All these curricula seem a bit too complex. IMHO, there's one thing that should be prioritized on top of everything else. The concept of probability, computable probability distributions, and Bayesian inference.
It's the one thing that brings a unifying umbrella to all modes of reasoning under uncertainty. https://probmods.org/ and http://forestdb.org/ seem to be the best resources for this at the moment.
Besides, I dislike Data Science which seems to be a new buzzword. Data Engineering would be more acceptable, as I think people working in companies are building stuff rather than developing new theories. But I don't like it that much.
I agree Bayesian techniques are important, and satisfying intellectually.
The problem is that it is entirely possibly to build perfectly good models without ever touching anything Bayesian (excluding naive-Bayes classifiers perhaps!), and then adding Bayesian techniques will rarely improve the accuracy in anyway.
But I'm happy to admit my understanding of Bayesian techniques is incomplete. It's something I'm working on (https://probmods.org/ is great), but I just haven't found anywhere to use it in anger yet.
So something that no one usually admits is that there are three types of reasoning about stuff (frequentist, bayesian, and nonparametric), and each of them has their pros and cons and circumstances to use them.
So with frequentist statistics, it is really easy to reason about what should be the correct estimator (it is almost always the obvious one). For example, with functional time series (where each data point is a function and not a real value), then it is straight forward to find an MLE - it is just the average function. But defining a prior on the space of twice differentiable functions isn't as easy.
It's the one thing that brings a unifying umbrella to all modes of reasoning under uncertainty. https://probmods.org/ and http://forestdb.org/ seem to be the best resources for this at the moment.
Besides, I dislike Data Science which seems to be a new buzzword. Data Engineering would be more acceptable, as I think people working in companies are building stuff rather than developing new theories. But I don't like it that much.