- #1

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- 60

## Main Question or Discussion Point

Some background, I have a Masters in Statistics and Bachelors in Applied Mathematics so It's not like I'm starting from scratch. But after graduation, I've been involved in education. I'm about one year out. I suppose this would hurt me in breaking in the field. I've interviewed at a few places. I actually didn't pass because they asked me a few questions about stuff I should have know, but became rusty on. I've made it the the second round though once. I suppose that's not too shabby. Some questions I flat out didn't know. I suppose that has to deal with domain knowledge specific to the company, so I won't bother prepping for those until the last min.

So what is the best way to prepare given my situation?

Right now I'm reading Introduction to Statistical Learning and Elements of Statistical Learning. I wonder if its worth it.

What stepping stone positions would help me break in?

Would Kaggle Help? I only know Linear regression and Generalized Linear regression off the top of my head. I forgot about Baysean methods and Longitudinal Regression, though I can review if need be.

What about Machine Learning Algorithms? I know how descent algorithms works, but it seems lots of packages have these built in already. So skip these?

I know R and some Python. Some OOP but rusty.

I just need a catch-all list of the skills I need. For the domain knowledge of the company, Ill just prep for those once offered the interview.

I don't care to get into a top company at this point. Most important thing to me is that I kickstart my momentum so that I can actually get somewhere.

I feel like I'm running out of time.

So what is the best way to prepare given my situation?

Right now I'm reading Introduction to Statistical Learning and Elements of Statistical Learning. I wonder if its worth it.

What stepping stone positions would help me break in?

Would Kaggle Help? I only know Linear regression and Generalized Linear regression off the top of my head. I forgot about Baysean methods and Longitudinal Regression, though I can review if need be.

What about Machine Learning Algorithms? I know how descent algorithms works, but it seems lots of packages have these built in already. So skip these?

I know R and some Python. Some OOP but rusty.

I just need a catch-all list of the skills I need. For the domain knowledge of the company, Ill just prep for those once offered the interview.

I don't care to get into a top company at this point. Most important thing to me is that I kickstart my momentum so that I can actually get somewhere.

I feel like I'm running out of time.