Fk0FQSMCiLF4JSvmuaGBhg==2025-10-01T05:27:43Zsummer 2025
Introduction to Computer VisionThere was a lack of clear expectations, rubric was not giving meaning that reports often were heavily penalized for not including details despite lack of clarity in what should be included.
Rating: 2 / 5Difficulty: 5 / 5Workload: 8 hours / week
4YpbCjbYv2B3IvKU2aXZhg==2025-09-25T09:08:07Zsummer 2025
Introduction to Analytics ModelingI had mixed feelings about this course.
On the positive side, I found the course content and rigour to be a solid way to kickstart my OMSA journey. The professor is super engaging and enthusiastic, and his lectures explain both the theory and examples very well. That said, the homework is largely self-directed - you’re expected to code independently (+ learn to code for those with no R background). The first homework was by far the most challenging for me (and possibly for other students too), which caught me off guard. After that, the remaining assignments were more manageable. Just note that there are one or two of the later weeks that need Python, which can be tricky if you don’t have prior experience.
Peer grading was frustrating at times since it’s inherently subjective - often the difference between a 90% and 100% depends on the grader, and many seemed to approach it as a chore rather than a peer learning opportunity. Personally, I would have preferred more (or any) TA grading, though given that homework makes up only a small portion of the final grade, it’s hard to complain much. There’s also a mini project, but it’s fairly light - no coding required, just a short report of your problem/use cases and proposed analytics.
The quizzes are what really determine your final grade. As long as you keep up with the lectures, you should be fine, though some of the questions are worded in a tricky way. Unfortunately, the quizzes don’t tie in with the homework at all, which further adds to my impression that the course workload feels disjointed.
TLDR: Great professor and lectures, but homework doesn't really align with the effort required (so don’t stress over it too much).
Rating: 3 / 5Difficulty: 3 / 5Workload: 10 hours / week
4YpbCjbYv2B3IvKU2aXZhg==2025-09-25T08:51:20Zsummer 2025
Data Analytics in BusinessI was initially hesitant about taking this course because of the negative feedback on its lack of structure and usefulness, but I also could not run away from it since it's a compulsory module.
That said, I was fortunate enough to take the revamped version, and I’d say I actually enjoyed it. The good thing is that the weekly homework is essentially a direct 'copy and paste' of the lecture exercises, with a few adjustments to the data and variables. As long as you grasp the lecture content, you’ll do fine. This also becomes a bad thing, as the course does become a little too straightforward for graduate-level.
Still, as one of my first two modules in OMSA, it was a good way to ease back into studying without being overwhelmed by coding and troubleshooting on my own. Taken alongside ISYE6501, this course felt like a simpler, more structured version, though there was definitely room for the homework to be more challenging/stimulating.
TLDR: Engaging, straightforward, and an easy “A". Seems much better than the previous version of MGT6203.
Rating: 4 / 5Difficulty: 1 / 5Workload: 3 hours / week
ZeM0UXNY1NJrZY00h0Ehzw==2025-09-23T16:53:03Zsummer 2025
Network SecurityEnded with A. The content of the class isn't too difficult. The exams were extremely easy - I stopped watching lectures 1 week in because of how bad they were, instead reading notes I found from past students. I don't think any of the assignments are hard. However, most assignments are ambiguous in what you need to do and are tedious. Would recommend taking it in the summer, pretty light course load, went on multiple week long vacations during the class.
My two main problems with the course were:
Rating: 2 / 5Difficulty: 2 / 5Workload: 8 hours / week
S4qbEenZksl0HwqhSf0WrQ==2025-09-16T03:47:49Zspring 2025
Machine LearningI'm only stuck on the first project and already about to drop this class. The problem - they do not teach you ANYTHING that you need to know. The course pre-requisites say that as long as you've taken and "introductory course in artificial intelligence" class you'll be okay. That is 100% a lie. Do not take this class unless you ALREADY KNOW everything about ML, because they will NOT teach you.
They will throw TONS and TONS and TONS of concepts at you on the first project and not explain them at all in any way. You're just expected to google everything and learn it. It's clear that the assignment was written with the expectation of somebody who's already used scikit-learn to make multiple ML models before and is intimately familiar with it, NOT just "someone who's taken an introductory course in Artificial Intelligence" like the course webpage says.
the lecture - "heres the basics of how decision trees, knn, svm, and neural nets work"
the assignment - "you're expected to already know how to stratify data, you're expected to already know how to cross validate data. you're expected to know terms like ROC-AUC, PR-AUC, prevalence baseline, MAE, MedAE, MSE, MAPE, SGD, confusion matrix, etc etc etc. (And those are just the terms i see on ONE PAGE of a 16 page document). You're expected to already be intimately familiar with scikit-learn, because the directions will make reference to implicitly, so if you don't already know it you'll just have no clue that it's even referencing sci-kit learn. You're expected to already know a ton of data science concepts because the instructions will make reference to them without explaining them. If you deviate from these instructions in any way you will be docked 50% of the points on this project. Furthermore, these instructions will be self-contradictory unless you're already intimately familiar enough with ML and data science to be able to discern the true intent of the instructions."
Don't even bother watching the lectures, they are a complete waste of time for the purpose of the project. The project assumes that you're already a master expert of ML, the lectures only cover the very basics.
The only way to survive is to have ChatGPT open and just ask it to explain everything to you, bit by bit, piece by piece. ChatGPT will teach you how to complete the assignment because the instructors won't. Hope you're got the premium subscription. Even then, it's nearly impossible.
I can't even comment on the quizzes, the tests, or the grading, because I can't even get there yet.
TL;DR do NOT take this as your first ML course in OMSCS despite what the course pre-reqs say. It's clear you should take it as your LAST.
Rating: 1 / 5Difficulty: 5 / 5Workload: 60 hours / week
Eucmhp9VNtNHzZK5/sDYfw==2025-09-06T08:07:57Zsummer 2025
Computer NetworksIt was a nice and informative class on computer networks. Highly recommend that you don’t take this during the summer like I did, the projects can be a bit difficult if you’re not really too proficient with Python. I think I would of rated it higher if I took it in spring or fall.
Rating: 3 / 5Difficulty: 5 / 5Workload: 15 hours / week
R/mDYLrlsAd4LJEtZJWniw==2025-09-03T13:27:20Zsummer 2025
Software Development ProcessAssignment 6 man. I was on track to get through this class with ease but then got hit with the 20% on this assignment and ended up with a C...
