Data Science Interview (I only remember 8/9 questions): 1. What is cross-validation, where it is used, how do you do it correctly? 2. What are the differences between supervised and unsupervised algorithms? 3. What are examples of structured vs unstructured data? 4. What is multi-collinearity, why is it bad and how do we deal with it? 5. What is data normalization and what are the reasons behind doing it? 6. How would you handle NaNs and outliers? 7. Describe the life-cycle of a data science project 8. What are various evaluation metrics in machine learning and how would you choose between each one of them? Coding Interview: 1. Print all numbers between 1 and n, omitting multiples of 5 and 7. 2. Variant of the maximum number overlapping intervals problem 3. Write a program to compute the TF-IDF scores of all tokens in a given corpus of documents Behavioral Interview: Typical questions that you can find online, nothing unexpected Data Science Interview Detailed discussion of my past projects Mini Data Science Project I was given some data and asked to explore the dataset and fit a model to predict a certain target on future data points. A simple regression problem. I was given 1,5 hours.
