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Check out Kaggle.com and Coursera's intro to machine learning.

minimaxir 8y ago
Since 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 ago
  1 - 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 ago
    As this submission and other top-level comments on this submission note, "chasing metrics" is not what data scientists do all day long.
    - speedplane 8y ago
      What do they do?
- LeanderK 8y ago
  while 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.

Since 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 ago
1 - 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 ago
  As this submission and other top-level comments on this submission note, "chasing metrics" is not what data scientists do all day long.
  - speedplane 8y ago
    What do they do?
LeanderK 8y ago
while 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.