Top 50 Interview Questions for Beginner Data Scientists

What interview questions should a beginner data scientist prepare for? This is an important question that many interviewees have. If you are going for a data scientist interview and don’t know what interview questions will you be asked, this blog post has some of the common interview questions that will help you excel in your interview. These interview questions are perfect for beginners because they cover basic topics about data science and machine learning and how it works. We hope this list helps!

1. What is the difference between AI, machine learning, deep learning?
2. Do you know how machine learning works?
3. How is machine learning different from statistical modeling techniques like linear regression, logistic regression, ANOVA F-test, etc.?
4. What is the difference between supervised, unsupervised, semi-supervised learning problems and how do they work?
5. What are some examples of popular algorithms that can be used for classification and clustering problems in machine learning?
6. What are neural networks?
7. What are perceptrons?
8. What is the difference between machine learning and deep learning?
9. What is reinforcement learning?
10. What are the pros and cons of deep learning?
11. What are loss functions and their importance in machine learning?
12. What are different types of loss functions?
13. What is the difference between cross-entropy, hinge loss, and mean squared loss?
14. What is activation function and how does it work?
15. What is the data science process and what are some of its phases, from data collection to data interpretation?
16. What are data preprocessing, feature selection, dimensionality reduction, data visualization, data imputation?
17. Why do you need data preprocessing in machine learning models?
18. Why is data visualization important and what are some of the most popular data visualization techniques you could use?
19. How can you reduce the number of features to make your model simpler?
20. What are some examples of dimensionality reduction techniques for visualizing data?
21. What are some examples of data imputation techniques you could use if you have incomplete data sets with missing values for different variables?
22. What are some techniques you can use for data preprocessing, feature selection, and dimensionality reduction?
23. Why is model interpretation important for data scientists when building machine learning models?
24. Which popular programming languages could a beginner data scientist learn?
25. Out of python, R, Scala, which language did you find most useful and why?
26. What are some of the best data visualization techniques for data exploration purposes?
27. What is the difference between bias and variance?
28. What is the difference between overfitting and underfitting data science models?
29. What are data distributions and how do they affect the model’s accuracy?
30. What are different model performance metrics that data scientists use to evaluate their models?
31. What are some supervised machine learning algorithms data scientists could use to solve regression problems?
32. What are some supervised machine learning algorithms data scientists could use to solve classification problems?
33. What are some data science applications that you could build using machine learning algorithms?
34. Give examples of NLP algorithms and data-science problems that can be solved using them.
35. What is ensemble learning and how does it work?
36. Give examples of algorithms related to ensemble learning?
37. How can you assess model performance in supervised machine learning algorithms? What are some common metrics that data scientists use to evaluate their models’ accuracy, precision, recall, F-score?
38. What is the difference between CNN, RNN, and LSTM?
39. Give some examples of CNN and RNN data-science problems.
40. Which all cloud ML platforms are data scientists using to build and deploy their machine learning models?
41. What is the difference between local algorithms (CPU) and distributed/cloud-based data processing platforms (GPU, TPU)?
42. Why do data scientists need to know about distributed computing frameworks (Hadoop, Spark) and how is it different from traditional data processing algorithms like MapReduce etc.?
43. How do you deploy machine learning models on a mobile device?
44. How is data processing faster in cloud-based data platforms vs CPU/GPU-based local machines?
45. How is Amazon Sagemaker different from the rest of ML cloud platforms like Google Cloud, Azure Machine Learning Studio, etc.? Which one do you think will be more useful to a data scientist?
46. How is scikit learn data science library different from other data processing libraries like NumPy, Pandas?
47. Why do data scientists need to know about Tensorflow and Keras when they are building machine learning models?
48. How is Pytorch different from Tensorflow and Keras data science libraries?
49. What are different types of activation functions data scientists could use for deep learning models?
50. What is backpropagation in deep learning and how does it work?