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As a full stack engineer that knows very little about data science. what courses, libraries, etc are worth my time to explore? What should I be well versed in t
by dopeboy 8y ago
As a full stack engineer that knows very little about data science. what courses, libraries, etc are worth my time to explore? What should I be well versed in to be competent in the future?
- reilly3000 8y agoCheck out Kaggle.com and Coursera's intro to machine learning.
- minimaxir 8y agoSince we're on the topic of "what data scientists really do", I have comments about these common recommendations: 1. Kaggle.com competitions incentivise chasing a metric, where very large amounts of man-hours are used to improve a model a tiny bit, which may not have dramatically different performance in a real-world situation than a baseline model. (Kaggle Kernels, which consist of Python/R Notebooks, are better for teaching how to analyze datasets: https://www.kaggle.com/kernels https://www.kaggle.com/kernels) 2. Andrew Ng's Coursera course is good, for an introduction to terminology only. You won't be using anything from the course in real-world use cases (e.g. hand-implementing the matrix algebra behind backpropagation), but it's good to learn what backpropagation is. And please don't say you're an expert in machine learning because you took the course.
- speedplane 8y ago1 - Kaggle is a teaching tool to help amateurs work with real data and real problems. Chasing metrics is what data scientists do all day long, it’s a good resource.
- minimaxir 8y agoAs this submission and other top-level comments on this submission note, "chasing metrics" is not what data scientists do all day long.
- speedplane 8y agoWhat do they do?
- LeanderK 8y agowhile I agree with your second thought, I don't agree with your first. Kaggle is like a sport and therefore can't and shouldn't be comparable to real-life data-science (I mean, who want's to compete in tasks modeled after real life? :D It's often A LOT messier in rl!). BUT I think kaggle still trains you in the right things. In my experience, the winners were almost always more creative and didn't win just through massive hyperparemeter grid-search. I also think that a lot of the usual non-ds struggles are just not as challenging. If I would be in the position to hire a DS and theoretical skills would not be that important (maybe there's already somebody with strong theoretical skills in team), i would just hire somebody based on his kaggle profile. It will probably take some time till he adjusts to real-world DS, but I don't think it will be that of a challenge.
- itronitron 8y agoWhatever you are using in your stack for data access / search probably has some associated capabilities that support data analysis, and hence data science. I would start there.
- TheAceOfHearts 8y agoA boring response, but have you studied stats? Knowing stats and a bit of SQL is enough to get you pretty far with a lot of problems. I'd consider those skills an important pre-requisite to more advanced tools and techniques.
- dopeboy 8y agoI haven't, atleast not since my last university class. I'd love to find some kind of course that trains me on the basics along with applying it through programming.
- speedplane 8y agoLearn how to export a database to CSV, then learn excel inside and out, it’s scratching the surface but a good start.
- gaius 8y agoAs a full stack engineer I’m confused, if you know the full stack already don’t you already know all this? It’s all part of the stack after all. “As a webdev...”
- dopeboy 8y agoI do know programming. I know how to build a backend and frontend. What I don't know is how to take a large dataset and start making predictions based off of it.