Course Details

Pros: 1. Easy and fun introduction to R 2. Only six projects, which gradually become more complex 3. I find this course exciting, engaging, and entertaining, yet also efficient—a skill that can be applied in the workplace. The last two projects are up to you, so that you can think of them as a portfolio. Cons: 1. Peer-grading - so you need to put in effort, and some peers tend to make tough judgments about things that are subjective to one's taste. 2. You need to make a contribution to Eddisc in time I highly recommend this elective; the time commitment increases towards the end, so set aside time for projects 5 and 6, as R sometimes struggles with heavy projects. You have a one-day gap if you are late, which can also be helpful at times. Takes 1-6 h/ a project + lectures. I was learning, inspired, but also had some time to live. TA is beneficial and understanding, and provided me with excellent guidance.

Pros: 1. Professor actually active in the forum 2. Freshman year of undergrad easy 3. Good intro to R/tidyverse Cons: 1. If you know ggplot, you probably aren't going to learn much 2. Some unclear directions 3. PCA for the 57384573498th time in this program Detailed Review: Not much to say beyond what I said above. Easy A.

Pros: 1. General knowledge about visualization, applicable to any graph in the future 2. Professor and TAs were very approachable 3. You learn by doing, while getting an A if you completed all assignments. Cons: 1. Only one program: R, using ggplot 2. Some classmates had particular expectations about homework and projects, and could grade you based on them. 3. As discussions were mandatory to get a grade, some of them were not helpful or interesting at all. Detailed Review: Professor is really good explaining the topics. I even enjoyed some of the classes. He knows a lot about R and ggplot, and you can find information in internet as well. To get good grades, need to follow instructions exactly. We did not have any midterm or final test.

This class was really, really good. Teaches a ton of concepts very well, and a very reasonable course load. I took it right after Probability & Statistics as my second class and it was really good. This class is a mixture of: 1. Basic data visualization principles: making plots look good, making them communicate the right information, figuring out how to communicate some message clearly when there are four or five variables, how to build color-safe palettes, etc 2. Different types of visualization: histograms, densities, bar plots, smoothed regression plots, visualizing proportions, trend lines, etc. 3. Some very dimensionality reduction techniques at a very high-level. Spent some time on PCA. Talked about nonlinear dimensionality reduction a bit with stuff like t-SNE vs UMAP. Clustering and hierarchical clustering. However, we didn't go into much mathematical detail and the focus was mostly on how to use these in practice. 4. Wrangling and cleaning data, understanding data provenance, processing data in R, long vs wide data format, etc. 5. R, R Markdown, and ggplot: lots of stuff about how R works, how ggplot works, getting used to the functional programming stuff, making nice presentations in R Markdown, and so on. Just a great class and the teacher's lectures were very enjoyable. The teacher is a co-author of famous R visualization package ggplot2, so this is a great class to learn. He's done a lot of visualization for the military, apparently, and was pretty big into the idea of making "visualizations for the generals" - meaning which say a lot quickly for busy people. He has these interactive worksheets each week that show you how to wrangle data and visualize it in various ways. Just an overall fun class and good if you haven't used R much before. Assignments are peer reviewed and can be fairly time consuming, typically involving writing a scientific paper-esque report in R Markdown. However, it was good practice and you can learn a lot about good visualization technique, although some of them took quite a while. My only critiques would be: 1. I wanted more math - really wanted to learn more about the mathematical details of things like t-SNE and UMAP. It was a lot more about how to "talk with data" rather than algorithms in detail. 2. I also wanted to get a bit further into R. We learned how to use it in practice for the assignments, but when I was done I still felt like I wasn't quite comfortable with the language syntax. R is a pretty weird language, with weird things like quotation functions and all that, and I could have used more explanation.

Pros: 1. Good soft intro to using R for data wrangling, viz, and a little regression modeling and PCA 2. Lectures are interesting 3. Content is well organized and easy to follow 4. TAs, Dr. Wilke and fellow students are all highly engaged Cons: 1. Only uses R, ggplot. A survey of other viz tools might be a good use of a class week. 2. Did not cover dashboarding best practices. Detailed Review: I enjoyed this class and found it immediately helpful in my job. Sometimes in industry you cannot pick the tools you use for viz, so a short survey of tools one may encounter might have been useful (i.e. Tableau, Looker, Plotly, Matplotlib, PowerBI, etc.). The course is also focused mainly on individual plots, which fits well to viz for research and analysis. There is little covering dashboarding practices.

