MỘT SỐ CÂU HỎI PHỎNG VẤN CHO VỊ TRÍ DATA SCIENTIST

Interview questions for Data Scientist Jobs
1) Differentiate between Data Science , Machine Learning and AI.
2) Which technique is used to predict categorical responses?
3) Why data cleaning plays a vital role in analysis?
4) Differentiate between univariate, bivariate and multivariate analysis.
5) What do you understand by the term Normal Distribution?
6) What is Linear Regression?
7) What is machine learning?
8) What does P-value signify about the statistical data?
9) What are categorical variables?
10) Define true positive, false positive, true negative, false negative rate?
11) What is the difference between Supervised Learning an Unsupervised Learning?
12) What is an Eigenvalue and Eigenvector?
13) How can outlier values be identified and treated?
14) How can you assess a good logistic model?
15) What are various steps involved in an analytics project?
16) During analysis, how do you treat missing values?
17) What is multicollinearity and how you can overcome it?
18) How are confidence intervals constructed and how will you interpret them?
19) How will you explain logistic regression to an economist, physican scientist and biologist?
20) How can you overcome Overfitting/underfitting?
21) Differentiate between wide and long data formats?
22) How will you define the number of clusters in a clustering algorithm? in k-mean?
23) Is it better to have too many false negatives or too many false positives?
24) What are Precision and REcall of a machine learning model
25) What are the steps you would follow to validate a multiple linear regression model?
26) How can you deal with different types of seasonality in time series modelling?
27) In experimental design, is it necessary to do randomization? If yes, why?
28) Can you explain the difference between a Test Set and a Validation Set?
29) Explain backpropagation
30) Main components of a neural network model
31) Give some situations where you will use an SVM over a RandomForest Machine Learning algorithm and vice-versa.
32) What are the advantages and disadvantages of using regularization methods like Ridge Regression?
33) How will you find the correlation between a categorical variable and a continuous variable ?
34) How do you handle missing data?

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