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Comment Not to land on you, but... (Score 1) 265

You really shouldn't generalize about what psychology majors are going to be like. In the department I did my Ph.D in, psychology was closely allied to biology and ecology, and there was another department across campus that did social psychology. Some of the psychologists were pretty darn quantitative. But they were being quantitative about the mind, which is (my bias) maybe more interesting than the examples you used last time you taught calculus. Also, while the majority of students may be psych majors, some will be from other majors. What do you want future lawyers, school principals and politicians to know about statistics? This is your chance to teach them. Sooo, they might have good math skills, or not. But you can't assume that they know calculus, obviously, so you probably want to use a textbook that treats stats as a tool for understanding patterns in data, and goes easy on the theory behind maximum likelihood estimates and so on. I like Perry Hinton's Statistics Explained, but it really depends what you are trying to teach the students to do. http://www.amazon.com/Statistics-Explained-Science-Students-Edition/dp/0415332850/ref=sr_1_1?s=books&ie=UTF8&qid=1340578689&sr=1-1&keywords=statistics+explained If the psychology majors are any good, they may be more used to thinking clearly about surveys and tricky experiments than you are. Perhaps you can structure the course so that learning goes both ways.
Software

Submission + - Is parallel programming just too hard?

pcause writes: There has been a lot of talk recently about the need for programmers to shift paradigms and begin building more parallel applications and systems. The need to do this and the hardware and systems to support it have been around for a while, but we haven't seen a lot of progress. The article says that gaming systems have made progress, but MMOGs are typically years late and I'll bet part of the problem is trying to be more parallel/distributed.

Since this discussion has been going on for over a decade with little progress in terms of widespread change, one has to ask is parallel programming just too difficult for most programmers? Are the tools inadequate or perhaps is it that it is very difficult to think about parallel systems. Maybe it is a fundamental human limit. Will we really see progress in the next 10 years that matches the progress of the silicon?

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