Pros: 1. You'll learn how to draw effective charts and summarize data 2. You'll gain some proficiency in R (assuming you want to) 3. Easy A Cons: 1. The class discussions can sometimes be garbage 2. Too many noise/nuisance posts - people trying to score participation points 3. Do you really want to learn R? As another reviewer here says it's such an anti-pattern language Detailed Review Claus Wilke is a well known name in Visualization and the creator/maintainer of several R libraries including cowplot etc. Everyone needs a solid background in data visualization - this course has made me sit up and notice graphs. There is a good reason why WSJ/NYT/Economist pages have those memorable visualizations - Prof. Wilke will show you why. And if you take the class assignments seriously, you can learn how to build those visualizations.R (ggplot in particular) is not a very intuitive statistical programming language but it does produce the best publication quality graphics. Perhaps someday this course could incorporate D3/Java Script and teach web templating which is more relevant to data science and visualizations. My biggest gripe with the course is the edstem/Piazza discussions - some of the posts are really nonsensical and just lobbed in there for participation points. Perhaps the course can rethink it's approach towards participation.

Pros: 1. Good breath of material covered 2. Lectures have great examples 3. Projects are tied in well to course content Cons: 1. 2. 3. Detailed Review: While this course is quite easy, you can learn a good amount on visualization techniques and plotting! Additionally, if you have never learned R, this course can be a very good introduction into the language. IMPORTANT TIP: always read the instructions for projects carefully and follow the instructions exactly!!! Some students may get into trouble for not following the instructions precisely, i.e. having too few or too many figures, or picking a dataset that is outside of the project instructions, etc.

Pros: 1. Good and concise lectures 2. As well organized as things get in this program 3. Easy A Cons: 1. Piazza "participation" required 2. Only focused on one tool (ggplot) 3. Detailed Review: This course hits the goldilocks zone of being easy but also being useful. The lectures are good (much better than most other courses I've taken) but I ultimately didn't watch all of them. I think this is a decent course to take as your first in the program or paired with one of the harder classes (assuming you are working full-time). I took it with Deep Learning. The assignments are pretty unexciting, most of them are basically just telling you what visualization to make and you copy the code from the lecture slides. I feel like this course would work well with a final project where we can find our own dataset and make our own report. I wish this course was more tool-agnostic than it was. Every assignment was in ggplot R code and a lot of the lectures are about getting used to the giant programming anti-pattern that R is. Considering how many tools exist today, I feel like we could learn more broadly applicable information with just markers and paper creating visualizations by hand rather than sifting down with R documentation for an hour at a time. Unless you forget to do a peer evaluation or forget to turn something in, you should easily get an A in this course.

Pros: 1. No exam 2. Lectures and slides are well explained and organized 3. A pretty easy course in general Cons: 1. Part of the grade depends on Piazza participation 2. 3. Detailed Review:

Pros: 1. Great lectures 2. Excellent instructor/TA support 3. Generous grading rubric Cons: 1. Peer grading is tedious 2. Mandatory participation = Piazza spam 3. Detailed Review: This class is easy and only requires a few hours of work per week, but I still felt like I learned a lot. Dr. Wilke and the TAs are especially great. They're more engaged than most in-person instructors I've had, even though they're dealing with the spam-fest that is this class's Piazza. The lectures are really well done too - they follow a pattern of first explaining all the concepts and then showing how to code it in R. It's an effective formula that also does a great job of reinforcing learning over the course of the semester. Even though this course is straightforward, there are some students who have trouble with it. The code is really the easy part - what seems to cause problems for people is following instructions and interpreting the results of their work. Do you want to use a package that isn't on the course list? Don't. Are you thinking of making 11 figures when the assignment says 2? Don't. Can you explain your main takeaway? Can you connect your figures to that point? And can you do it all in 2 paragraphs or less? If not, you aren't effectively communicating results. There is certainly coding in this class, but even more than graphs and tables, it's about communication. Putting that all together, rather than finding optimal colors and shapes for your charts, is really the objective of this course. You'll get more out of it if you go in with that mindset.

