(Interesting note: just found out that the Cochrane behind the famous Cochrane-Orcutt method was at Monash, http://www.buseco.monash.edu.au/depts/ebs/.).
We started doing the basics of econometrics -- things like regression and ANOVA -- in Excel last year, but moved to E-Views this semester to do more advanced stuff like time-series analysis. That's too bad, because there's a much better program we can use: R (http://www.r-project.org/). The main reason is it's free -- we can download and use it at home, so we don't have to depend on the computer labs being open and free to get our assignments done. Here's a good article that talks about why R is great: http://jackman.stanford.edu/papers/download.php?i=22.
And yes, I know R is mostly command line and teaching it at Monash would take up too much of our time, taking our focus away from the econometrics theory. But R can be customised and tailored to the Monash courses with a little effort; and it has a Tcl/Tk widget set built-in which can be used to implement graphical versions of the stuff they teach us using E-Views -- things like restricted model F tests (Wald tests), AR(1) estimation, weighted OLS estimation, things like that.
That said, I'm still learning R and it's sometimes been frustrating to try matching my results on time series data to what my textbook, Wooldridge, says I should get. Things like ARMA(p, q) estimation seem to be built in to non-obvious places like the gls function in the nlme package. But it works, for the most part. Using Excel after R -- especially R's matrix handling -- feels like going backwards now.