This is Part 2 of the Interview Question series that I recently started. In Part 1, we talked about another important data science interview question pertaining to scaling your ML model. Be sure to check that out!
Interviews can be intimidating, but explaining a project you put your blood and sweat in, shouldn’t be!
A typical _open-ended _question that often comes up during interviews (both first and second round) is related to your personal (or side) projects. This question can take on many forms, for instance:
- Can you walk us through a recent project you completed?
- Can you tell us of a time you were part of a challenging project?
- What are some interesting projects you have worked on?
And trust me when I say this, this question is the best thing that can happen to you during an interview. It lets you steer the conversation in your favor and focus on topics/ML frameworks/algorithms you are confident about!
In this article, we will be decoding how to pick an interesting project, how to structure our answer so that we don’t miss any important detail and also learn some buzz words that should definitely be part of your answer. All this is done in 10 easy steps!
Step 1: Selecting a project.
Goes without saying, while picking a project to demonstrate your technical prowess, make sure it resonates well with the company you are applying for.
For instance, for an e-commerce company, I would go with a retail dataset, for a fintech company I would choose a loan application dataset, and for a healthcare company I would prefer to pick Covid-19 or a breast cancer dataset. The trick is to pick a project based on your target audience. I swear by Kaggle to provide good quality datasets along with some analysis notebooks to get you started.
Also, it is actually a good idea to have some end-to-end projects from different sectors under your kitty.
Step 2: Explaining the data source.
Begin your explanation by specifying where you got the data to work with.
It could be that the data was provided/collected by you at your last company. Maybe you did the project for fun and extracted the required data via Kaggle. You can even mention that it was some open-source data available on the net freely. Perhaps you mined the data (of course ethically) using third party APIs (happens a lot for Twitter data). Whatever be the case, make sure you are revealing the source of your data. Additionally, give a brief overview of some of the columns/features in your dataset.
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