I wasn’t sure to take Data & Visual Analytics course this Fall, as there had been some negative comments in the OMSCS Google+ Group for Spring 2017, but everything went smoothly. You can take a peek to the DVA course website. There were 3 homeworks and 2 projects and just 1 final exam as you can see below. There were other 5 assignments called Activities that were worth 1% each, and they were completely optional as you can see if you sum everything up. Activities were not 10 times easier than homeworks (worth 10% each) let me tell you haha.
The exam was open notes/internet, it was still proctored using ProctorTrack as usual, but just because they really wanted to make sure that you didn’t get someone helping you out in person or through the internet (something like a forum or slack request for help) during the exam.
Letter grading rules were nothing special really, you got an A for 90 or above and a B for 80 or above.
I did 4 out of the 5 activities, that assured me 4 extra points, which I though may be useful as I remember someone telling me the final exam worth 30% could be a massacre if there was no curve applied, which is just what happened in previous semesters. I didn’t want to be so exposed to a B or C grade chance, as there was a different course teacher (Dr Joyner), and we didn’t know much about what to expect specially on final exam.
I spent hours on each “1% activity”, and I remember that after I finished each of them I got like “wow and this was only for a 1% of the grade”. But in the end I think it was worth it, I got an A as I wanted.
I think what made things a little easier is that I had already taken Machine Learning (CS 7641) and Machine Learning for Trading (CS 7646) courses before DVA, which helped me a lot, specially for the final part of the course where we saw Logistic and Linear Regression. Additionally, something I think helped me was that I was taking Computing for Data Analysis (CSE 6040), another Georgia Tech course at edX at the same time, which covered maybe a 30% of the material, but from a different perspective (using Python instead of R and from a computational angle).
Another observation would be that really all Data Analytics courses I’ve taken so far have touched Machine Learning concepts in some way, most of them include it by the end of the course material, and it really makes things more interesting and a little more difficult as well.
Something particularly stressful that happened to me while taking DVA was having 50% of the grade on the table by the time I took the final exam, but everything turned out just fine.
In the end, DVA wasn’t as tough as I expected, coming to a very unknown environment and with some hard comments on Spring 2017 course roll out. My expectations were low, but in the end I got a course that wasn’t as difficult or problematic as I thought and I had a lot of fun with data visualization and I got to practice R a little more, as I had already had an R course in Summer, which I recommend to you (GT’s ISYE 6501x Introduction to Analytics Modeling at edX).
PS: If I had the chance to take courses in a different order, I would’ve taken DVA and ML4T courses first, and ML afterwards, as ML was by far a tougher course. Just to make a comparison if you’ve already taken ML4T, I consider ML4T to be more work intensive than DVA, but not necessarily more difficult.
Well, guys this is all, have fun! Good luck picking your courses for next semester. Hopefully I’ll be taking 3 courses next Spring 2018: Graduate Algorithms, Big Data for Health Informatics and Human Computer Interaction. I’m a little frightened by the difficulty of GA and BD4H and taking them at the same time but I have no other option, wish me luck. See ya.