Reviews

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.

Great class with interesting lectures, but you will learn more from the labs. I was a little bit daunted when signing up for this class by all the comments saying that the labs are taking extermely long to complete. It turns out that if you are familiar with C/C++ enough, it might not be that bad. I finished lab 1 in 12 hours, but found Lab 2 (CUDA/Trust) taking the most time (> 20 hours for me) but wedid have 4 weeks to work on it and it was my fault to procrastinate until the last weekend. The rest of the labs all took 12~14 hours to complete, including researching and report writing. I am mostly new to golang and completely new to Rust - but they also turned out to be all right. I do wish that the lecture materials are more closely connected to the labs. An advice would be to start early on the labs to get a feel of how you can manage it. Plan well, and you will enjoy the projects & lectures and learn a lot.

Pros: 1. Lectures are short, interesting, and to the point 2. Concise and sufficient notes as required reading (i.e. the "textbook") 3. Teaches the nuts and bolts of QC/QM, which not all courses do Cons: 1. Homeworks take a considerable amount of time and effort 2. Not being great in math or proofs made this class difficult for me 3. OH are usually crowded and may be difficult to get your questions in Detailed Review: I joined UT specifically because no other online program I knew of had a QIS class. Prof. Aaronson speaks at a lot of events, but none of his lectures are available online. Having now taken this class I'm thoroughly impressed with the quality of the material and the teaching ability of Prof. Aaronson. The TAs were exceptional in their support. If you're wondering how to budget time, this was my breakdown per week: - W1: 8.75 hours [1.5 hours (readings); 3 hours(lectures); 4.25 hours (hw wk2)] - W2: 8.75 hours [3 hours (hw wk2); 2.25 hours (lectures); 1 hour (OH); 2.5 hours (recitation wk2)] - W3: 10.75 hours [2.5 hours (lectures); 3.5 hours (recitation wk3); 1 hour (peer-grading hw wk2); 1 hour (readings); 2.75 hours (hw wk3)] - W4: 15 hours [1 hour (hw wk3); 5.25 hours (lectures); 1.75 hours (recitation wk4); 4.75 hours (hw wk4); 1 hour (OH); 0.5 hour (readings); 0.75 hours (peer-grading hw wk3)] - W5: 3 hours [1 hour (OH); 1 hour (wk5 recitation); 1 hour (hw wk5)] - W6: 17 hours [2 hours (OH); 5.5 hours (hw wk5); 1 hour (peer-grading hw wk4); 3.25 hours (lectures); 0.75 hour (recitation wk6); 2 hours (readings); 2.5 hours (hw wk6)] - W7: 24.5 hours [1.25 hours (OH); 7.5 hours (hw wk6); 0.5 hour (peer-grading hw wk5); 3.25 hours (lectures); 1 hour (readings); 7 hours (recitation wk7); 4 hours (hw wk7)] - W8: 21.25 hours [6.25 hours (OH); 6.5 hours (hw wk7); 0.75 hour (peer-grading hw wk6); 4 hours (midterm prep); 0.25 hour (peer-grading hw wk7); 2.5 hours (midterm); 1 hour lectures] - Spring Break: 4.25 hours [2.75 hours (lectures); 1.5 hours (readings)] - W9: 8 hours [2.25 hours (lectures); 0.5 hour (readings); 1.75 hours (recitation wk9); 3.5 hours (hw wk9)] - W10: 10 hours [2 hours (OH); 5.75 hours (lectures); 1.5 hours (recitation wk10); 0.75 hour (readings)] - W11: 20 hours [1.75 hours (OH); 6.5 hours (hw wk10); 3.25 hours (lectures); 0.75 hour (peer-grading hw wk9); 0.5 (peer-grading hw wk10); 2.25 hours (recitation wk11); 5 hours (hw wk11)] - W12: 11.5 hours [1.5 hours (OH); 0.5 hours (hw wk11); 3.5 hours (lectures); 2 hours (recitation wk12); 4 hours (hw wk12)] - W13: 12.5 hours [7 hours (hw wk12); 1 hour (OH); 0.5 hour (peer-grading hw wk11); 3.25 hours (lectures); 0.75 hour (recitation wk13)] - W14: 15 hours [3.5 hours (hw wk13); 1 hour (OH); 1 hour (peer-grading hw wk12); 3.25 hours (lectures); 4 hours (readings); 0.25 hour (recitation wk 14); 0.5 hour (peer-grading hw wk13); 1.5 hours (hw wk14)] - W15: 6 hours [0.25 hour (peer-grading hw wk14); 5.75 hours (final prep)] - W16: 8.5 hours [4.5 hours (final prep); 4 hours (final)]

Pros: 1. Detailed, challenging, rewarding assignments that drive home concepts of parallel programming. 2. Gain familiarity with many languages: C/C++, Go, and Rust. 3. The lectures are engaging and fun to watch. 4. No exams or quizzes Cons: 1. Very time consuming class. 2. The textbook didn’t provide much value beyond the lectures and assignments. 3. Probably not a great first class (recommend taking AOS and/or virtualization) first. Detailed Review: Excellent class that anyone who is interested in systems should take. There are no quizzes or exams, just 5 pretty significant projects. Lecture notes and projects are available online prior to the start of the course, which is useful if you’re unfamiliar with any of the programming languages and want to get a head start on the work. No need to watch lectures before doing the projects. This class is a ton of work, but it’s deeply rewarding and you’ll come out on the other end having learned quite a bit.

