Oh just be careful one of the early seminal "how to implement" papers has an incorrect description of the vandermonde matrix which results in not being able to guarantee matrix inversions. Be sure to check the errata for older papers.
If you want a good "halfway between theory and practice" experience, I suggest implementing gf256 in Julia. That is, create the datatype and define +, -, *, /. (Along the way g^n and log_g might be helpful too). For Julia > 0.6.2 the builtin lu factorization operator is general enough that once you've definitely the basic 4 operations (and zero(gf256) and one(gf256)) you can call the matrix solve operation \ on your datatype and immediately recover your erasures without having to go through tedious coding of an elimination routine.