Interview prep for Senior Data and Applied Scientist till 2025 and beyond

[@theCuriousCuratorML]

List of resources for Applied Scientist Career. 1. www.algoexpert.io (bundle pack 95$) or leetcode (50 easy 70 medium and 30 hard problems) 2. Probabilistic Machine Learning: An Introduction by Kevin Murphy 3. Probabilistic Machine Learning: Advanced Topics by Kevin Murphy 4. Machine Learning a Probabilistic Perspective by Kevin Murphy 5. Algorithms for Optimization by Mykel Kochenderfer and Tim Wheeler 6. CS229 Machine Learning lecture notes at SEE (Stanford Engineering Everywhere) 7. Linear Algebra by Gilbert Strang 8. basic probability and statistics Reading list this year: 8. Trustworthy online controlled experiments by Ron Kohavi, Diane Tang and Ya Xu 9. The System Design Interviews by Lewis Lin 10. Building Data Science Applications with FastAPI 11. Practical ML Ops by Noah Gift & Alfredo Deza 12. Engineering MLOps by Emmanuel Raj 13. Designing Data-Intensive Applications by Martin Kleppmann 14. Bandit Algorithms by Tor Lattimore Optional: 15. Convex Optimization Theory By Dimitri Bertsekas 16. Counterfactual and causal inference by Morgan and Winship 17. Dynamic Programming and Optimal Control Volume 1 and Volume 2. 18. Linear Algebra and Learning from data by gilbert strang

[@theCuriousCuratorML]

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[@kbhaskar36]

Thank you so much for sharing this info.

[@mehular0ra]

Helpful. Thanks!

[@theCuriousCuratorML]

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[@frauduser2673]

Sahu Bhaiya Ji is Back !

[@bhaskarjhadotcom]

Sir, Ek One-to-One session chahiye aapke saath🥲

[@theCuriousCuratorML]

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[@sanjotsagar1458]

Reading all of this is really interesting, but you forget once you read, you've to revise and all, i prefer making my own notes for whatever topics i need to study, so theyre a bit short, how do you tackle this problem of forgetting stuff ? Anyway nice collection of books lol

[@theCuriousCuratorML]

Never miss an opportunity to brainstorm with a colleague. Have been doing it for a while. I also revisit and cover newer topics once every few years. Reading same thing again after few years gives a new perspective because you have also grown since you last looked at it.

[@sanjotsagar1458]

@@theCuriousCuratorML Why do you recommend those heavy math books ? I actually will take those courses in the first semester in my masters but i dont see questions from them being asked in interviews

[@sanjotsagar1458]

@@theCuriousCuratorML Also why does an applied scientist need todo MLOPs

[@theCuriousCuratorML]

@@sanjotsagar1458 they may not but we don't work in silos. Having some understanding of ml ops helps you communicate better. Also it's very company specific and sometimes a part of it needs to be done by you. Another reason is curiosity plus industry trends. Employers needs super human abilities. You should be good at everything 🤣 You don't write poetry but they made you read them in school. Similarly we need to have our fundamentals and we may not use all aspects of it. Some interviewers can ask something from it. Maybe it's not math heavy, may be it is.

[@convolutionalnn2582]

What are maths require for computer vision Research scientist?