A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.
First of all, thanks for visiting this repo, congratulations on making a great career choice, I aim to help you land an amazing Data Science job that you have been dreaming for, by sharing my experience, interviewing heavily at both large product-based companies and fast-growing startups, hope you find it useful.
With an increase in demand for so many Data Scientists, it's really hard to successfully get screened and accepted for an interview. In this repo, I include everything from getting successfully screened and rocking that interview to land that amazing position, make sure to nail it with the following resources.
Every Resource I list here is personally verified by me and most of them I have used personally, which have helped me a lot.
Word of Caution: Data Science/Machine Learning has a very big domain and there are a lot of things to learn. This by no means is an exhaustive list and is just for helping you out if you are struggling to find some good resources to start your preparation. However, I try to cover and update this frequently and my goal is to cover and unify everything into one resource that you can use to rock those interviews!
Please leave a star if you appreciate the effort.
Note: For contribution, refer Contribution.md
First and foremost, develop the necessary skills and be sound with the fundamentals, these are some of the horizons you should be extremely comfortable with -
Build a personal Brand
Develop good connections, through LinkedIn, by attending conferences, and doing everything you can, it's very important to land referrals and get yourself started with the interview process through good connections. Connect regularly with Data Scientists working at top product-based organizations, fast-growing startups, build a network, slowly and steadily, it's very important.`
Describe past roles and an impact you made in a quantifiable way, be concise and I repeat, quantify the impact, rather than talking with facts that have no relevance. According to Google Recruiters, use the XYZ formula -
Accomplished [X] as measured by [Y], by doing [Z]
Keep it short, ideally not more than 2 pages, as you might know, an average recruiter scans your resume only for 6 seconds, and makes a decision based on that.
If you are a fresher and don't have experience, try to solve end-to-end use-cases and mention them in your CV, preferably with the demo link(makes it easy for the recruiter) and the link to source code on GitHub.
Avoid too much technical jargon, and this goes without saying, do not mention anything you are not confident about, this might become a major bottleneck during your interview.
Some helpful links :
If you want to quickly revise your math basics, follow this : https://media-exp2.licdn.com/dms/document/C4D1FAQFzFmR919-Erw/feedshare-document-pdf-analyzed/0/1655384106479?e=1656547200&v=beta&t=9bm4OUyWfM1dQR8LWXsLrGDqYz_Yr_e7TJxHXLXe36I
If you want to quick revise you Stats and ML basics, follow this : https://media-exp2.licdn.com/dms/document/C4D1FAQFLvzVgVxYAAA/feedshare-document-pdf-analyzed/0/1656265480370?e=1657152000&v=beta&t=RD90ZEx3x2VLUGSthO-1uYKadzwTRixKRg3s8j2nvOc
This is probably the entry point of your Data Science project, SQL is one of the most important skills for any Data Scientist.
Case studies are extremely important for interviews, below are some resources to practice, think first before looking at the solutions.
Going through these will definately add extra brownie points, so don't miss these if you got time.
Although this might be optional, but do not miss this if the Job Description explicitly asks for this, and especially never miss this if you are interviewing at FAANG and similar organizations, or if you have a CS Background. You don't have to be as good as an SDE at this, but at least know the basics.
You can't afford to miss this if you are interviewing for a Big data role.