I've been reading a number of data science books recently (Python, R, etc), plus attempting a number of the MOOC courses, and so on, and the content is reasonably consistent: regression, classification, neural nets, and so on.
I've happily worked through the examples, which make sense, etc., but the struggle is when I get to the day job -- I'm an analyst, trying to get the next level of rigour. The challenge I have is "I have a problem that looks like a round hole" and "the text book looks like a square block"... plus the real world data has holes, data collection issues and so on.
For those of you who have e.g. done graduate courses, are ML experts, etc., what advice do you have on how to building experience in how to apply the different techniques you've learnt?
At the moment, I look at something like Kaggle challenges and just don't know where to start. I recognise that there is something to be said for working out the problem class - classification, prediction, etc -- and then giving it a whirl, but have people found this to be the most effective route forwards?
NB I recognise that this may be against the question asking rules of Cross Validated, but hoping it gets by on the "subjective" path... Googling for "applying data science" or similar terms leads to a bunch of sites trying to get me to enrol in school next year