The term “Data Science” is not yet well establish, so interviews for Data Science jobs might include a very broad range of questions, depending on the interpretation of the term by a particular company. In this post I attempt to organize Data Science interview questions in some usable form, but it might also be biased by how I see Data Science myself. I hope you also can find it useful. The sources of the questions are:
• links that I discovered on the Internet,
• my own data science interviews (being on the interviewee side)
The questions are without answers. First of all, the answer that I would write could be bad or wrong, and second, the post would be too big. Also, going through the list and looking for the answers yourself is a good exercise to prepare for an interview.
This list might look scary at first, but it’s very unlikely that all of these questions will be asked during one interview. Very few jobs require applicants to know all of these points. So it’s rather a broad overview of things that may potentially be asked. Don’t let this list of questions discourage you if you don’t know the answer to some of them: chances are that these questions are not important for your interview.

Pandas is Python software for data manipulation. We show that some rather simply analytics allow us to attain a reasonable score in a Kaggle competition. While doing that, we look at analogies between Pandas and SQL, a standard in relational databases.

Following with the analysis of migration flows, I have done next two visualizations. These charts are called bump charts and are very suitable to represent rankings.

Under increasing pressure and facing unique challenges, Human Resources departments are turning to analytics to improve their business practices. Learn what HR needs to be focused on, and what pitfalls they need to avoid.