Pros: 1. Course material is well put together and taught effectively 2. Expectations for all aspects of the course clearly defined 3. Instructors and TAs are very responsive and knowledgeable Cons: 1. Peer grading is used with a very lenient rubric 2. The participation component of the grade made Piazza difficult to use Detailed Review: This course is very well put together and likely one of the easiest in the program. Every week, lecture videos on two topics taking about an hour each to watch are posted. The lectures always start with a conceptual overview followed by a practical application of the topic in R. Almost every topic is accompanied by an interactive worksheet, which provides an opportunity to work hands-on with R and explore the concepts more if you would like. There are 5 homeworks and 5 projects in this class. Homeworks are essentially completion grades and very low stakes taking less than an hour to complete. Projects are short reports focused on a dataset and a question about that dataset. The format and rubric for the projects remains the same throughout the semester, though projects get more open ended later in the course. Example projects and sample solutions for earlier projects are provided such that the expectations for content are very clear. Outside of the homeworks and projects, a small percentage of the grade is from contributing in any form on Piazza. It appears they lowered the minimum requirement from previous semesters, but I still felt that people were posting/commenting just for the sake of the participation credit which heavily cluttered the Piazza feed. However, the TAs and instructor were very responsive on Piazza and tried to answer questions to the best of their abilities. A controversial aspect of this class is the peer grading, which is used for all assignments, but I felt that the peer grading system is fine as is. The rubrics are broad and lenient enough such that you can get a good grade with minimal effort. Your grade is the median of three peer's opinions, combatting against potentially unfair peer grading. I never had an issue with peer grading, but I assume the course staff is responsive and open to handling grading disputes. Peer feedback is encouraged but not required, so self-reflection on your work compared to the provided solutions is likely your best bet for improvement. I assume the course staff are also open to giving feedback on assignments if you feel you need more. Overall, this class is a solid introduction to data visualization and Wilke's lectures will help give you a great foundation for thinking about what makes visualizations more or less effective in conveying their information. He provides a simple start to many different aspects of R, but it is up to you to expand more on the content since the assignments closely followed the lectures and worksheets and don't really challenge you to try harder if you don't want to. Regardless, this class sets you up for success in data visualization, a key component of this field.

Pros: 1. Covers a lot of material in R, yet the pace of the course is very well managed 2. 3. Cons: 1. 2. 3. Detailed Review: I had never used R before and this course did a good job providing the foundational knowledge to manipulate and plot data in R. Overall, the grading of the course and amount of time required to complete the homeworks and projects was minimal. Despite this, I feel like I learned a great deal. I purchased the textbook for the course, but never opened it, as the lectures did a good job covering everything.

Pros: 1. Useful lectures 2. Doable projects/homework assignments 3. Cons: 1. Mandatory Piazza participation crowds page 2. 3. Detailed Review: This is a good class to combine with a hard one. I took this and Probability and Simulation and worked full time in a demanding job and was okay. The lectures explain all you need to know about R. You learn how to make good visualizations. Watch out for projects-- worth a lot more.

Pros: 1. The instructor, Claus Wilke, is an expert in data visualization and his lectures are concise and engaging. 2. The class is very well organized and the grading criteria is explained clearly. 3. The course covers a lot of useful material while still having a manageable workload. Cons: None This is one of the easier classes in the program but it is also one of the most informative and polished classes as well. The professor is truly an expert in data visualization and it was a pleasure learning from his wealth of experience. His lectures are very engaging as he has excellent communication skills and a good sense of humor. The R programming language is used heavily throughout the class but the emphasis is on how to use the ggplot package. All of the R knowledge you need to be successful in the course is provided in the lectures. The professor is the author of the free, web-based textbook used in the course but because the lectures were of such high quality, I never needed to consult the textbook and rarely needed to consult outside references such as Stack Overflow. All of the lecture slides are available to view on the course website and the code used to generate the slides (R Markdown) is also provided in a GitHub repository. All of the homework assignments and projects are peer-graded but I had no issues with the grading. The grading criteria is explained clearly and is somewhat lenient so you should have no problem getting an A in the class if you follow the instructions. If you need help, you can post your question on Piazza and the TAs or the instructor will respond quickly. I was impressed by how active Professor Wilke was on Piazza. Overall, this is a great class. I learned a lot and the workload was very manageable. To provide context for my review, I received an A in the class.

