STAT 3280

I'll start by saying Ross is a good person and cares about the students.

Ross is also generally clueless when it comes to teaching and is extremely inconsistent. Assignments were unclear and often tedious. There is a project due at the end of the semester, I would recommend starting this earlier than you think. It takes a good 20-30 hours to complete, and more if Ross decided you did not meet his specs (which were ambiguous to begin with). Not the hardest class but wouldn't recommend taking it with Ross.

The actual course content was interesting and quite useful. I had never thought about data visualization in the manner presented throughout this course with various principles. The HW assignments were mostly useful in reinforcing knowledge about different data visualization principles and tools. The project was also thorough. There were 10 HWs, 15 in class assignments, 2 plot activities, and the final project (with 2 extra credit tokens), but the class was graded on specs grading (either you passed specs on an assignment or not). Here is where the course wasn't so good: the vagueness of passing specs on assignments WAS HUGE. I spent hours trying to figure out what would pass specs, only to email Ross and find out that I still hadn't passed specs yet. If the assignments were extremely clear on what would pass specs, then the assignments are actually enjoyable and pretty easy overall.
Note: DO NOT try and complete the project in a weekend, it takes 12+ hours (although you are allowed to use GenAI to help)

The actual course content was interesting and quite useful. I had never thought about data visualization in the manner presented throughout this course with various principles. The HW assignments were mostly useful in reinforcing knowledge about different data visualization principles and tools. The project was also thorough. There were 10 HWs, 15 in class assignments, 2 plot activities, and the final project (with 2 extra credit tokens), but the class was graded on specs grading (either you passed specs on an assignment or not). Here is where the course wasn't so good: the vagueness of passing specs on assignments WAS HUGE. I spent hours trying to figure out what would pass specs, only to email Ross and find out that I still hadn't passed specs yet. If the assignments were extremely clear on what would pass specs, then the assignments are actually enjoyable and pretty easy overall.
Note: DO NOT try and complete the project in a weekend, it takes 12+ hours (although you are allowed to use GenAI to help)

I really liked this course overall, but there were a few things about it (really one big thing) that annoyed me and generally make me hesitate to fully recommend this course. I learned a lot about what goes into effective graphs and how to use R and R-adjacent tools to create them, which will definitely be very useful in the professional world. The coursework itself was generally not that difficult - there is, on average, one homework and one classwork assignment per week, with an occasional extra assignment or mini-project thrown in. There's also a final dashboard-type project, which you do NOT want to procrastinate on, as others have mentioned.

My only major gripe with this course is the grading system - Professor Ross uses "specifications grading", which in theory means that an assignment either gets a 1 or a 0 depending on whether said assignment passes "specs". In practice this basically means "A or nothing" for every single assignment, with the "specs" in question being very vague and subjective. Graphs and charts aren't an exact science, which makes it hard to determine what "passing" actually looks like in practice unless you attend office hours consistently. I think most people end up doing pretty well in this course regardless, but it was a bit frustrating trying to get my redo tokens and everything to work out along the way, which could have been avoided if a regular percentage grading system were used.

I cannot recommend this class enough. Professor Ross cares so much about his students and I genuinely believe that the material taught in this class is some of the most instantly applicable content at UVA. There is no coding requirement to take this class but I knew a few people who came into it with no knowledge of R and struggled a lot. I would recommend taking an intro class or at least having a background in Python or some other language because the assignments definitely assume a base level of understanding. Specs grading is very reasonable and Prof. Ross gives you opportunities to earn more "tokens" that allow you to redo assignments you didn't meet specifications on.

You need to go class every Friday because there are weekly in class assignments that are required. If you make sure to put in the effort of attending and paying attention in class on Monday and Wednesday, these ICAs are pretty easy. There is homework almost every single week that takes 1-2 hours. Sometimes it's coding, other times it's reading and a reflection. You can use AI sources for everything in this class (ChatGPT is good for simpler assignments, ClaudeAI is good for more complex code). Make sure to start your final project a month in advance. For me, finding and cleaning my data and creating the first two visualizations took the longest. Because I started earlier, I was able to go to office hours before they got super busy and get my questions answered.

ICAs, homework, and plot projects are graded as 1 (pass specs) or 0 (does not meet specs). The final project is graded as a 2, 1, or 0. To get an A, you need to pass 13/15 ICAs, 9/10 Homeworks, 2/2 plot projects, and get a 2 on the final project. If you meet everything else but get a 1 on the final project, you will end up with a B. There is no + grading (i.e. no A+, B+, etc) but there is - grading if you are 1 item short (i.e. if you pass specs on 12/15 ICAs instead of 13/15 but pass enough homeworks, the plots, and the final project, you'll get an A-). At the beginning of the semester, you get 2 tokens that allow you to redo assignments. Redoing a homework or ICA costs 1 token while redoing a plot project or the final project costs 2 tokens. You can earn up to 2 more tokens by doing additional assignments. I didn't do any of the bonus assignments and used 1 token and ended the semester with an A. This is probably one of the easiest and most enjoyable classes for the stat major/minor. #tCFS25

This class is very manageable to do well in, but it does have a couple downsides.

Pros:
- Content is quite straightforward if you have prior experience with R, and it's some of the most readily applicable stuff you will learn
- No exams at all and doable workload (weekly homework, weekly in-class activity, occasional mini-projects, and a final project)
- Specifications grading = you either get a 0 or a 100 on assignments, depending on if you pass the "specs" for each assignment, and final grades are based on how many assignments you pass specs on
- You get 2 tokens to re-do assignments if you don't pass specs and/or late submissions (there are opportunities to gain 1-2 more tokens throughout the semester)
- Dr. Ross makes himself pretty available for students to seek help and ask questions

Cons:
- The guidelines for what passing specs will look like are often pretty vague, so you may need to clarify some things before submitting
- The in-class activities often took more than the 50 minute class period for a lot of people and Dr. Ross did nothing to accommodate that, which was kinda annoying
- Dr. Ross often posts homework late without adjusting the deadline. Thankfully, most of them don't take awfully long to complete, but it's nice to be able to get it done early.

Make sure to start the project early, because it will kick your butt if you wait till the last minute. You are allowed to use ChatGPT or any generative AI tool to help you with the project, so make use of that! Otherwise, the class is fairly chill and the professor is pretty helpful.

Professor Ross highkey makes your life a bit hellish in taking the course. He has 3 day MWF class sessions as opposed to the typical department 2 day/week MW schedule. He spends the 3rd day doing in-class assignments, which are fine within themselves, but class can feel dragged out in some aspects for this extra class within the week. That said, Prof Ross assigns homework in ways that demand a level of excellence to pass his specifications grading. At the end, you become a fantastic data visualizer. That said, you will be stressed in parts of the semester trying to get enough "S" marks for a satisfactory grade as you only have so many make-up tokens and homeworks are spaced out enough that you don't always know where your grade stands. Make sure you do everything early so you're not at the mercy of his busy schedule (as everyone else is) when figuring out questions to pulse check if you might want to revise your work. Also, do your final project as early as you can for the same reasons. Then you'll be cruising for the class.

Keep in mind that Prof Ross is harsh on the students so that they do well. When it really counts (i.e., grading the final project), he's a fair man. However, he might not feel that way throughout the course and grading ICA's (Friday In-class Assignments) or other homeworks. This is simply how he is as a professor in an effort to push students to do well and not bs the class. For that reason, some students don't like him. At the end of the day, I've grown to appreciate professors like him as he's one of the litmus tests for good and lazy students. Also really got to know him as he was my capstone prof and he's quite chill.