Data science interview questions

Data science interview questions: In this article, we list down 60 important interview questions on Python data science one must know. Whether you're a candidate or interviewer, these interview questions will help

1. How to flatten a matrix?
2. How to calculate the determinant of a matrix or nArray?
3. How can you train and interpret a linear regression model in scikit learn?
4. Name the libraries in python used for data analysis and scientific computations?
5. How can you build a simple logistic regression model in python?
6. Which is the main difference between a panda’s series and single-column dataframe in python?
7. How to calculate the diagonal of matrix?
8. How to invert a matrix in python?
9. How to calculate the trace of a matrix?

10. How to convert dictionary to a matrix or narray in python?

11. Write a code to sort a Dataframe in python descending order?
12. How to run a basic RNN model using pytorch?
13. How to save and reload deep learning models in pytorch?
14. How to use autoencoder for unsupervised learning models?
15. How to handle duplicate values in dataset for a variable in python?
16. Which random parameter is used to create a scatter plot matrix?
17. How to check if the data set or time series is random?
18. How to reshape a numpy array in python?
19. How to select elements from numpy array in python?
20. How to create a vector or matrix in python?
21. What are the possible ways to load an array from text data file in python?
22. Which is the data missing marker used in pandas?
23. Why you should use Numpy arrays instead of nested python lists?
24. What is the preferred method to check an empty array in Numpy?
25. List down evaluation metrics for regression problems?
26. Which python library will prefer you prefer to use for data mining?
27. List down the evaluation metrics for regression problems?

28. Write code to sort an array in Numpy by the nth column?

29. How are Numpy and scipy related?
30. Which python library is built on top of matplotlib and pandas to ease data plotting?
31. Which plot will you use to access the uncertainty of statistics?
32. What are some features of pandas that you like or dislike?
33. Which scientific libraries in scipy have you worked on within your project?
34. What is pylab?
35. A package that combines numpy, scipy and matplotlib into a single namespace?
36. Which python library is used for machine learning?
37. What are the differences between supervised and unsupervised learning?
38. How is logistic regression done?
39. How do you build a random forest model?
40. How can you avoid the overfitting of your model?
41. What are the differences between supervised and unsupervised learning?

42. How is logistic regression done?

43. Explain the steps in making a decision tree?
44. How do you build a random forest model?
45. How can you avoid the overfitting of your model?
46. Differentiate between univariate, bivariate and multivariate analysis?
47. What are the feature selection methods used to select the right variables?
48. Write a program that prints the numbers ranging from one to 50?
49. For given points, you will you calculate the Euclidean distance in python?
50. What are dimensionally reduction and its benefits?
51. What is bias, variance trade-off?
52. What difference is between supervised and in supervised machine learning?
53. What is a confusion matrix?
54. Explain how an ROC curve works?
55. What are the support vectors in SVM?
56. Explain the decision tree algorithm in detail?
57. What ensemble learning?
58. Which is a technique is used to predict categorical responses?
59. What are recommender systems?
60. What is power analysis?
61. What is collaborative filtering?