Pros: 1. The course is extremely well developed; Professor Wilke clearly knows his subject matter and does a wonderful job explaining the concepts clearly and concisely. 2. Project based assignments. 3. Instructor & TAs are responsive on Piazza/office hours. Cons: 1. Peer grading 2. N/A 3. N/A Detailed Review: Professor Wilke does an outstanding job with this course. He is clearly passionate about the subject matter and does a good job of explaining how data visualization fits into the larger degree program. It is a great class to take if you are just starting the program, especially if you haven't been in a formal academic environment for a few years. The assignments are quite easy (almost too easy), but they definitely give you an opportunity to learn the fundamentals of R and data visualization in general. As the class progresses, you will have more freedom to make creative choices for your visualizations and can really dig into the nitty gritty of ggplot2 as much as you would like. If you are new to programming, this class can be a good jumping off point to become more familiar with troubleshooting and reviewing documentation; the lectures/assignment instructions don't always provide the details necessary to generate the right plot. Though, to be fair, those situations are the exception rather than the norm. Professor Wilke typically breaks his lectures up into a theory component and practical component. You will usually get one video on theory/abstract principles for data visualization in general, followed by a second video describing how to implement those principles in R/ggplot2. I personally enjoyed this format, but it may not be for everyone. I have a strong distaste for peer grading in general, but there quite a bit of thought put into this course to mitigate any issues that may arise from peer grading. You also have the opportunity to dispute grades with the instructor/TAs if you believe that you have been slighted. I never had to go that route, but it is worth noting that it is available. All in all, I loved this class. My only regret is that it was the only class I took this semester. Based on the workload, I could have paired this with another course, even while working full time.

Pros: 1. Lecture and slides very well put together 2. Lots of worksheets and code examples 3. Instructor and TAs very responsive and helpful 4. You will learn R Cons: 1. Too easy 2. Piazza is impossible to keep up with 3. 100% peer reviewed Detailed Review: The class was enjoyable with clear, well organized, interesting lectures and assignments. Lots of easy to follow code examples. You will learn usable R. The instructor and TAs were very active on Piazza and quick to respond to questions. I have a strong programming and analysis background and I found the course to be very easy. It didn't feel like masters level work. There is a Piazza participation component to the final grade. There were way too many people in the class to be able to keep up with Piazza and I quickly quit trying to do so. The class is 100% peer review grading (which I dislike). I did purchase Dr. Wilke's book "Fundamentals of Data Visualizations". I like the book and frequently referred to the directory of visualizations in chapter 5 when working projects, but it is definitely not necessary to succeed in the class (and available for free online).

Best Course. Professor is knowledgeable. TAs are supportive. Lectures are masterclass. All the professors in MSDSO should learn from Professor Wilke on how to design and deliver an online course. Best things are Worksheets for practice

Pros: 1. Professor Wilke is very knowledgeable in the subject. He's quite active in the R scene and maintains a few open-source libraries 2. Professor Wilke is active on Piazza to answer our questions 3. The course material is excellent Cons: 1. Content easy for those with prior experience in data manipulation and data visualization 2. All work is peer-graded. The course could use more rigor and better feedback Detailed Review: The professor is very knowledgeable and the course material he created is excellent. I highly recommend this course as the 1st course to take for those new to MSDSO. That said, the course content is a bit on the easy-side for those who have experience in both data manipulation tool/library (say SQL or Pandas) and graph plotting library (say MATLAB, Matplotlib, or Plotly). You're basically applying the same concepts you already know into a R tidyverse + ggplot2 workflow. If you're good with reading code examples and library documentation, you could probably do all the coursework without watching any of the lecture videos (you should though, as the lectures give you plenty of insights). Nonetheless, I think the level of difficulty is suitable for those who are new to such concepts. Professor Wilkes explained the concepts really well and this is how I would like to be taught if I could go back in time and learn again. The mandatory participation turned Piazza into quite a bit of a mess full of useless comments. We have 300+ students enrolled in the course, we should not need mandatory participation for a lively Piazza. I think forced participation is doing more bad than good. Perhaps this could be taken into account in the next iteration of the course. All the coursework is peer-graded so the comments are hit-or-miss. I get this is a 1000USD course so I don't expect all my homework to be marked by TAs, but surely we could for just one or two homeworks? The feedback as of now is a bit lacking. So overall for me it was an easy A (I ended with like 99+%) but if I had known and had a choice (the MSDSO program is still "pick 10 from 10" as of Spring 2022, 1.5 years into the program I find this very disappointing), I probably would not have paid 1000USD to learn concepts I already know only in a different language/library.