Pros: 1. Easy - balances out other more difficult courses. A good course to pair up. 2. Good balance of theory and application. 3. Most engaging lectures I've seen in the program thus far. Detailed Review: Overall this has been my favorite course in this program. This course has been (along with Data Viz) the most practical and immediately useful course to me personally (working as a DS in a healthcare field), and Dr. Parast did a great job of balancing the theory with practical applications. I liked how this course wasn't a marathon like the others and provides a needed balance to the other more difficult courses in this program.

Pros: 1) Excellent Instruction and Real-World Relevance: Professor Parast delivers clear, engaging lectures that connect data science concepts directly to practical healthcare applications. 2) Strong Emphasis on R Programming: The course provides valuable hands-on experience with R, reinforcing learning through well-aligned lectures, assignments, and code-based exercises. 3) Organized and Supportive Structure: The course is well-organized, easy to follow, and thoughtfully paced, with highly effective TAs who offer strong academic support and guidance. Detailed Review: I took DSC 395T with Professor Layla Parast and found it to be an excellent and highly valuable course, particularly for anyone interested in applying data science methods to health and medical research. Despite having a strong background as a scientist, I gained new insights and practical skills that have directly benefited my work. The course is well-designed, with a clear focus on real-world applications. Professor Parast is an outstanding instructor. Her lectures are thoughtfully organized, easy to follow, and enhanced with high-quality video content and detailed notes. She adds meaningful commentary beyond the slides that helps tie theoretical concepts to practical healthcare problems. The integration of lecture material, notes, and R code is particularly effective in reinforcing key ideas. A major strength of the course is its hands-on approach to using R for data analysis. Through guided coding assignments and examples, I learned R and significantly improved my skills in data manipulation, visualization, and modeling. The homework and exams are directly aligned with the lecture materials and coding exercises, so staying engaged with these resources prepares you well for exams and homework. The teaching assistants were also a strong asset to the course. They were helpful, responsive, and kept the pace of discussions and support sessions well-matched to the course content. Their support made it easier to work through more challenging material and get timely feedback on assignments. While the course assumes some background in probability, statistical inference, and regression, no formal prerequisites are required. I would recommend pairing this course with "Principles of Data Science" for a more comprehensive learning experience, especially for those looking to learn or strengthen their skills in R. Overall, DSC 395T is a well-paced, expertly taught, and highly applicable course. I strongly recommend it to students at all levels who want to develop practical data science skills with a focus on healthcare. It delivers on both conceptual depth and practical value, making it a worthwhile addition to any data science training path.

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. Really interesting topic with a lot of immediate relevance for current research and applications. 2. Lectures are well done, assignments progress evenly in difficulty, introduce you to a broad range interesting applications and techniques using PyTorch. 3. Content feels updated with discussion on latest approaches from past few years 4. Midterm did a good job at reflecting material taught in lectures, plus lots of prep materials were given for it ahead of time (extra homework problems, previous exams) 5. Plenty of time given for the Final Project (4+ weeks), which was a cool opportunity to use a real, modern and fairly large deep neural network through the Huggingface library for exploring a topic of your interest. Cons: N/A Detailed Review: Highly recommended. Even if you're not interested in NLP as a specific discipline (I wasn't), you'll learn a lot from this class about modeling problems in general, statistical techniques for approaching those problems, and get lots of experience with PyTorch (plus Huggingface which is really cool). Recommend touching up on your probability theory (nothing more than an undergrad level) which will help you understand some of the approaches discussed, and spend a little extra time getting to know the PyTorch library (there are plenty of tutorials online to quickly get you up to speed to do the homeworks, plus its covered well in the lectures).

Pros: 1. I definitely know what quantum computers are now. 2. Class elegantly builds up from the basics (quantum states) to Shor's algorithm, covering pretty much all the details of how it could be implemented, hypothetically. 3. Lots of engaging math, you will work to apply different kinds of linear algebra, proofs by reduction, information science principles in situations where it's really not obvious what the results will be. You'll come away feeling like you really figured some stuff out. Cons: 1. It's a demanding class. Some of the homeworks require creativity in proving things that are totally not obvious; even going to office hours and seeking out 'hints' sometimes I spent crazy amounts of time to (mostly) finish the homework. Hardest class I've taken in the program by quite a bit, although it was basically an un-altered undergraduate honors class so it's hard to argue that they need to make it easier. 2. Peer grading (it is what it is, again this encourages actually understanding the material. 3. Lots of work on arcane sub-topics that, unless you end up being a quantum scientist, you will never think about again. Detailed Review: Class is excellent, and the work load led me to understand the material in a much deeper way than I otherwise would have. But it was a lot. Took this along with a super easy course + full time work - it was a slog of a semester, but kind of awesome. Final was pretty difficult and long - no trick questions, but not trivia either. "Textbook" by the professor closely mirrors the course, makes the homeworks a lot easier - don't forget it's there!

Great class and really liked the topics covered in this class. As mentioned above the TA was very helpful and works hard to find solutions when you are stuck. My only complaint is that for the summer I wish we had a bit more time for hw3 and the final project, as we were constrained for time and many people felt they didn't have adequate time to to do either of them. Hw4, while probably the hardest, had 2 weeks to do which helped a lot. This class is a lot more work during the summer session due to it being a shorter semester.