Math and statistics for software engineers — the parts that show up in real work without going through a full math degree. Most courses target one of three audiences: machine-learning engineers who need linear algebra, probability, and calculus refreshers; quantitative analysts working in finance or marketing analytics; and engineers preparing for graduate study or a math-heavy role transition.
The applied core is the same across these audiences: linear algebra (vectors, matrices, eigenvalues), probability (distributions, conditional probability, Bayesian reasoning), inferential statistics (hypothesis testing, confidence intervals), and the calculus relevant to optimization and gradient descent. The honest framing in most courses is "enough math to read papers and understand what the libraries are doing" rather than rigor for its own sake.
