Knowledge of basic concepts from calculus, linear algebra, probability theory, and statistics is also required. If you need to catch up, a good resource will be Part I of the “Deep Learning” book or “Mathematics for Machine Learning”. For a deeper dive take a look at MIT courses.
Whether a Data Scientist should be good at math or not – that’s a debatable question, I’d say a holy war. This post by Andrej Karpathy offers a good argument supporting math as a prerequisite for doing ML (and, well, understanding what you are doing).
Check the expression below (it’s from bonus assignment 8). If you are not comfortable with the idea of taking its derivative w.r.t. to \(w\), better refer to the resources listed above.