Data Scientist Interview Questions & Answers

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Data Scientists analyze raw data by using statistical and mathematical techniques, and computing. This may include filtering and processing data to reach meaningful interpretations and conclusions; and creating solutions.

Whether you're a job seeker preparing to be interviewed for the role of Data Scientist or an employer preparing to interview candidates for Data Scientist position, these Data Scientist interview questions will help you prepare yourself for the job interview session.

Data Scientist Interview Questions

Below are a list of some skill-based Data Scientist interview questions.

1. Describe regularization and its importance
2. What is selection bias and why is it important?
3. How would you design a taxonomy to determine key customer trends from unstructured data?
4. Write the formula to determine R-square.
5. Describe a time when you went beyond the requirements of a project.
6. What steps have you taken to improve your analytics skills?
7. Tell me about the last mistake you made in an algorithm and what you did to correct it.
8. Tell me about the biggest data set you have processed and its use.
9. Is it better to have too many false positives or false negatives?
10. Break down an algorithm that you used recently on a project.
11. What software and tools did you use in your most recent project, and why did you choose those?
12. Describe a challenge you have encountered during a project and how you overcame it.
13. What are correlation and covariance?
14. What is the purpose of A/B testing? Describe how you have used A/B testing recently.
15. Explain the steps in making a decision tree.
16. Give me an example of when you have used decision-tree algorithm.
17. Describe to me a time when you used ensemble learning techniques.
18. How would you explain to upper management why a data set is important?
19. Why do you think deep learning is becoming popular?
20. How would you explain logistic regression to someone?
21. Can you give me an example of when you have used logistic regression recently?
22. Do you prefer using Python or R? Why?
23. What data visualization tools do you like best?
24. What technique do you use to predict categorical responses?
25. How do you select K for K-means?
26. Are you familiar with economics terms such as price optimization, price elasticity, inventory management and competitive intelligence? If so, please define them.
27. What are some limitations of resampling methods?
28. When does parallelism help your algorithms, and when does it hurt them?
29. How do you treat outlier values?
30. How do you assess a logistic model?
31. How do you treat missing values during an analysis?
32. Create a function with two sorted lists that generate a sorted list merging the two of them.
33. What types of problems does regularization solve?
34. What are the benefits and downsides of regularization methods?
35. How do you overcome multicollinearity?
36. How do you interpret confidence intervals?
37. What language do you use with fuzzy merging?
38. What is the most recent project you have worked on?
39. What steps do you typically follow in an analytics project?
40. What software and computer programs are you skilled in?
41. Describe your analytics style.
42. Tell me about a time you disagreed with your boss and how you handled it.
43. Tell me about a time you disagreed with a coworker and how you handled it.
44. How do you determine if your linear regression model fits certain data?
45. Explain how you would find the relationship between a continuous variable and a categorical variable?
46. If you flip a coin 1,000 times and tails show up 575 times, is the coin biased?
47. What machine learning algorithm is your favorite? Why?
48. Is more data better than less? Explain.
49. How do you make sure you don't analyze something that produces meaningless results?
50. How do you prevent overfitting when designing a statistical model?
51. How often would you update an algorithm?
52. Can you explain the difference between a Validation Set and a Test Set?
53. Are you familiar with exploding gradients? What does this term represent and how does it compare to standard gradients?
54. You create a data storage system to organize data figures, but it isn’t working correctly. Are you comfortable asking others for help?
55. What is the confusion matrix used for? Can you provide an example?
56. How do you decide which models or algorithms to use in analyzing data sets?
57. We want to improve our customer retention rates. What methods would you use to analyze our customer data?
58. Describe your process for cleaning messy data.
59. What’s your experience with SQL?
60. How would you test if survey responses were filled at random as opposed to truthful selections?
61. Can you explain the concept of data mining?
62. What’s the difference between structured and unstructured data?
63. How do you stay up to date on the latest news and trends in the industry?
64. Are you comfortable presenting your findings to large groups?
65. What is cross-tabulation and why is it important?
66. What’s the difference between business intelligence and data science?
67. Describe the nature of predictive analytics and which types you prefer to use.
68. What do you know about natural language processing?
69. Which metrics would you want to include in a dashboard for a new feature?
70. You are given a data set consisting of variables with more than 30 percent missing values. How will you deal with them?
71. For the given points, how will you calculate the Euclidean distance in Python?
72. What are dimensionality reduction and its benefits?
73. How do you find RMSE and MSE in a linear regression model?
74. How can time-series data be declared as stationery?
75. You are given a dataset on cancer detection. You have built a classification model and achieved an accuracy of 96 percent. Why shouldn't you be happy with your model performance? What can you do about it?
76. We want to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level. What is the most appropriate algorithm for this case?
77. Are you familiar with the term “big data”?
78. How would you describe the relationship between data and information, and information and knowledge?
79. What is the most important thing you have learned as a data scientist?
80. Provide an example of a time when you used data mining to discover useful information.
81. If you could only use three tools as a data scientist, what would they be?
82. What would you say is your greatest weakness as a data scientist?
83. Do you have any experience using R programming?
84. When would you use logistic regression over classical regression?
85. After studying the behavior of a population, you have identified four specific individual types that are valuable to your study. You would like to find all users who are most similar to each individual type. Which algorithm is most appropriate for this study?
86. Your organization has a website where visitors randomly receive one of two coupons. It is also possible that visitors to the website will not receive a coupon. You have been asked to determine if offering a coupon to website visitors has any impact on their purchase decisions. Which analysis method should you use?
87. What is the ROC curve?
88. What are the popular libraries used in Data Science?
89. What makes you stand out from other data scientists?

Data Scientist Interview Questions and Answers

Every interview is different and the questions may vary. However, there are lots of general questions that get asked at every interview.

Below are some common questions you'd expect during Data Scientist interviews. Click on each question to see how to answer them.

1. What is Your Salary Expectation?
2. Tell Me About Yourself
3. Are You a Leader or a Follower?
4. Why Do You Want To Leave Your Current Job?
5. What Is Your Greatest Accomplishment?
6. What is Your Greatest Strength?
7. What is Your Greatest Weakness?
8. Why Should We Hire You?
9. Do You Have Any Questions for Us?
10. Why Do You Want This Job?

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