TAs were okay, group project was okay, individual project was actually kind of fun.
Please take assignment 6 seriously.
Rating: 2 / 5Difficulty: 3 / 5Workload: 8 hours / week
9BTN/KCHgyi5KsCx7q4SKsFJPu8fv5zAo/LMa3Dho8M=2025-09-01T05:36:45Zsummer 2025
Statistical Modeling and Regression AnalysisThe content is not difficult but the coding homework is brutally time consuming. Often there are 15 problems with each problem having 3 sub problems. The timed, proctored, closed internet coding exam was also brutal. I'm someone who usually finishes timed proctored exam in less than half of allotted time, but I used the full 4 hours to the last minute for the coding exam in this class.
I know it's doable in Python, but it's definitely easier with R. TAs officially "strongly recommend" (in their own words) using R.
The professor is mostly absent. Assignment instruction is often confusing and frustrating as you constantly have to chase for clarification.
Rating: 1 / 5Difficulty: 5 / 5Workload: 18 hours / week
CNFJRTK8LziCE4STdYeT/Q==2025-08-30T06:37:52Zsummer 2025
Introduction to Cognitive ScienceThis class is incredibly easy and most can finish with a solid A if you put in some effort. It's also an excellent work-ahead class and is ideal if you foresee a busy period of your life coming up or simply want to pair with a more difficult class. Although I would avoid pairing this with a report-heavy class like ML as the final report legitimately requires a significant amount of time.
Quizzes (15%): The quizzes are open-book and you have two attempts. They are heavily based on the lectures and associated readings and you can easily figure out the answers from skimming the readings. The quizzes are also open from day one and can be knocked out in a few hours.
Individual exercises (35%): For summer we had 6 individual exercises in which you are presented with readings and questions to answer. The requirements are pretty clear and a slightly deeper reading of the literature will give you everything you need to know. I'd say just give them what they're asking for as clearly and succinctly as possible.
Term project (45%): The term project is the hardest aspect of this class and honestly the most time consuming. The project has four milestones in which you pitch your project, submit the final project, and prepare a presentation on the project. There is an optional milestone that you can submit if you want further feedback early into your project. The project itself is an individual effort and you have options of writing a literature review, performing a cognitive science experiment, or creating a computational model. I would say START EARLY and budget more time than what the course requires (~100 hours). I had to take a couple days off work before the due date to bring this one to the finish line. I would also say to put some serious thought about what project you want to do early on as pivoting halfway will seriously put you behind.
There are no real downsides of this course except for the fact that the TAs can be a bit unresponsive at times. However, the grading is VERY generous so you really can't complain. If you address all of the points for the exercises and term paper clearly, then you will get an A.
Overall, what you put in is what you get out, but I honestly feel that putting in a significant amount of effort is not rewarded as equally as just doing the bare minimum. I will say that the course content itself is interesting and very thought-provoking, but again, the class itself is not challenging and it leaves plenty of space for you to take the initiative if you want to dive deeper into the material.
Rating: 4 / 5Difficulty: 1 / 5Workload: 10 hours / week
yS5aZpZ4oKps04hQR6K/lw==2025-08-25T13:39:54Zsummer 2025
Introduction to Graduate AlgorithmsI took this class over summer full time and it was brutal, I passed with a B, but it was not an easy B and there were a lot of times during the entire semester I was worried I wouldn't make it and considered dropping but I refused because I needed this course to graduate unfortunately.
The difficulty in this course doesn't come from the material but in the structure of the class and how it is run. I actually really like algorithms but this class was more like a writing class on how to write pseudo-code as prose.
Homeworks are supposed to provide guidance for exams but the homework feedback is provided ultra late like a few days before each exam when you turned it in weeks before. This wouldn't be so bad if the exams were curved or not worth the entire grade, but yea that's just how this class is structured to be high stakes and stressful for no reason in my opinion.
Oh yea, and the notes are all closed notes and closed book, again adding artificial difficulty like giving a really hard math problem to solve by hand and saying no calculators. Yea, it's hard especially when you need to learn how to solve so many different cases in such a short period of time but it's also not really valuable or useful.
I can't really provide much advice other than to focus on completing the lectures, do the practice problems, and focus especially on the homework problems because you're very likely to get a variation of a homework problem or lecture problem for your exam moreso than a practice problem (Although they do still show up and that's why it's such a huge time sink, you will cover around 20 different problems, but only get asked 2-3 on every exam for the free response which is worth more than half the exam).
The TA office hours (Jove Luo) were really helpful. I feel bad for the cohort that has to take the class if he decides to take a break because the OH provided me so much help when I felt lost.
He covers the practice problems and gives numerous examples of the desired answer format, it was very helpful to me, but really the most frustrating thing about this class is that you really don't get enough opportunity to practice your own solution writing and getting feedback until basically the day the test arrives so that's why so many people bomb the exam, it's not hard, but it is when you have had 0 time to actually practice by getting feedback from the staff and not living on an island.
The worst part is that they're very strict with the point removal, but also make a lot of mistakes. I made 2 regrade requests and won both, 1 regrade removed 20% of my entire exam grade for a mistake the TA made and then they returned it back 100% admitting the error, but that entire process of seeing my bombed test score, then reviewing the notes, having to write for feedback, then write a regrade request was just all unnecessary work that was caused twice for me.
Sadly, this is a required course for a lot of specializations so there's no avoiding it, but hopefully this info is somewhat useful for where to focus and a strategy to pass because it would have saved me a lot of time, instead I spent so much time trying to cover everything and it really was an unnecessarily stressful experience to an otherwise well-run program for professionals.
Rating: 1 / 5Difficulty: 5 / 5Workload: 40 hours / week
Mg8VbB4wqnj8Dv27PhxvHQ==2025-08-25T00:42:27Zsummer 2025
Game Artificial IntelligenceA little about me: I have a bachelor’s degree in CS and about 1.5 years of SWE experience.
Courses I took before this one: IIS, SDP, and VGD.
I think this course is overall good and pretty manageable in the summer. The grades are basically made up of 8 different projects (and quizzes). You don’t really need to take VGD before this course either.
It’s the kind of class where you get out what you put in, I think some people can get by with ~5 hours a week, others might put in closer to 20 depending on how much effort you want to invest.
