[waterlesscloud]

I'm doing the Computational Finance class via Coursera at the moment, and I've done a number of other courses previously.

I agree the programming assignments in the Finance class tend to be too simple. Most of the code is literally handed to you, you just have to understand it well enough to change it. I also understand that even that can be a major challenge if you don't have the background for it.

But I'm choosing to see the class itself as a starting point. It's a framework for my own explorations into the topics. I can do the minimum and get the minimum out of it. Or I can use what's provided as a base and go further.

The Coursera Algorithms class, for example. Writing code that got the answer was relatively easy, so once that step was done it became about optimizing the code for my own learning benefit.

It's like any educational process, you get our what you put in.

[FaceKicker]

Right, you can get more out of the assignments if you try, but to me the purpose of assignments (versus passive learning - lectures, reading, etc.) is to force your brain to synthesize rather than just comprehend. The ideal assignment, then, is one that forces you to synthesize as many concepts it intends to teach as possible.

Just like you could go back and implement for yourself the skeleton code they handed you, you could also go out and implement everything in the lectures without any assignments at all. It's just that, like you said, the assignments provide a useful starting point. And I'm only saying they could be even more useful by requiring you to implement more of the complete pipeline.

The fact that an incredibly self-motivated person could learn everything there is to know about machine learning with the course as a starting point doesn't mean that it's bad to make the course more useful for a somewhat lazier or less interested person.