Data science is a booming field in the tech world. If you're looking to make a successful career in data science, anticipating the interview can be an anxious task. Whether you're an experienced professional, a newbie, or hold many data science certifications, interviewers can render you speechless with unexpected questions. That won't work, right?

What Should You Expect At The Interview?

A data science interview will consist of questions from various topics related to the field because employers will be looking for someone with strong technical knowledge and good communication skills. Many of these questions will be designed to intentionally keep you on the edge of your seat to see how you perform under pressure, so be prepared and confident.

To prepare yourself well, here are 50 questions that a data science interviewee might ask you. Use this as a guide to prepare answers that will resonate with the company. You can also bookmark this article for spot references.

50 Crucial Data Science Questions

1. What is the life cycle of a single data science project?

2. How will you measure yield (over baseline) resulting from a new or refined algorithm or architecture?

3. What is cross-validation and what's the correct procedure for doing it?

4. What's better, designing robust or accurate algorithms?

5. Have you written a production code before? Did you prototype an algorithm and created a proof of concept?

6. What is the biggest data set you have worked with, with regards to the training set size, having your own algorithm implemented in production mode to process billions of transactions?

7. What are some popular API's and how will you create one?

8. Can you scape web data or collect tons of tweets? If yes, how?

9. How will you optimize algorithms? Answer with examples.

10. Name a few examples of NoSQL architecture.

11. How will you clean data?

12. How will define or select metrics? Have you used designed and used compound metrics before?

13. Name some examples of bad and good visualizations.

14. Were you a part of the team that designed dashboards and alarm systems? What was your role?

15. How frequently should an algorithm be updated?

16. Give examples of machine-to-machine communication.

17. Have you automated a repetitive analytical task? How did you do it?

18. How will you assess the statistical significance of an insight?

19. How will you turn unstructured data into structured data?

20. What is the efficient method to cluster 100 billion web pages with a tagging or indexing algorithm?

21. If you were conducting a data science interview, what questions would you ask?

22. What is regularization and what is its use? What are the merits and demerits of specific methods like ridge regression and LASSO?

23. What is a local optimum and what is its significance in a scenario where k-means clustering?

24. How will you generate a predictive model of a quantitative outcome variable using multiple regression?

25. What are precision and recall and how are they related to the ROC curve?

26. What is a long-tailed distribution? Name three examples of phenomena that have long tails.

27. What is latent semantic indexing and what is it used for? Are there any limitations to this method?

28. What is the Central Limit Theorem and why is it important?

29. Explain statistical power.

30. What are the uses and limitations of resampling methods?

31. What are the differences between artificial neural networks with softmax activation, logistic regression, and the maximum entropy classifier?

32. What is selection bias and why is it important?

33. Give an example of how an experimental design can answer a behavioral question? How does experimental data differ from observational data?

34. What is the difference between long and wide format data?

35. Is mean imputation of missing data an acceptable practice? Explain.

36. What is Edward Tufte's concept of "chart junk'?

37. What is an outlier and how will you screen for outliers? What will be your POA if you found them in your data set?

38. What is principal components analysis (PCA)? What problems require PCA?

39. If you were given data on the duration of calls to a call center, how will you make a plan to code and analyze the data?

40. What is a false positive and a false negative? Describe situations where each of that is important.

41. What are the differences between administrative datasets and datasets gathered from experimental studies?

42. What is a gold standard?

43. Differentiate between supervised learning and unsupervised learning with examples.

44. What is NLP?

45. How will you write a code to count the number of words in a document using a programming language of your choice? How will you extend this for bi-grams?

46. What are feature vectors?

47.  Describe a scenario where you will use SVMs and Random Forest.

48. How would you define big data? What is the largest size of data you have worked with?

49. What method will you use to work with large data sets?

50. Write a mapper function to count word frequencies and write a reducer function for the same.

Use these questions to form your answers and present them confidently. Use your sound judgment to show them your abilities. Remember, preparation is the key to success if you want to pursue a career in data science. Good luck!