Probability, convex and nonlinear optimization, linear algebra, and statistics are clear wins in many problem spaces, especially machine learning, AI, and computer vision. Ordinary and partial differential equations and numerical methods are heavily used in process modeling and HPC for problems in science and engineering. Geometry, curvature, and matrix theory are heavily used in computer graphics to model shape and movement, as well as computer vision. Number and group theory are the basis of crypto, I believe. In general, calculus and its variations are used to represent and describe solution techniques theoretically before mapping them to discrete solutions using numerical methods.