From: Sahiwal Division

Registered: 2012-03-22

Posts: 22,197

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55 Most Common Data Scientist Interview Questions And Answers

Data Warehouse :: Data Scientist Job Interview Questions and Answers

Data Scientist based Frequently Asked Questions in various Data Scientist job interviews by interviewer. These professional questions are here to ensures that you offer a perfect answers posed to you. So get preparation for your new job hunting

1 Tell me how do you handle missing or corrupted data in a dataset?
2 Tell us why do we have max-pooling in classification CNNs?
3 Tell us how do you identify a barrier to performance?
4 Tell us how do clean up and organize big data sets?
5 Explain me do gradient descent methods at all times converge to a similar point?
6 Tell me why is resampling done?
7 Tell me how is kNN different from kmeans clustering?
8 Can you differentiate between univariate, bivariate and multivariate analysis?
9 Tell me how can outlier values be treated?
10 Explain me what is data normalization and why do we need it?
11 Tell us why do we use convolutions for images rather than just FC layers?
12 Tell us how has your prior experience prepared you for a role in data science?
13 Do you know what is logistic regression?
14 Tell us how regularly must an algorithm be updated?
15 Do you know why is naive Bayes so ‘naive’ ?
16 Explain me why data cleaning plays a vital role in analysis?
17 Do you know what is the goal of A/B Testing?
18 What is dimensionality reduction, where it’s used, and it’s benefits?
19 Explain me what tools or devices help you succeed in your role as a data scientist?
20 What is star schema?
21 Tell me is rotation necessary in PCA? If yes, Why? What will happen if you don’t rotate the components?
22 Explain me what is logistic regression? Or State an example when you have used logistic regression recently?
23 Explain me why do you want to work at this company as a data scientist?
24 Tell us how would you go about doing an Exploratory Data Analysis (EDA)?
25 Tell me why do segmentation CNNs typically have an encoder-decoder style / structure?
26 Do you know the steps in making a decision tree?
27 What is cross-validation?
28 Tell us what is the significance of Residual Networks?
29 Tell us what is root cause analysis?
30 Tell us what are the drawbacks of the linear model?
31 Tell me how do you work towards a random forest?
32 Explain me when is Ridge regression favorable over Lasso regression?
33 Tell me what is power analysis?
34 Tell us are expected value and mean value different?
35 Tell us what methods do you use to identify outliers within a data set?
36 Tell me how do you know which Machine Learning model you should use?
37 Tell me how is True Positive Rate and Recall related?
38 Tell me which technique is used to predict categorical responses?
39 Please explain how do you overcome challenges to your findings?
40 Explain me what makes CNNs translation invariant?
41 What is selective bias?
42 Tell me do gradient descent methods always converge to same point?
43 Tell me what is Linear Regression?
44 Tell me what are the types of biases that can occur during sampling?
45 Do you know what are feature vectors?
46 Tell me what is the Law of Large Numbers?
47 Tell me Python or R – Which one would you prefer for text analytics?
48 Tell me what is Collaborative filtering?
49 Tell me what are Eigenvalue and Eigenvector?
50 Please explain what are Recommender Systems?
51 Do you know what are confounding variables?
52 Tell me what are Recommender Systems?
53 Explain me what is Interpolation and Extrapolation?
54 Tell us what is Collaborative Filtering?
55 What is survivorship bias?

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