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Algorithms was an amazing class, amazingly well taught. At first I thought it was silly, the way a lot of the lecturers discuss algorithms is as if they are somehow within it themselves. The lectures, exercises and quizzes combined had a way of gifting us the knowledge of an array of algorithms I mostly knew only vaguely about until now. The lectures were surprisingly well done, teaching the concepts in a way that can really be understood and walked away with. I heard at first strange seeming analogy verbiage such as "I like it" used as an analogy to a conditional being satisfied during an algorithm's steps, and silly as it seems, it is a really great way to understand the steps behind the algorithm. Take the quick sort lecture as an example, I think that is the one I am thinking of. Just think "I like it" if the conditional is satisfied, whichever one is greater than the other and it is a really good mental pneumonic, it just gets your brain flowing past whether or not one's greater than the other or not and you can mentally fly through the algorithm and see it working in your head and then understand it so later you can make it work in the lab and get quiz questions about it right. I think I have to give the Algorithm II class A+ for teaching algorithms so well and surprising me at the level of understanding the lectures, and combination of lab and homework provide.

 ...you know what I was in the middle of writing something and now I have to drive people around for some reason ... Algorithms was a spectacular class....but I have to go drive people around town now.....I wrote most of this earlier so I'm just putting what I have up for now. (this)

I give the world F- at letting people get their work done and not trying to get in and get people to do stuff like not do their homework.

Right now we are in Intro to Data Science, and, like the networking class, the database class, and several others, I see why it's called 'intro to' because there is a lot! And the it's a lot of gold! How to easily perform mass data analysis the likes of which I had absolutely no idea how to do until now!

I think the class is very well done, especially for how foreign this material is, at least to myself. Everything's 'easy' but not if you don't know exactly, especially if it's new to you, the syntax. For example to get the average of thousands of numbers you can just do array.mean() but it goes even further than that boolean masks are built-in and in our labs we answer complex questions with a single line of code, being led into knowing how to by questions that lead you into knowing how to do them. It's hard to explain but if you saw the last one first I think your brain would explode but if you do them in order, they take time but by the end you wind up understanding how to do what everything asks you. It's quite amazing and especially for how powerful these tools are, crunching billions of numbers with the touch of a character. We've done charting and boolean masking and all kinds of complex cross queries and retrieving data AND probabilities and a little statistics! and supposedly next is shoving all that at A.I.!! I'm very impressed with how much this class is actually teaching me and very much enjoying the knowledge. I can't say I want to be a data scientist now but I can say I will definitely carry these tools with me. One of the videos or something we read somewhere in this last weeks lecture stated that it's now known that to not use these data analysis tools is now understood to be irresponsible. I completely agree, because you wouldn't hire a contractor to build your house with a rock as a hammer, etc. People need to know and use the tools available to us, especially tools as powerful as these, especially if they are free! We do all kinds of data analysis in real estate appraisal (much to my past dismay) but now I'm bringing these tools to the table next time I have any analysis at all to do, and I can't wait. 3-D modeling of data set on the fly if you get these languages figured out, and the sequential (although perhaps a bit vast) knowledge training this class provides, if you can get through it all, I feel like it's really actually teaching me a lot! Like in grade school actually learning math combined with learning Java on my own but being led through a class. I don't know how they did it but I do think it is working. When I took the Java class in the beginning, it was the same, except I already knew Java like the back of my hand. I felt like the class would be too much for someone who didn't already know Java. This class, looking at the material, it looks like too much, but I am getting through it bit by bit and completely, then I get through nearly everything, take the quiz, and realize the quiz itself is actually re-teaching me the very first couple bits I learned day 1 but were lost amidst the knowledge gained in the labs. In other words you gain a lot of knowledge going through the examples in the labs and working through everything you wind up finding a solution to everything. But that doesn't mean you necessarily remembered every way to do everything, at least not in my case, I had forgotten some essential syntax specifics I was meaning go back and review anyway. Then I take the quiz, and I realize it's showing me these exact specifics that had become fuzzy and even partly lost across all the lab work. Like the quiz was saying, 'you do realize you can do it this way, like we showed your right at first, right?'

So I give the class A+ for teaching ability - it is really well taught as well as being exciting material to know, in addition, it looks graphically exciting for other people to see happening as well. There is a lot of material to go through but if you have the patience to make sure to get through it all I think the knowledge is worth its printed weight in gold. That's me a third of the way through the class, wait until the end to see what I'm still saying, we're supposedly about to embark on throwing all we know at A.I. machine learning mechanisms....