What is Reddit's opinion of Applied Predictive Modeling?

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Yes. This book: Applied Predictive Modeling

You really just need to be proficient enough to pass case studies for an interview process. I suggest Python as it is on the upward trend and you won't find R outside of insurance, academia, biostatistics, or companies that only make localized models. R is harder to deploy without the use of R Studio Server and Python is just more flexible with what it can do.

The technical chops aren't the most important part, it's being able to understand the problem and provide the right solutions. Bad data scientists will rush to use XGBoost on everything. Most of the time, people need inferential statistics and so a basic GLM will work just fine.

I have been interviewing a ton recently and you can easily tell the data scientists who come fresh out of bootcamp or watching YouTube tech influencers from the data scientists that took time to understand the material and have a passion for it. I've also interviewed a lot of ex actuaries. I usually root for them, but they're usually embarrassingly bad at understanding concepts. Actuarial exams are so hard, we have no choice but to memorize material and never get time to process the information thrown at us. So when drilled with theoretical questions, they start to stumble. Some think just because they've done a couple models in an actuarial predictive modeling team, that's all they need to know.

Everyone learns differently. What worked for me was to sit down with a book (I suggest Applied Predictive Modeling; you can easily find a pdf version) and just work through the problems. You can find a Python translated version of the example set here. Read through, highlight, take notes etc. I also really like the University of Washington coursera specialization. We actually use that for our internal data science training program.

What you have to keep in mind is that you're competing against DS candidates with Master's and phD's. Some take any job they can get, but others go after the big tech companies. I personally have no desire to work with big tech. So you really have to lean into your statistics background from the actuarial exams along with the analytical skills you've gained from working with awful insurance data. You'll probably also have a better chance transitioning to an insurance based or insurtech role because actuaries have that domain knowledge that straight out of college students won't have.

Interviews usually range from 3-5 rounds.

1. HR Screen (make sure you're not insane)
2. Skills Assessment (conversation to test domain knowledge)
3. Case Study (given a data set, find a solution to a problem)
4. Culture fit, or follow up QA

I've done a few that also do live coding which is nerve wracking. I hate those interviews because no one codes well with someone breathing down their neck.

I did now. Any way of getting a sticky/wiki/FAQ of useful materials /common questions for noobs like me? People can vote/review books and MOOC's / Kaggle competitions, and what was the best for them. Give us newbies something to get started on so we don't have to flood the sticky. Then gives more of a community support rather than one person's suggestion.

For instance

Applied Predictive Modeling

or the less theory version

Intro to Statistical Learning were two books that helped me with understanding statistical models and had applications and exercises in R

R for Data Science was decent enough and had updated packages for making tidy data.

I found the Data Science Coursera Specialization decently useful, but didn't go deep enough. It did give me enough of a taste to know this is the direction I want my career to go in. So I'm hesitant to do more MOOCs.

I also don't have experience in Data Science hiring, but have it for consulting/actuarial. I'd be happy to help critique resumes during my free time for all the graduating students.

In their book, Applied Predictive Modeling, Kuhn and Johnson write (page 95):

> When the outcome is a number, the most common method for characterizing > a model’s predictive capabilities is to use the root mean squared error > (RMSE). This metric is a function of the model residuals, which are the observed > values minus the model predictions. The mean squared error (MSE) is > calculated by squaring the residuals and summing them. The RMSE is then > calculated by taking the square root of the MSE so that it is in the same > units as the original data. The value is usually interpreted as either how far > (on average) the residuals are from zero or as the average distance between > the observed values and the model predictions.

As you mentioned, there are other possibilities. Every measure puts the emphasis differently.

I didn't know about MLR until this post. So without having spent any time with it whatsoever, I would only say that one of the nice things about the caret package is that you can also leverage Kuhn and Johnson's book, Applied Predictive Modeling, as well as YouTube videos of Max Kuhn discussing <code>caret</code>.