[latentdeepspace]

Do people realize that, these interview question collection do not help? I think there is 2 things to address here:

- Interviewers will know "what is known" by every candidate (with the help of these pages) and harder questions will be asked

- If these questions are asked at >junior levels, then RUN! the work will not satisfy you. The interview should be fun, and show the creativity of the candidate. These ones could be answered by anyone who read it a few times. I would not like to work with somebody who only know the answers to these questions and not more

[marcusabu]

I think it's a bit far fetched to assume that recruiters tailor interview questions based on these Github repo's. For me as a junior data scientist it really useful to test my own knowledge and highlight areas which I need to study more.

[fractionalhare]

Lots of tech and finance companies (particularly those with standardized interview processes) will blacklist questions if they're found online. Those companies will constantly check GitHub, GeeksForGeeks and Leetcode to see if their questions are listed there with solutions.

This probably won't be the case for a question as basic as, "what is regression?" But for any intermediate to advanced interview question involving regression, I would expect companies to jealously guard it.

If you're earnestly interested in building and testing your knowledge, I would recommend you read The Elements of Statistical Learning and Data Analysis Using Regression and Multilevel/Hierarchical Models. Also a good upper undergrad textbook in probability, like A First Course in Probability.

[jointpdf]

A couple recommendations piggybacking off of yours:

A First Course in Probability has a lot of problems (with solutions) and worked examples, but it’s light on intuition and pedagogy. It’s not an easy book to learn from, on its own. I highly recommend listening to Joe Blitzstein’s STAT 110 lectures and reviewing the wealth of problems/notes. The greater mastery of probability theory that you have, the easier studying ML and stats is. https://projects.iq.harvard.edu/stat110/home

Elements of Statistical Learning is a true textbook—a comprehensive bible that could occupy you for many thousands of hours. ISLR is the better book for a crash course: http://faculty.marshall.usc.edu/gareth-james/ISL/

There are also lectures and slides from the authors: https://www.dataschool.io/15-hours-of-expert-machine-learnin...

[disgruntledphd2]

Also, Regression and Other Stories is the new edition of the Regression with Multilevel models book, and it's much, much better (especially for n00bs).

[logarhythmic]

A bit off topic but how much of data science work requires this probability/statistics knowledge on the job? I've heard you basically need a PhD to do modelling and "real" data science

[tomrod]

From time to time I'm a hiring manager. I absolutely watch for these kinds of things, be it codegolf type challenges or large banks of interview questions. Depending on the role this can be helpful or hurtful to the expectations I would have on a candidate.

If the goals is to have a skill hire, typically someone who can maintain an existing, well-documented system, then having them know trivial details and banked information can be quite helpful. On the other hand, talent hires I would take in a different direction. If the candidate's only stand out quality is a clear memorization of banked answers I would wonder whether they could work from fundamentals.

[lumost]

Having somewhat standard "objective" questions helps even with senior candidates. You'd be surprised how many senior's would struggle with the basics, or who aren't as senior as their resume would lead you to believe.