The only frustrating part for me was that you only get 2 submission attempts per assignment on Gradescope, but other than that, this course is great, definitely recommend.
Rating: 5 / 5Difficulty: 2 / 5Workload: 7 hours / week
4bAmToXB3t9tFYKjhRjd0Q==2025-08-23T23:43:22Zsummer 2025
Digital MarketingStudy for the midterm and final. Doing bad on those are the only way you could get anything lower than an A.
Rating: 5 / 5Difficulty: 1 / 5Workload: 5 hours / week
R1Kj70Cla4uAqu/Wj9AR4Q==2025-08-23T13:46:37Zsummer 2025
Introduction to Information SecurityLet me start with my TLDR: Its a fun course to learn surface level security knowledge. Be suspect of the difficulty and time commitments it currently shows. The class used to be much easier and less rigorous in the past. Advice: START THE PROJECTS EARLY!
More in depth:
This is my second course in the OMSCS program, with the first being ML4T. I also work full time as a Data Engineer.
I imagine that when the TA's put together the course content, they did so with the vision of 'if i was training a jr cyber analyst, what should they know?'. If you are interested in learning that material than you will enjoy the content and projects.
Overall I would say that this class is not 'easy' but 'straightforward'. Its 100% project based, so if you put in the effort you should get an A. This is a class with a wide breadth of topics, that are totally unrelated to each other. So, you can spend 8 hours on the API project but 50 on the binary exploitation project (yes, that was me). I would assume that for at least one project, you will have knowledge gaps and have to spend more time than you think.
How it worked (for summer 25): Projects would open and you would have until Sunday midnight est to complete them. Usually the next project would open on Friday so if you got ahead of the game, you can have up to 9 days to finish each project. If you are not confident in binary exploitation, I would HIGHLY recommend working hard to make sure you have the 9 days for this project.
What I liked:
You will use a decent amount of tools, languages and techniques that will assist you in a jr. software/security career.
Finding the flags is a nice dopamine rush :)
TA's really go above and beyond in creating some helpful tutorials and relevant sources for research.
All the projects are 'do-able'. I really had no assembly and computer architecture knowledge. But I was able to finish the binary exploitation project with an A. But to do so, I had to do a crash course in all of the above, along with the C debugger and was able to complete the project. Again, its all about the effort/time you put in.
Once you finished your project, you were 'done'. Nothing else to worry about until the next project opens.
What I didn't like:
No relevant lecture content provided by the professor. I think that relying on the TA's for 100% of the content is shameful. For a supposed expert in the field, why is there no learning content for the projects? It seems very lazy and it's obvious the professor does not care at all about this class. Relevant lectures/overview on the topic for each project should be a bare MINIMUM for a professor. This would GREATLY improve the learning experience. I left the class getting an A, but honestly if you don't take diligent notes during your research into the topic and review them, its very easy to just brute force projects and not learn anything.
I also think it is a huge waste that the database security project was 'last' because it was one of the better projects in my opinion.
Time spent:
For average hours per week I calculated the time from the day the semester started to the end, so there was 2 weeks where we didn't have any work because there was no final.
I put together a list of time projects took me to get an A on all of them. Keep in mind, i'm a slow worker and liked to take my time to go in depth on the topics taking notes to learn more. If just 'getting the A' is your goal, you can probably cut these times in half because you can definitely game this class.
(Man in the Middle): 30 hours (ML): 20 hours (Binary Exploitation): ~50 hours (API Security): 8 hours (Web Security): 20 hours (Log4Shell): 25 hours (Database Security): 20 hours (Cryptography): 20 hours (Malware Analysis): 15 hours
Rating: 3 / 5Difficulty: 3 / 5Workload: 18 hours / week
+vrBbYGMp/QLf7FttbIhIw==2025-08-23T06:25:03Zsummer 2025
Knowledge-Based AII enjoyed this course. It helped me frame my perspective about AI Agents. The foundations on how an AI Agent need to be built is interesting. Newly introduced ARC AGI is fun. Summer was little short in duration to do ARC AGI project in truly KBAI manner. However, I feel I have broadened my thinking and knowledge in this field after this course.
Rating: 4 / 5Difficulty: 3 / 5Workload: 12 hours / week
+vrBbYGMp/QLf7FttbIhIw==2025-08-23T05:55:20Zspring 2025
Machine Learning for TradingI took ML4T as my first course in OMSCS. After an academic gap of 9 years, I felt this course helped me slide smoothly into academics. This course though some find it easy, I believe is well organised with excellent TA Support. I would suggest keep reading all posts comments on Ed discussion. If you have any doubt, it can be put on Ed and TA almost immediately responds which is really helpful. Weekly TA live discussions are excellent as this being an online course, it is quite challenging to get doubts clarified so even if you cannot attend the live, recording will be available within a day. Even the questions put on Ed will also be discussed in those sessions. Though project descriptions are extremely lengthy, TA goes through it fully in those sessions.
Rating: 5 / 5Difficulty: 3 / 5Workload: 12 hours / week
nDdwarmsB6c18RBejfPSgw==2025-08-23T03:06:26Zsummer 2025
Artificial Intelligence Techniques for RoboticsCourse was well structured and the syllabus was rational - you learn about robot localisation, path finding, PID, and SLAM (alongside other smaller topics). The projects were effective in forcing students to actually understand and implement these concepts, although the difficulty gap between the lectures (high level) and starting on the project can be quite big.
Exams were closed book, proctored MCQs which were challenging but fair. Other than these 2 major components, there's almost no busywork unlike other courses which was refreshing too.
Overall, good course to take during summer that won't kill you, but also challenging enough to feel like you did learn something useful by the end of it.
Rating: 4 / 5Difficulty: 4 / 5Workload: 14 hours / week
RadUHmNAM2Eel0fvwEKV6ClfqQzavWZESRkwywSXU/0=2025-08-22T00:26:08Zsummer 2025
Advanced Topics in Software Analysis and TestingLectures are relevant to assignments
Assignments are interesting (mostly C++, one Typescript, one Java)
There is an exam for the lessons not covered in assignments
5% extra credit from participation
Rating: 4 / 5Difficulty: 3 / 5Workload: 12 hours / week
vyP8VyExLVMcHYPxZVnhQg==2025-08-20T23:05:50Zsummer 2025
High Performance ComputingThis was my second course at OMSCS, and this was much harder than I could have anticipated. Firstly, taking this course in the condensed Summer semester is not recommended. This course is already extremely dense, and the assignments require a lot of time investment. Definitely take this course alone. This course is very theory-heavy, but for good reasons.
