Avoid these mistakes that I observed from 100+ interviews

Over the course of the last two years, I have interviewed a lot of candidates for entry-level data science roles. I noticed a few completely avoidable mistakes being repeated frequently by many candidates.

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Photo by Maranda Vandergriff on Unsplash

Here are some of those mistakes and ways to avoid them.

#1: Diving way too deep, way too early

Can you tell me a little bit about XYZ project on your resume?

“OK, so we start by converting PDF to images using pdf2image library and then run Tesseract OCR on it to convert the image into text. …

New research explained

Precision, recall, and F-score won’t get you far. Use these new insightful metrics instead.

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Interpretable and fine-grained evaluation techniques are crucial for making well-grounded improvements in any ML model. Photo by Kent Pilcher on Unsplash


What is NER and why should you care?

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Photo by Nathan Bingle on Unsplash

What is Named Entity Recognition (NER)?

In simpler words, if you want to find out ‘who’, ‘what’, ‘when’, ‘where’ from a sentence, then NER is your task.

Take the following example (or try it yourself here):

Charudatta Manwatkar

Physics graduate. Data Scientist. Nerd. जे जे आपणासी ठावे। ते ते इतरांसी सांगावे।

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