Not an expert but I think it depends on the types of equation and their amenability to different solvers via properties like stiff vs non-stiff which you might’ve encountered. Of greater important to “real-life” that is typically neglected at the undergraduate level is numerical linear algebra for matrix operations which are of course fundamental to numerically solving differential equations. These methods accomplish the fundamental matrix operations of linear system solving and eigenvalue decomposition and do so either iteratively or directly; they can also be parallelized which is essential to tackling massive data sets or can take advantage of certain properties like sparsity.