That being said, this was a very rewarding course, where discussions with other students greatly increased my understanding of the material. Despite the heavily theoretical material, the lectures are very well structured, and the TA's are very supportive.
There are 2 exams in this course, and they are both very difficult, however, both exams are curved. The course also had adjusted grade scales, where a C was >=64%, B >= 74.5%, and A >= 85%. Start the assignments as soon as they are available, as Spring and Fall semesters have 3 weeks between assignments, whereas Summer semester only has 2 weeks between assignments. There is also an extra credit assignment towards the end of the course, which in our case was worth an additional 10% of our grade.
Rating: 5 / 5Difficulty: 5 / 5Workload: 18 hours / week
vyP8VyExLVMcHYPxZVnhQg==2025-08-20T22:54:48Zspring 2025
Graduate Introduction to Operating SystemsI took this course as my first in OMSCS. As someone who has previously taken an OS course in undergrad, this course was a lot more engaging than the other, where the lectures were well structured, with a toy store analogy running through the entirety of the course, which helped contextualize these concepts very well.
That being said, this course is definitely on the harder side. It is jammed packed full of material, with very rewarding assignments. However, the exams are very difficult, with them constituting half of your grade.
The time investment on this course was on the higher end, as some of the assignments can have you scratching your head for a good number of hours, with the difficulty only increasing as the assignments continue, definitely set aside a lot of time for this, and take it alone or with a lighter workload course alongside.
Rating: 5 / 5Difficulty: 4 / 5Workload: 12 hours / week
sdvfEoGvnS6Gh4tetk1XCw==2025-08-20T03:20:34Zsummer 2025
Artificial Intelligence Techniques for RoboticsI took this course over the summer so I believe the pace of assignments is a little bit faster than in a normal semester. I have a background in CS, this was my 5th course in OMSCS and I ended up with an A. Overall, this course is very manageable and the autograder is fairly lenient for the projects, which is nice because it can get pretty frustrating tuning your solutions to pass all of the test cases. I felt that the projects in this course were satisfying and I do feel like my Python skills improved in the end. I feel like I gained a good introduction to some robotics concepts and was genuinely interested in the course content throughout. Additionally, the TAs were really active on the Ed forum. My complaints about the course are that the lectures have a lot of typos, which made them a little bit frustrating to watch at times, and as I already mentioned, the projects can be time consuming when you have a working solution and it boils down to tuning. I personally ended up just calling it good enough for a few of the projects and settling with a mid-range A instead of spending many more hours for the 100. For a few of the concepts, I also had to spend some extra time with additional resources they provided in the course and some external resources as well to really understand them. I found the exams to be relatively challenging but fair. You get two chances to take them, but the questions are different. And notably, I and many others found the second time to be more challenging. Whether that was random or not I don't know, but just keep in mind the second time could be harder.
My advice for people taking this course would be:
Overall, this course is worth taking in my opinion. I would consider this an easy A class if you just put in some effort. It was a fair pace for summer and I think if you have a lot of time to spend on school, it could definitely be paired with another course in Fall / Spring.
Rating: 4 / 5Difficulty: 3 / 5Workload: 10 hours / week
X/XKjRvrkYmriHluqQ/7Bw==2025-08-19T03:14:08Zsummer 2025
Machine Learning for TradingDon't believe the average stats from reviews, this class was made significantly more difficult and time-consuming since it was handed over from Prof Balch to Prof Joyner. Now it's way too heavy on reading and writing and was my least favorite class out of the 6 that I had already taken in this program. There was just way too much effort doing pointless tasks that don't result in any learning because a lot of effort was put into paying attention to incredibly minor details in both the projects and exams.
Project descriptions are way too long with information and massive deductions scattered everywhere. You can literally get 20% off a project that's worth 25% of your grade for putting the wrong line color on a graph with a single line. There were some complaints about pointless details and the justification was that they needed to include these fine details to make sure that you were reading the instructions and weren't cheating.
At the same time, the rubric can be too specific and too vague at the same time, also resulting in massive deductions. There are some places where the grading criteria is "sufficient detail" which just opens up a can of worms on how much detail is sufficient. Compared to when the class was taught by Prof Balch, there's just way too much emphasis on the writing aspect that makes the time spent on projects to easily be 20 or even 30+ hours each project (with a report). The final project even took me over 40 hours due to writing and building up on project 6 which was ridiculous since there wasn't much learning but rather rote tuning your model and writing about it.
The exams are also nearly insufferable due to being composed of multi-checkbox questions which turn it into a 100+ true/false question exam. True/false questions are horrible because everything becomes a trick question and the inclusion of a single word or comma becomes more important than actually understanding concepts. Plus some of the exam questions are on stupid, irrelevant details that have absolutely nothing to do with machine learning or trading. Without giving any specific details to respect the honor code, the questions are about as relevant as asking what was the weather in a particular city on a particular date.
Oh, and don't bother watching the lectures as the additions to the course have made them nearly irrelevant to the class despite being so enthusiastically produced by Prof Balch. The exams were disproportionately focused on the required readings which were so abstract and far beyond the scope of the lectures that I often wondered what the readings even had to do with the course other than to provide a question bank to ask very specific details off of.
I had an A for 3/4 of this class but lost massive points on a project worth >20% of my grade due to said issues with line color and "sufficient detail" which brought me down to a low B and made the rest of the class incredibly stressful. I squeaked by with barely 2 points over the threshold for an A but spent >25 hours each week. The changes have made this not an easy class anymore and it was by far my least favorite class in the 7 that I have taken in this program. There were "harder" classes like GIOS and AOS that took me less time just because they were way more interesting and didn't waste your time on tiny pointless details and going on a model-tuning fishing expedition.
Rating: 1 / 5Difficulty: 5 / 5Workload: 28 hours / week
sMGfjdC0D9guZn30kGqjbg==2025-08-18T21:52:41Zsummer 2025
Human-Computer InteractionThis course was very helpful to ease into the program. Joyner does a great job teaching the subject while keeping it interesting. I had no problems getting high marks on the papers by ensuring I followed the rubric. The tests were straightforward and open note, so they ended up being fairly doable. The only part that causes me grief were the quizzes. These are close note, short answer questions that took me the whole time to answer. I did great on the first two quizzes and OK on the last two. Overall ended with an A and the course was great to get me back into the swing of school. I recommend taking it seriously, but this course makes for a great summer class.
