Interviews are a very crucial part of any Data Scientist’s life. Data Science Interview Questions can often get overwhelming and can leave a candidate sweating. However, there is no reason to fret as it may not be as difficult as it is made out to be. 

Tips to tackle Data Science Interview Questions 

Understand the your role and skills

Before applying, make sure you know what role you want to achieve via giving an interview. Data science has various different roles which can take according to their interests and skills. Identify those skills and apply accordingly for the roles most suitable. This will help you become more confident.

Prepare your Resume well

Make sure you have a good resume; it can have a great impact on your interviewer. Make sure your resume is concise and only have relevant information and nothing useless. The more neat your resume, more your chances of making a good impression.

Interpersonal Interactions

You may be super smart and brilliant at what you do but if you have to have basic people skills. Make sure you have your basic straights when it comes to interpersonal communications and interactions. Eye contact, body language can be crucial without even your knowing!

Following Up

Your interview doesn’t; end at just the interview. There are post interview steps you have to follow. Follow up with your interviewer if you do not get a reply within 3-4 days or the stipulated time period. Ask them for feedback, show them interest, thank them for their time. These little gestures go a long way.

Check Out: IBM Data Science Professional Certificate

Data Science Interview Questions 

Here are some of the most common Data Science interview questions which are often asked from a candidate during interviews.

1. What is the difference between supervised and unsupervised machine learning?
2. What is bias, variance trade-off?
3. What is exploding gradients?
4. What is a confusion matrix?
5. Explain how a ROC curve works?
6. What is selection Bias?
7. Explain the SVM machine learning algorithm in detail.
8. What are the support vectors in SVM?
9. What are the functions of the different kernel in SVM?
10. Explain the Decision Tree algorithm in detail.
11. What are Entropy and Information gain in the Decision tree algorithm?
12. What is pruning in Decision Tree?
13. What is Ensemble Learning?
14. What is a Random Forest? How does it work?
15. What cross-validation technique would you use on a time series data set?
16. What is logistic regression? Or State an example when you have used logistic regression recently.
17. What do you understand by the term Normal Distribution?
18. What is a Box-Cox Transformation?
19. How will you define the number of clusters in a clustering algorithm?
20.  What is deep learning?
21. What are Recurrent Neural Networks(RNNs)?
22.  What is the difference between machine learning and deep learning?
23. What is reinforcement learning?
24. What is selection bias?
25.  Explain what regularisation is and why it is useful.
26. What is TF/IDF vectorization?
27. What is the difference between the Regression and classification of ML techniques?
28. If you are having 4GB RAM in your machine and you want to train your model on a 10GB data set. How would you go about this problem? Have you ever faced this kind of problem in your machine learning/data science experience so far?
29. What are Recommender Systems?
30. What is the p-value?
31. What is ‘Naive’ in a Naive Bayes?
32. What is Naive?
33. Why we generally use Softmax non-linearity function as last operation in-network?

FAQ