[kaminskis]

Your observation is on point - a strong understanding of maths is crucial for data science engineers. The topics you've mentioned - probability/statistics, linear algebra, calculus- are covered in our course, and our learners are expected to build a solid understanding of them gradually. To ensure effective learning of these topics, we space out these topics over nearly all of the course. It's a change from our initial version where we've had it concentrated early in the study - however, we saw that such an approach was quite demotivating to many learners. Next, we use spaced repetition. This happens during our daily standups and project reviews, where senior team leads (expert data scientists) regularly ask questions about topics that might have been covered in previous modules. The questions also tend to focus on understanding (e.g., why something is relevant, how it can be used in business situations) rather than simply recalling formulae or definitions. Compared to Andrew Ng's Coursera course, we require our learners to understand these topics deeper. However, upon graduation, the level of most learners will be less than PhDs graduates, who spend half a decade on learning these topics. Nevertheless, our students will have strong practical skills to do data science, which isn't properly taught in academic data science education (we hear this a lot from hiring partners). What we have as a key goal, however, is to give our graduates enough understanding to: a) be able to work on junior-level tasks effectively from day 1. The libraries you mentioned are helpful here, even though not enough on their own; b) develop the capacity to continue learning maths-related topics independently so that the learner, even after graduation, can continue getting better at maths and feel comfortable with it instead of fearing it