Prepping for interviews (how and what to check)
I’m 2 years into my DS profession, most of my works sadly has been extra knowledge evaluation. I’m on the job market once more and have just a few interviews lined up however nervous that my expertise are rusty. I’m having hassle determining what to deal with for interview questions, I’m clearly not FAANG or any of these firms simply firms the place I can get some strong DS expertise.
Been these websites:
https://www.springboard.com/weblog/data-science/data-science-interview-questions/
https://www.projectpro.io/article/100-data-science-interview-questions-and-answers-for-2021/184
Any recommendation?
Comments ( 4 )
3 major areas: 1) machine learning, 2) probability and stats, 3) programming/leetcode. If you’re not in a rush and have a couple of months to prepare, I’d suggest the following resources:
ML: [ISLR](https://www.statlearning.com/), which also has a free companion course, videos, and slides on EdX
Prob/Stats: if you need a super basic refresher (frankly, I did during my last job search), there are free courses on Udacity that I thought were pretty helpful. [This one](https://www.udacity.com/course/statistics–st095) gives a great elementary overview of basic prob/stats concepts with homework problems. There’s also a couple of great, free video series with accompanying jupyter notebooks that touch on more detailed stats concepts and even A/B testing. I don’t have the links on me handy, but feel free to DM if you need them.
For a more thorough overview of stats, I’d recommend All of Statistics by Wasserman, and this fantastic [course](https://stat510.org/) at UIUC taught by David Dalpiaz.
Programming: DS&A course + leetcode is the way to go. Leetcode is probably the least important concept on this list, but I think leetcode easy/some medium should be sufficient unless you’re aiming for FAANG. I guess basic knowledge of merge and quick sort, binary search, etc is probably a good idea. Free algorithms course [here](https://www.coursera.org/learn/algorithms-part1)
By no means an exhaustive list, happy to take feedback.
Yeah I’ve had minimal ML experience. I can explain difference between a Decision Tree and Random Forest, Linear vs Logistic Regression ect, and have done some in practice but I have a hard time getting into the nitty gritty of it all.
Have you seen [Ace the Data Science Interview](https://www.acethedatascienceinterview.com)? it covers exactly this!
I recommend focusing on networking too. With how the job market is right now networking will give you a big advantage.