This class was fine. The title is somewhat misleading - you don't really learn a whole lot about "Computational Methods" and there is barely any "Data Analysis". There's a nominal coding component in R, and weekly lab lectures where the TAs teach some stuff about R, but it's very basic stuff, and you can get through all the coding homework parts by following the TA example code. I was somewhat disappointed that with a name like "Computational Finance and Data Analysis for Financial Engineering", there were few useful skills that I learned (ARMA and ARCH models notwithstanding).

Liao isn't the most engaging lecturer, and he can be a little hard to understand at times, but at least his slides were good. The first half of the class is a review of basic concepts in finance and probability. I haven't taken 106F, but some of my classmates said it was all review from that class. The second half covers time series concepts and models, including conditional volatility (ARCH/GARCH), with a final section on portfolio theory which I found interesting. The time series and conditional volatility models are the most useful part of the class for real-world skills, but I took Econ 144 with Rojas in the same quarter, and the time series concepts in this class paled in comparison to the shitshow that was Rojas. The second half of this class would actually be a good preparation for Econ 144.

The tests were much more difficult than the homework (which was not a good preparation). Make sure you have probability and statistics from Econ 41 squarely down, as this class relies on that a lot. Both the midterm and the final allowed "cheat sheets", which were very helpful (if you take the time to prepare them right). Overall, the class wasn't that difficult, and it's a fine choice if you're interested in econometrics electives.