Looking for advice to build predictive modeling skills for a future full-time Analytics job. I’ve taken some courses in my masters in program, but not sure I’m prepared for the job market. Any online training programs or resources you could recommend? Example job description open at JPMC: Develop rule and data driven models to maximize service level. Develop predictive capabilities to recommend business improvement. Data mining and modeling skills with Python.

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That's one of those generic descriptions. Don't sweat it. Review your data mining class if you had one ("classic" ML) and you'll mostly be ok. Since you're at a bank, brush up on time series stuff just in case.

A typical ops/applied science interview will have you go over ML at a basic level and then ask you to discuss your specialty. You may be asked to derive OLS, build naïve Bayes from scratch, and draw out gradient descent math. Beyond those, it'll be conceptual stuff (explain boosting, what's a convolution, what are transformers, etc). If you're asked to implement an LSTM on the spot, your interviewer is playing hard ball--unless you're a PhD with LSTM in the name of the dissertation on your resume, of course...

Kaggled does seem like a good way to get some modeling practice, but don't forget the domain knowledge and all the production stuff. I'm talking about understanding where a model can fail, catching feedback loops, recognizing how to match a model to a business requirement, etc. Finding survey papers and blogs on things like tech debt in ML shouldn't be too hard, and will really set you apart from the avalanche of data science new grads who are also all Kaggling.

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Thank you! Appreciate it

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It’s really true what they say about projects providing the best way to learn those skills. I start on Kaggle to get basic ideas for a project, then build off of them to make them more customized and relevant to the concepts/areas you want to focus on

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Thank you!

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