Rating: 5 / 5Difficulty: 3 / 5Workload: 12 hours / week
a9PlvSp4B/hPzSB/H2oE5A==2025-08-18T19:16:25Zsummer 2025
Data Analytics and SecurityThis is my fifth OMSCS course. I chose it as an easy summer class, and it is easy—the curve means almost 90% people gets an A?—but I wouldn’t recommend it. The material is shallow and the coding assignments feel random; I’d rather solve a clear problem than explore without a goal. As others have noted, it’s basically Python/R 101. The grading rubric is vague: I took an unexpectedly low score on the first part of the final assignment, which dropped my grade from 99 to the mid-80s. I eventually earned an A by writing the final paper in a way more academic style—something never made explicit—but the whole thing felt like guesswork compared with my other courses. Even as a light companion to a tougher class, I’d skip it for your sanity.
Rating: 2 / 5Difficulty: 1 / 5Workload: 3 hours / week
5gzgma/eRK6s/VzvgWN7Ww==2025-08-18T18:49:37Zsummer 2025
Deep LearningI think this is an interesting class with quite well organized lecture, reading materials and assignment. I spent on average less time on the assignments (~20 hours) than the one in ML classes (~35-40 hours). There are 5 close book quizzes in the summer, so it kind of force the student learn the principles and algorithm, which is beneficial. Also enjoy doing the group project, make sure to find reliable teammates!
Rating: 5 / 5Difficulty: 4 / 5Workload: 20 hours / week
XzosBcdhnDW15JHR+dckJw==2025-08-18T04:28:18Zsummer 2025
Special Topics: Business Fundamentals for AnalyticsThis course combined both the best and worst components of OMSA. Despite not looking forward to yet another required watered-down business class, I thoroughly enjoyed the first 75 percent of the course. Unfortunately, the Marketing module was so ineffective and disengaging that it tainted my overall experience. Final grade: 97%.
The Good
The Bad
TLDR Excellent TAs, self-assessments, and simulations, but the Marketing module was weak, exams relied on memorization, and the course lacked rigor. An easy A that shows both the best and worst of OMSA.
Rating: 3 / 5Difficulty: 1 / 5Workload: 12 hours / week
WtbvySDnlL3VacxhINR7DQ==2025-08-17T21:20:59Zsummer 2025
Natural Language ProcessingAn excellent course. Lectures are very informative and well structured and I learned a lot from the projects.
Easily one of the best courses I've taken so far (7 courses in).
Rating: 5 / 5Difficulty: 5 / 5Workload: 6 hours / week
K6NJirXopbAX3IHW+z3fuw==2025-08-17T00:37:32Zspring 2025
Network SecurityI have based my ratings here for difficulty and workload on manual student work excluding AI tools (When class policy excludes them).
TLDR: Take this class if you know what you are getting into and are ready for battle. Curves are generous and an A is absolutely within reach if you're not new to the topic and/or are willing to grind it out.
This class seemed to be harder on me than most. I came in with an almost nonexistent security background and middling general tech exp. For whatever it's worth.
Most of your grade is project work done in modules on provided VMs with instruction sets that were fairly vague. For this cohort, our absolute All-Star of a Head TA (Melissa) made a huge difference. By combing transcripts of office hours, Ed, and the class Discord, I felt MUCH more confident in approaching known sticking points. Also, the grader is mostly transparent if I remember right, which is a huge plus.
Despite the difficulty of the projects themselves, the instructional team was VERY generous with curves and extra credit. So if you want a class specifically on this subject with an emphasis on simulated attack/defense scenarios, I would take it. If you want an easier A, I wouldn't.
Rating: 4 / 5Difficulty: 5 / 5Workload: 28 hours / week
K6NJirXopbAX3IHW+z3fuw==2025-08-17T00:03:44Zspring 2025
Machine Learning for TradingI have based my ratings here for difficulty and workload on manual student work excluding AI tools (When class policy excludes them).
TLDR: Until AI tools are allowed substantially or workload is reduced, do not take this class.
I think this is a class that has so much it wants to share with its students. It really does want its students to do well and learn a lot. Professor Joyner has proven this intent in my opinion. You can also tell by watching the lectures (Which you're also better off skipping to save time, sadly), that Professor Balch (The course's originator) was a true leader who also wanted his students to succeed. What I heard/read (Total conjecture here), is that the class was somewhat different back then, with more focus on coding and less focus on academic writing, of which Professor Joyner demands a LOT, in addition to what was already a complete course.
So, my belief is what you see in the combined ratings here reflects good will towards Prof Joyner, but coasts on expectations based on what the class used to be. It also reflects my belief that the student body (Who is relying heavily on AI tools to complete everything the course demands of them) is engaged in a bit of a course design battle with an instructional team who is possibly trying to work against AI tools by increasing raw workload rather than with it.
There are at least 30-50 pages (plus slides and lecture vids) to ingest each week, 12 short Quizzes based on them, 2 tough exams, 8 coding/implementation projects with mostly transparent graders, 5-6 7-10pg academic journal articles to write based on project work, and 2 pts of extra credit. With respect, no one is doing all of this with 8 hrs a week while maintaining academic integrity.
The exams and journal articles you're expected to write are where you will lose most of your final grade points. It is likely the most pedantic class I will ever take in my life, with instruction sets longer than the 7-10pg articles you will ultimately write. Buried in these elaborate specs are many, many minor-scope details, for which missing even one detail could knock you down a whole letter grade. When you consider that it is possible on a given assignment to lose enough points to total 100 many times over, I think you have to consider that something is wrong with the assignment's design, despite the instructional team's stated refusal to consider this to date. My advice is to use the spec for the project + research paper and follow/address literally every single sentence of the spec in your code and journal article.
The exams are worded strangely, likely in an attempt to curb the effectiveness of AI tools, which you are strangely allowed to use on the exams but not the assignments and articles. They are quite hard even then to prepare for, and are aggressively timed and curved heavily. I'd recommend a deep understanding of the underlying concepts of the readings using generated outlines as opposed to a deeper reading due to time constraints.
