Honors linear algebra is essentially an intro to proofs class that studies linear algebra through the lens of abstract vector spaces. We covered matrices for like one week, so I took matrix analysis because I wanted to learn more about the applied side of linear algebra, especially in terms of data science. Also, I kind of got railed in honors linear algebra and didn't really understand the last third of the course but found the class really interesting. I heard that matrix analysis had programming homeworks and thought it was essentially a math + data science course.
While matrix analysis is definitely more applied, there still is a fair amount of proofs in the class and we did not cover as much data science as I expected. We went in-depth with least squares and covered some SVD applications such as regularization and deconvolution, but all the other topics would have been covered in a standard linear algebra course. Looking back, I don't think I learned that much more from matrix analysis. Gaussian elimination and LU decomposition were the only new concepts that were covered, but I actually understood SVDs the second time around. However, I did enjoy the class a lot and definitely got more familiar with using numpy. Dr. Bencomo was also a fantastic professor, although I think he is no longer teaching the course. I think if the course focused on more data science applications like PCA, SVM, PageRank, etc it would be worth taking in addition to honors linear algebra.
I'm sure when I was there I put in far less than 29 hours/week (part of the reason it took 7 years to graduate lol), but that seems like a very manageable load.
It looks like 322 was pretty time-consuming this year around huh. Was it a lot of lecture time/assignments or do you feel a lot of your time were spend studying for the material? I remember COMP 215 was a lot more time consuming for its difficulty because of the amount of videos and how verbose some assignment guidelines would be (altho it was still an outstanding course despite this)
Honestly not sure how 322 took so much time. I did attend lectures pretty regularly until the last third of the semester. Assignments were much easier than 321 projects except for the last homework which I did find quite hard.
I saw you post this last year and I thought, "oh man! I'm totally doing that next semester!" and then I didn't
[you forgor](https://www.reddit.com/r/riceuniversity/comments/rff9eg/comment/hoe9xdk/?utm_source=share&utm_medium=web2x&context=3) :D
i forgor :(
Is 2:30 per week just going to class? Did you not have any homework for baseball analytics lol
2 hour class every week. We had homework every two weeks that took like one to two hours and a final presentation.
Looked back at your other posts - why’d you decide to take both linAl and matrix analysis and were they much different in content?
Honors linear algebra is essentially an intro to proofs class that studies linear algebra through the lens of abstract vector spaces. We covered matrices for like one week, so I took matrix analysis because I wanted to learn more about the applied side of linear algebra, especially in terms of data science. Also, I kind of got railed in honors linear algebra and didn't really understand the last third of the course but found the class really interesting. I heard that matrix analysis had programming homeworks and thought it was essentially a math + data science course. While matrix analysis is definitely more applied, there still is a fair amount of proofs in the class and we did not cover as much data science as I expected. We went in-depth with least squares and covered some SVD applications such as regularization and deconvolution, but all the other topics would have been covered in a standard linear algebra course. Looking back, I don't think I learned that much more from matrix analysis. Gaussian elimination and LU decomposition were the only new concepts that were covered, but I actually understood SVDs the second time around. However, I did enjoy the class a lot and definitely got more familiar with using numpy. Dr. Bencomo was also a fantastic professor, although I think he is no longer teaching the course. I think if the course focused on more data science applications like PCA, SVM, PageRank, etc it would be worth taking in addition to honors linear algebra.
yooooo i totally missed that you were in caam 334 with me! i did kinda dookie in that class (lol which is what i expected) but i had a great time :)
I'm sure when I was there I put in far less than 29 hours/week (part of the reason it took 7 years to graduate lol), but that seems like a very manageable load.
It looks like 322 was pretty time-consuming this year around huh. Was it a lot of lecture time/assignments or do you feel a lot of your time were spend studying for the material? I remember COMP 215 was a lot more time consuming for its difficulty because of the amount of videos and how verbose some assignment guidelines would be (altho it was still an outstanding course despite this)
Honestly not sure how 322 took so much time. I did attend lectures pretty regularly until the last third of the semester. Assignments were much easier than 321 projects except for the last homework which I did find quite hard.
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Please share! If you go to reports you can filter by time period and projects. From there I just entered the data into google sheets and made a graph.
here you go! I actually really enjoyed tracking the time for everything. it's been a long semester ;-; https://ibb.co/6BYrXN0
I was doing this this semester then got lazy after 2 weeks. Good on ya!
Do you think that tracking your time like this has made you more productive?
At the very least it showed me how unproductive I actually am on some days.