As the engineering culture keeps growing, Data Scientists often team up with other engineers to build pipelines and perform a ton of soft engineering stuff. Job candidates are expected to face extensive coding challenges in R/Python and SQL (Essential and Tricky SQL).

From my past interview experiences, simply being able to code is far from enough. What differentiates experienced programmers from code-camp-trained beginners is the ability to dissect the big question into smaller pieces and then code it up.

It’s my “aha” moment.

For the past few months, I’ve been deliberately practiced to dissect the code and walk through my thinking processes, as many you may notice if you track my progress. I will do the same for today’s content.

Data manipulation and string extraction are essential components of Data Science Interviews, both as an individual subject or combined with other topics. I’ve elaborated on 6 Python questions for Data Scientists in a previous post and provide additional authentic interview questions asked by major tech companies:

Data Manipulation and String Extraction for Data Scientists/Engineers

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