I’d be more inclined to say that understanding expected value and variance at a baseline level is like fizzbuzz of statistics.

Basic regression at least requires understanding sampling standard error of estimators, distinguishing p-values from the posterior probability a hypothesis is true, transformation of random variables (like for understanding why classical 95% confidence interval bounds are +/- 1.96 * standard error) and some linear algebra.

But I agree the methods discussed, with the exception maybe of support vector machines, are all elementary statistical methods.

What’s sad is that if these represent the fizzbuzz of statistics, it means most jobs in statistics only require you to do tasks easier than fizzbuzz (cursory data cleaning & summary stats). Because in most jobs, dimensionality reduction, SVMs, clustering and boosting would be seen as incredibly advanced, scarce work projects for people to fight over who gets to work on them.