Pros: 1. Amazing lectures 2. Good coverage of content 3. excellent worksheets to learn material Cons: 1. Grading system is almost "too" lenient (it almost de-values potential learning via peer grading since some students clearly put in minimal effort) 2. Workload is very light, requires additional external practice to master skills 3. Piazza had mandatory participation and was honestly a cluster Detailed Review: Professor is amazing, and the supplied course material is excellent. Highly recommend this course if you're trying to pair together two courses in one semester as the work load is the lightest in the whole program. This course also provides an excellent intro to R for those unfamiliar with it and tidyverse. Non-project week workload is probably closer to 2-3 hours per week. Project workload also doesn't really ramp up till the second half of the course. At that point I'd recommend adding an additional afternoon for completing the projects (+6-8 hrs that week). Students were required to make minimum contributions to piazza which led to a lot pointless clutter.

Pros: 1. Lectors are engaging and easy to follow 2. Not time consuming (easy course) 3. Cons: 1. With peer reviews, sometimes there isn't any feedback 2. All homework solutions are basically given in lectures so not sure it really tests anything. 3. Detailed Review: I was hoping for a more rigorous course and I didn't feel like there is much to take away. While it was enjoyable, I didn't think it was a graduate-level course.

Pros: 1. Dr. Wilke is highly knowledgeable in R (specifically in ggplot2). 2. Course load is manageable. 3. Dr. Wilke is very responsive on Piazza. Cons: 1. A lot of the course covers content that could be googled, but the course provides a structured context and timeline in which to learn these things. 2. Peer grading lacks the depth of feedback one might see from a professor in undergrad. 3. Data is often too clean to show the difficulties of data science in the real world.

Pros: 1. Great lectures and text book 2. Most applied course in program I've taken yet 3. Peer grading is lenient - makes it easy for a good grade Cons: 1. Peer grading is lenient - makes it easy to not get most out of the course 2. Mandatory Piazza posts means discussion board is full of non-value added comments 3. Detailed Review: Lectures - The first part of every lecture goes over general concepts of what makes up a good visualization. This is material that can be utilized no matter what language or software you are using to generate the graphics. The second part goes over how to generate the graphics in R utilizing the latest in the Tidyverse / Tidymodels framework. The professor also has a shiny webapp at the end of each section which gives you a walkthrough tutorial on applying the code discussed in the lectures. This was extremely helpful and useful. Textbook - The textbook is free to access and goes over much of the same as the first part of each section. Great reference if you prefer a written text. All of the slides are also available on the course website. Homework / Projects - The homeworks are pretty simple. You could pretty much copy paste a lot of the code from the lectures and make a few modifications to receive full points. The projects were also pretty simple, although you did have to put in some more thought in than the homeworks. The last two were more open ended (choose your own questions for 4, choose your own dataset and question for 5) which allowed a lot more creativity on what analysis you did. However with the lenient peer grading rubric, a lot of lower quality reports still got an A even though you wouldn't want to share such reports in an actual work environment. If you are just looking for an A, you can likely get it without investing too much time or effort into the projects. If you want to get the most out of the course though you need to be self-motivated as the grading won't push you above and beyond. Piazza - 14% of the grade comes from participation points on Piazza. The first half of the course was sort of a train wreck. They had it were you needed to make 5 comments or post and get one instructor/TA endorsement to receive full credit. This resulted in students posting insane amounts of comments and posts fishing for an endorsement. In the second half they modified it so you could receive full points for either 6 comments/posts or 5 comments/post and instructor endorsement. This made Piazza much more manageable. Professor Wilke and the TAs were all very active on Piazza and it is clear they are passionate about the content of the course. Overall - It is clear Professor Wilke put a lot of time and effort preparing the material for this course. It is also one of the most applied course I've taken so far in the program. I am already applying the concepts I've learned in class to my day job. Overall great course. Also the work load is pretty light in this class, so it is very manageable to pair it up with a harder course.