The TAs could be a bit more 'human', but all-in-all they were honestly excellent and professional. Per the norm, there is no contact with the Professor.
The tragedy of the course is that the material itself is not hard or particularly advanced. If it was pruned significantly, my personal belief is that students would better master and retain all key concepts the course conveys, and be less pressured to take shortcuts. But if I'm being honest, I would not take this or another class offered by Prof. Joyner again after this experience.
Rating: 2 / 5Difficulty: 5 / 5Workload: 30 hours / week
K6NJirXopbAX3IHW+z3fuw==2025-08-16T22:52:16Zsummer 2025
Network Science: Methods and ApplicationsAs always, I have based my ratings here for difficulty and workload on manual student work excluding AI tools (When class policy excludes them). TLDR: If you want an 'Easy' A at the expense of an emotional roller coaster, feeling as if your very grade is up to chance for most of the semester, this class is for you.
I'll try to be fair and not hyperbolic here. But in all honestly, this class is an embarrassment and it has really fractured my respect for the entire program.
Quizzes account for a significant part of the total grade and they are essentially open-book. I like this in theory, but the problem is that questions are extremely poorly worded, thought through, and tested. They are clearly written by someone without the level of English necessary for their proper formulation. Quiz questions at times are empirically impossible to solve, due to either poor grammar or to edge cases the instructional team has not considered. When legitimate calls for clarification are raised on ED, they are often shot down unprofessionally. The response tends to be, to paraphrase: "Deal with it. We'll fix it with a curve later. Stop crying about it." Or just silence.
Projects, which are more like walkthroughs with unit-level challenges via jupyter notebook, account for the bulk of your final grade. For the most part they're reasonable, both in terms of raw workload and difficulty. Where it fails is, as before, with poor curriculum design and administration. Entire sections of certain assignments were unsolvable due to under-prepared notebooks, helper functions, and/or broken opaque graders. The running joke among students was to wait as late as possible to complete each assignment, to minimize time and stress wasted due to such errors. In one particular assignment, many of us received failing grades. After the shock wore off, it became clear the onus was on us to manually report the problem individually. God only knows how many graded assignments have otherwise been accepted as final and impacted final letter grades or worse. We are criticized at the program level for 'grade chasing', but now I'm starting to believe this may be a necessary practice after this experience.
The head TA was mostly responsive and professional, although showed a lack of maturity in acknowledging the well deserved feedback from students, and also in accepting responsibility for the incompetence of his subordinates, whom in many cases left entire assigned discussion threads unattended at key times.
If I could go back in time, I do not think I would take this class without a new instructional team and new materials.
Rating: 1 / 5Difficulty: 4 / 5Workload: 15 hours / week
n09SLVGtZHm61UNoxOwaQw==2025-08-15T23:49:44Zspring 2025
Statistical Modeling and Regression AnalysisAs in every course in this program, and in life =), you get out what you put in, in both effort and intention. The right tools, resources, and support are provided, but should be paired with personal commitment and some tolerance. Canvas and other systems are not perfect, nor are students or TAs/professors. I did not encounter anything the instructional team failed to address properly and I always felt supported and guided. I found this class very useful, as it thoroughly covers the principles behind regression, deeper than in introductory courses. The slides are in R, however as part of a revamp, there is now sample code supporting the lecture problems in both R and Python. Homework and exams can be completed in either language. The professor and TAs are very knowledgeable and helpful. Office hours are especially valuable, as some TAs share a great deal from their own experience. Overall, the instructional team consistently tries to add value and contribute. I had no issues with the video lectures, but for those who prefer it, the transcripts paired with the corresponding slides are available in a single document, not all courses have that. Exams are now open book, whereas before they were not. In my opinion, HWs and exams do evaluate the understanding of concepts. As with anything new, some memorization is needed, but it’s up to you whether to stay at that level or really make sense of the concepts. I definitely got a lot out of this course. Chose to complete it in Python, fingers crossed that Time Series will be available in Python too.
Rating: 5 / 5Difficulty: 3 / 5Workload: 15 hours / week
UVvO6DCuCk74Qptze6p9WQ==2025-08-15T20:45:48Zspring 2025
Machine LearningThis course is very difficult, but I also think some may be too harsh on it. I've been told that grading comes down to the TA that you have, so I'll simply discuss my experience with the course and you can take it or leave it:
The Pros:
-You will learn how to use Python libraries and you will learn the fundamentals of machine learning. -While ML4T projects were focused on you understanding how to program the various ML algorithms, all algorithms that you need for the projects exist in pre-established libraries - you can even use AI to help you code. -The focus is on the application of ML algorithms, particularly in terms of analyzing data, how to use the various techniques, train learners and avoid overfitting. -Lectures are fun an engaging for newcomers -If you like writing, and find yourself to be good at it, you will enjoy this course -Class is curved!
The Cons:
-If you don't like writing, this may not be an enjoyable course for you -While the material is good for learning the fundamentals, it is a touch outdated -While the lectures are entertaining, they are extremely verbose - I recommend watching them at a faster speed.
With all of this in mind, I highly recommend this course for anyone who wants to go down the machine learning path, without a ton of knowledge of the field, and here are my tips that I hope will help you succeed and hopefully ace the course:
For whatever my results are worth, my grades on the projects were 86, 91, 103 and 85 respectively as well as an 80 on the final, and my grade was curved up to an A. Best of luck all.
Rating: 4 / 5Difficulty: 5 / 5Workload: 25 hours / week
CprK70rjk/yNl8zylh2baQ==2025-08-14T19:12:04Zsummer 2025
Artificial Intelligence Techniques for RoboticsAI4R (now Robotic AI Techniques; RAIT) was a great survey course discussing some major approaches to robotic localization (where are you?) and control (what should you do?). There's enough math that a light stats and/or linear algebra refresher could be useful, as it will help you develop a better intuition for the concepts.
The midterm and exam are of middling difficulty, Honorlock proctored, and quite fair overall. The meat of the course is in the project and problem set work, where the problem sets will introduce practical code-based approaches to the concepts presented in lecture, and the projects provide a more substantial exposure and challenge involving the key concepts. The code framework is robust, with test cases and visualizers available to inspect your code's behavior. The overall course load, even for summer, is quite fair, though if you fall behind you're going to have a bad time. I personally advise trying to stay 1-1.5 weeks ahead, i.e. finish things a bit over a week before they're due, to ensure you have time to spend on the later, more difficult projects in the course.
