Biochemistry lecture and lab courses often contain enzyme kinetics as part of the curriculum, but do not regularly focus on interpreting the kinetic constants. Similarly, as we implement course-based undergraduate research experiences in our lab courses, we encourage our students to produce publication-quality images and determine the enzymatic kinetic constants with the high precision. This "Methods and Techniques" article provides recommendations on preparing for enzyme kinetics while using sample Mathematica or Python scripts to perform nonlinear data fitting using variations of the Michaelis-Menten equation. This article describes why the k[subscript cat]/K[subscript m] value should have greater importance than K[subscript m], and supports renaming the ratio k[subscript cat]/K[subscript m] as a new constant, k[subscript SP], thereby disconnecting Km from our interpretation of this value. Fitting enzymatic data directly to k[subscript cat] and k[subscript SP] instead of k[subscript cat] (or V[subscript max]) and K[subscript m] provides the same values in data fitting, but with lower uncertainties in their values. This article provides a guide to help with experimental design, choosing appropriate modeling equations, and preparing publication-quality graphics. Bridging the theoretical knowledge from lecture to the practical research applications of enzyme kinetics are required for careers in drug development, metabolomics, and metabolic engineering. Survey results indicate that students with this instruction gain confidence in interpreting and producing enzyme kinetic data, as well as in determining kinetic constants from their data and explaining these results. Together, this article provides a guide to help students and instructors as they collect and interpret enzyme kinetic data.