Pros: 1. Super interesting material 2. Entertaining lectures 3. Information communicated versus effort is off the charts. Cons: 1. 2. 3. Detailed Review: I don't know how he did it, but he's managed to communicate an awful lot of information in such a way that it doesn't feel like I'm working. There isn't a word wasted in lectures, even his throwaway remarks are all helpful tips, like what he's found useful when evaluating regressions. We have lots of projects with interesting datasets, we're touching on all sorts of analysis like basic regression, PCA etc. He's covered report writing, how to use R, how to pick good visualizations, how to make color palettes and more. This is a class on communication delivered by master of the art.

Pros: 1. The worksheets provided help you exercise what is discussed in the lecture right away 2. The projects and homework assignments are directly applicable to future outside use cases 3. The lectures are succinct and the notes are very well done Cons: 1. Peer grading 2. Required participation points on Piazza 3. Detailed Review: The course itself is fantastic. A great intro to R using R-Studio. The worksheets that Prof Clivan has authored are fantastic and make it really easy to apply what was discussed in the lecture. The project assignments are very well structured and increase in difficulty throughout the semester to naturally build on acquired knowledge. The textbook is authored by the professor. There is no code provided but he does have all of the code used to create the graphics in the textbook on his Github. With a little searching, you can find the course code and learn from it. I purchased a copy but find the online version is just as good- if not better as you can find things more quickly. The lectures follow the textbook plus provide R-code implementation code and examples. Each lecture topic has a provided worksheet (not graded) that lets you play with the code and datasets while exercising what was taught in that lecture set. The only thing I didn't like was the mandatory participation points for Piazza. This led to a mess on the forums with lots of unnecessary posts or people making up questions they didn't really need an answer to. The peer grading is generally lenient but I'm not a fan of this method of grading- but you get what you pay for with a $1000 online course. One good thing about it is that you can kind of see how other people are implementing things and can learn from them. It's just frustrating to spend hours on a project and get zero feedback- it feels like I could have done less work and gotten the same outcome. For example on one homework- I made a clear error on a required regression line (I did a line plot instead) and there was no feedback, nobody seemed to catch it and there were no points counted off.

Pros: 1. Relatively easy class 2. FANTASTIC lectures 3. Very involved teacher / TAs Cons: 1. 2. 3. Detailed Review: I don't have a negative thing to say about this course. To be fair, I am only partway through the semester, but this class has been so fantastic so far that I needed to leave a review. I wouldn't be surprised if this course turned out to be my favorite of all classes in this program. So, just to get it out of the way, this class is relatively easy compared to the other courses in this program. I wouldn't be surprised if it turns out to be the easiest of all the classes. BUT, completely ignoring that, it is also a perfectly taught class. The teacher, Professor Wilke, had a hand in designing / improving ggplot2 and so he is an expert in the material he is teaching. The class is formatted in this way: Each week, you get lectures. The lectures are usually split into a couple different parts (first, a theoretical discussion on why we use certain visualizations, ideal situations to use them in, etc and then secondly, an in-depth look at the code to create the visualizations). The teacher also provides all lecture material in powerpoint slides. Each week, if you feel the lectures were not enough for you to grasp the material, the teacher also has provided interactive R workbooks for you to practice writing the code yourself. Each week or every other week, there is usually an assignment of some sort (homework or projects). Homeworks are smaller and take up 17% of the grade while projects are a bit more involved and take 67% of the grade. The remaining portion of the grade is based off participation (literally just commenting / posting / replying on Piazza). In order to receive your grade for your assignment, you do have to complete peer reviews (usually 3 or 4 per assignment). The assignments are all directly related to the material taught in the previous / current week, so the class feels very cohesive. This class should be an easy A for anyone who takes it, but it's not just that the material is easier than the average course in the program. It's also just taught by a teacher who clearly wants you to succeed and he has provided all the resources you'll ever need to do well. The only complaint I have seen about this class is from other students regarding the participation points. In my opinion, these are basically free points since all you have to do is make 5 interactions on piazza (posts, questions, comments, replies to other people, etc) and get a single one of your interactions endorsed by the teacher or TAs (and they are very liberal with endorsing things). I don't mind this process, it's pretty brainless and doesn't take long to do it. But other students have fretted over feeling like they have nothing to post, so they feel pressured to make up a dumb post just to get the points. A downside to this is that there ARE a lot of pointless piazza posts, so you do have to wade through piazza to find what you're looking for sometimes.