It's a bit of a lighter course, but very interesting and fun. The TAs are extremely organized and helpful, and I would strongly recommend this course if you're considering it.
Rating: 5 / 5Difficulty: 2 / 5Workload: 10 hours / week
FNE7KG9UHITnWcBoMYKKlQ==2025-08-14T11:33:00Zsummer 2025
Machine LearningThe syllabus of this course has recently changed, adding three unit quizzes and two post-grade remediation opportunities (summer workload). Overall, this should be a good thing. Specifically, the quizzes make the final grade more manageable—especially with multiple attempts available, you can consistently secure around 90% on them. Although I didn't use the assignment response remediation, it should be helpful. My average score across the three assignments was also around 90%, so even without thoroughly preparing for the final exam, I still received an A. The final exam was quite tough and overwhelming—it seemed that only those who truly understood all the material could perform well on it. Since I have a background and some experience in machine learning, most of the course (except for the game theory part) was relatively easy for me. Taking everything into account, I would still rate the difficulty as 4 out of 5. Also, the workload I input is for average 3 weeks, that means, I spend about 2 weeks doing nothing, and 30 hours one week before due.
Rating: 4 / 5Difficulty: 4 / 5Workload: 10 hours / week
6wfsiy4f/touldY/xmD8Bw==2025-08-13T18:32:27Zsummer 2025
Machine Learning for TradingBased on the ratings, I thought this class would be super easy, but it wasn't as easy as I thought it would be. Definitely more time consuming than expected. Still a fairly decent and well organized class though.
Rating: 3 / 5Difficulty: 3 / 5Workload: 12 hours / week
4+N+5HpDIIcPjegkhLzJsg==2025-08-13T12:46:42Zsummer 2025
Introduction to Information SecurityIIS was my second course in the program (after ML4T), and I took it as an exploration of computing systems. I had heard it was a good first/early course in the program.
In one way, I strongly disagree that it's a good first course in the program. Your grade is entirely based on your project grades, which are a collection of capture the flag challenges that require you to learn the basics of various types of exploitation at various levels of modern computing systems. This means that your grade is not at all based on whether you do any reading or watch any lectures or can pass any exams. So in the sense that a first course in the program is intended to acclimate you to the kinds of work you are going to need to do as an online student throughout the graduate program, this one absolutely does not do that.
But I think the reason that the course is sometimes recommended as a first course in the program is that it is a wide survey of topics that you can dive much deeper on, and I think that is actually a great reason to take it as a first course or an early course in the program. One week you're diving into javascript XSS attacks, and the next you're looking at buffer overflow exploits in C. You have to learn a lot on the fly, the challenges are really clever and fun, and you get exposure to lots of technologies and ideas of modern computing through the projects.
I don't recommend this as a summer class if you've got plans for your summer. The turns are very tight for the projects (1 week each), and in particular, the binary exploitation project gave a lot of people headaches that would be worth at least two weeks in a spring/fall semester. I spent ~30 hours on it and went in with some C experience. But most of the projects are doable in more like 10-15 hours.
Ed discussions for this course can bring out the best and worst in classmates and TAs. Some of the challenges are hard to hint without giving too much away, so sometimes folks are giving really useful and thoughtful insights in their responses about how to think about a flag. But TAs have to redact a lot, which makes some folks who are lost fairly snippy and unprofessional. And collaboration outside of Ed/Slack is strictly prohibited, which can be jarring for folks used to forming study groups or Discord servers to figure things out.
Rating: 4 / 5Difficulty: 3 / 5Workload: 10 hours / week
Hlbv1xErB9n1pHPQKEPY4Q==2025-08-12T20:01:48Zsummer 2025
Computer NetworksCS6250 - CN - Is not a serious master's level course. The 4 coding assignments are trivial, and the quizzes' and exams' MC questions are taken nearly word-for-word from the lectures. Quizzes are open-book while exams have you install a spyware on your pc to ensure you are not opening other tabs (recommend just booting from a live USB).
Since they are all MC, obviously memorization of the theory is all the professor is teaching you. However, the level of rigour here is more like that of a high school class; it is all only a high level overview of the topic. "Lectures" are all just short webpages you read (no textbook) with a few of them having optional Kaltura narrations by the professor. There are "required" and "optional" research papers but you won't be tested on either - so why bother reading them? I honestly believe I could've learned all the content in a 2 hour YouTube video.
Rating: 2 / 5Difficulty: 2 / 5Workload: 5 hours / week
3umnwGrvcYw4jxaYF/rTnw==2025-08-12T18:13:15Zsummer 2025
Network SecurityOverall solid course. Quizzes are open book and projects are straightforward. Only issue is sometimes the VMs are buggy or have device specific configuration issues that can be challenging to debug.
Rating: 4 / 5Difficulty: 3 / 5Workload: 10 hours / week
UPMquKZjOzk3B7vxhaaTkA==2025-08-12T13:03:33Zsummer 2025
Introduction to Information SecurityI should probably preface by saying I had to end up dropping the course because it was getting very difficult to manage coursework and work along with married life. I was really excited to take this course since before applying to OMSCS in Summer 2024; given the seemingly lighter reviews for the course I thought it would be a good summer course. Turns out right as I was applying for the program Dr. Lee decided to add a 9th project for the course and made them all for credit compared to 6 projects for credit in previous summers and 8 projects for full semesters.
For the 9 projects: Each project was assigned out on Thursday night and would be due on the Sunday night the week after. So 9 days for each project with some overlap on new projects being released with the current project still not due yet. I already had a slow start to the course with the first project since capture the flag style projects were very new for me and then taking longer to finish on that projects and then subsequent projects created a vicious cycle for the rest of the course. After getting a 30% on the binary exploitation project and spending probably 50 hours on it I realized I was f**ked. Ironically that project had the most helpful and gracious TA of the course. Speaking of TAs...
TAs: Look I really don't know how TAs in OMSCS are going through and I have largely given TAs the benefit of the doubt and I appreciate all of their help for what I'm assuming is a crap pay but holy hell can the ones in IIS stop with the massive God complexes? They seemed far more concerned with catching cheaters than actually helping students and when the topic of virtual machines was brought up their response was that they won't try at all to accommodate for Mac users.
