3 ms·

https://aws.amazon.com/training/learning-paths/machine-learning/data-scientist/ https://aws.amazon.com/training/learning-paths/machine-learn... Patience. Spend

by tmsh 8y ago

https://aws.amazon.com/training/learning-paths/machine-learning/data-scientist/ https://aws.amazon.com/training/learning-paths/machine-learn... Patience. Spending that extra time (it helps if you really enjoy it or can program yourself to really enjoy it). https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700... for inspiration. To answer the question though: I'm not sure what you produce other than maybe blog or publish some analysis using your data skills. And maybe: https://github.com/MaximAbramchuck/awesome-interview-questions#data-science https://github.com/MaximAbramchuck/awesome-interview-questio... (source: non-CS engineer at amazon who watched the amazon videos internally before they were made public. I'm not a data scientist yet but sometimes, esp. when people talk about the challenges of AGI, I think about transitioning.)

ultrasounder 8y ago
Thanks for the links to AWS videos. These were posted here on HN sometime back. Will definitely bookmark them and come back to them. At the moment(literally)i am still finishing up Udacity PyTorch and hope to continue wit the venerable DL4Coders part1 and part2. As someone else had posted, coming up with useful implementations of popular ArXIV papers seems to be one sure shot way of building up a personal brand on Github.
- tmsh 8y ago
  Cool yeah! I listened to the first three courses (Math for ML / Linear and Logistic Regression / Elements of Data Science) on my commute, and even though I thought I knew things, just persisting in "re-learning" the material helped a lot to fill in the gaps for me. I was just browsing the links again too, there's a whole lot of other courses/specialities after that too (if not posted there, maybe elsewhere). I am not an expert, but from what I've heard/seen, being really solid on the fundamentals of regression and feature modeling (and not being afraid to read and apply ArXiv papers) are all key. And eagerness and statistics go a long way and are valuable to companies.
- jafingi 8y ago
  Andrew Ng's courses on Coursera (Machine Learning) are the best ones IMO. Gives you the introductions to the math behind and some practice.

by tmsh 8y ago

ultrasounder 8y ago

Thanks for the links to AWS videos. These were posted here on HN sometime back. Will definitely bookmark them and come back to them. At the moment(literally)i am still finishing up Udacity PyTorch and hope to continue wit the venerable DL4Coders part1 and part2. As someone else had posted, coming up with useful implementations of popular ArXIV papers seems to be one sure shot way of building up a personal brand on Github.

tmsh 8y ago
Cool yeah! I listened to the first three courses (Math for ML / Linear and Logistic Regression / Elements of Data Science) on my commute, and even though I thought I knew things, just persisting in "re-learning" the material helped a lot to fill in the gaps for me. I was just browsing the links again too, there's a whole lot of other courses/specialities after that too (if not posted there, maybe elsewhere). I am not an expert, but from what I've heard/seen, being really solid on the fundamentals of regression and feature modeling (and not being afraid to read and apply ArXiv papers) are all key. And eagerness and statistics go a long way and are valuable to companies.
jafingi 8y ago
Andrew Ng's courses on Coursera (Machine Learning) are the best ones IMO. Gives you the introductions to the math behind and some practice.