Overall I still think this is a cool course that's different than others. I know there were plenty of others in similar situations but it was hard to balance schoolwork and work. It seems like a lot of students got A's in the course so most important lesson I learned is that summer courses just aren't for me. I'll retake the class in a future full semester. I also recommend this course later in your omscs career. I really had no life until I dropped the course. Also it's pretty demoralizing when you're hard core struggling and everyone in Ed is posting about how great the class is going for them, but what can you do.
Rating: 3 / 5Difficulty: 5 / 5Workload: 20 hours / week
78Rx++Fcja9Dy/E2nOh1ng==2025-08-12T02:59:24Zsummer 2025
Machine Learning for TradingTLDR: Grade: A Hours: 15-20 per week in summer term Tips: Put in the effort and do well on the projects, as the exams can be tricky no matter how much you study
If you are at all comfortable with undergraduate-level finance, statistics, probability, and/or ML, I would avoid this class. The topics covered are introductory and I can honestly say I didn't learn much. However, I am biased as I took many finance, statistics, probability, and CS classes in undergrad and have also worked with these topics professionally for years.
At the risk of sounding whiny, there are also way too many source materials to keep track of. On any given week, you are expected to read Chapter 2 from Book A, Chapter 8 from Book B, and watch Videos 2 through 8, etc. The constant context switching is a killer. Oh, and the video and audio are sometimes potato-quality.
I got high 90s-100s on all the projects and 80s-90s on the exams. Just review your code and reports and ensure that you've hit all the project specs and you'll be fine.
Overall, I could see this class being a good first or second class for someone who doesn't have much prior experience with finance or CS/ML. But for me it just felt like busy work.
Rating: 2 / 5Difficulty: 2 / 5Workload: 15 hours / week
r85SdD7EI19H4+C884j5Og==2025-08-12T00:08:33Zsummer 2025
Introduction to Information SecurityPretty chill class overall. Material isn't the most interesting and the CTF project structure takes some getting used to. Most of the projects were very manageable with the weekly deadlines. Machine Learning and Binary Exploitation took much more time than the other projects. Biggest thing I learned is I much prefer projects that build on concepts over time rather than a bunch of cursory introductions to topics.
Felt like I didn't really learn all that much by the end of the class. If I had to do it over again I'd probably skip this course, there are many other courses that have more interesting/valuable content.
Rating: 2 / 5Difficulty: 3 / 5Workload: 10 hours / week
QJfnPoFqMua2oeZuSZNI1g==2025-08-11T21:49:07Zspring 2025
Artificial IntelligenceGreat course. If you're looking to get enough pre-req info for all other AI/ML courses at GT, this is where to start. It goes moderately in-depth on everything AI/ML but not too in-depth. The projects are genuinely interested, challenging, and enjoyable. Lots of flexibility with the exams and course timeline. Strongly recommend taking this as the first course in the program.
Rating: 5 / 5Difficulty: 4 / 5Workload: 12 hours / week
QJfnPoFqMua2oeZuSZNI1g==2025-08-11T21:44:04Zsummer 2025
Machine LearningFull time SWE with top 5 CS undergrad with lots of experience taking graduate-level courses.
This course was brutal, at least during the summer semester. However, I learned an incredibly large amount of content during the course. The reports, although time consuming, are incredibly informative at helping you understand the content and also improves your paper-writing skills. The quizzes are fair and allow multiple attempts. The paper resubmission is also fair if you can squeeze in the time to do it. The lectures are good, but not perfect. Some sections are hard to follow no matter how much textbook you read.
Advice for future students: 1.) Be cautious of taking this in the summer. Prepare yourself for a stressful summer. 2.) Read teapowered ML Notes. 3.) For the reports make sure to have an explanation for every single result you achieved. Base your explanations on course content and ML theory. Anything that is "suggested" or "recommended" in the report handout is secretly a "REQUIRED OR YOU WILL LOSE POINTS". Don't. Skip. Anything.
TLDR: Extremely stressful in Summer Semester. Super informative though.
Rating: 5 / 5Difficulty: 5 / 5Workload: 20 hours / week
GH13bbOvqcOgp7Ysaqty0A==2025-08-11T19:08:21Zsummer 2025
Artificial IntelligenceGenerally, this is a challenging but doable course. I ended up with a 98.5. Workload is NOT consistent throughout the semester. PLEASE NOTE, for the summer term, you will not get less material covered nor fewer assignments compared to the regular semester.
Each week, you will have some chapters to read, lecture videos to watch, one or two ongoing assignment, and a challenging quiz (for extra credit). I spent more than 30 hr/wk during week 2 - 4 and much less in the second half.
The material for the first half of the semester is way much better than in the second half, with particularly poor-quality material in the deep learning and planning under uncertainty sections. Read the book can really helps
The TAs are fine, most TAs hold office hours and respond to questions on Ed threads. However, there were frequent corrections and clarifications needed for both exams.
Course components:
Rating: 4 / 5Difficulty: 3 / 5Workload: 15 hours / week
vt/awCkTL2KApu8V21euXw==2025-08-11T12:37:07Zsummer 2025
Machine Learning for TradingThis course serves as than introduction to both finance/trading and machine learning. I took this as my second course in the program (GIOS in Spring '25). Overall I thought it an interesting course, which was a bit more time consuming given the condensed summer schedule. The finance side of this course I thought was relatively light, so if you already track financial markets in the news I would way you probably will be pretty easily able to pick up what's covered there (or you may already know details and it may just be review). On the ML side the concepts of supervised learning, unsupervised learning, and reinforcement learning are introduced. In my opinion this is where the value of the course lies, in providing some context to broad categories of learners and how they function. You go on to apply these ML concepts in a series of 8 projects. If you take this course I would recommend working ahead on projects 4 and 5 so you can spend extra time on project 6, 7, & 8 which are worth more of the overall grade.
The things I didn't love about this course were:
Rating: 3 / 5Difficulty: 3 / 5Workload: 15 hours / week
nTHM/jzaNwA0CjQ4/XQvOw==2025-08-11T02:57:11Zsummer 2025
Special Topics: Introduction to Computer LawThe lecture material was quite interesting, but to be honest, I found the project work quite displeasurable. I doubt most students will feel this way - to a normal student, maybe the non-coding assignments are a nice break from other classes. But, I think it's worth putting the word out for others like myself: if you find programming assignments fairly easy in general but struggle to stay motivated when the assignment isn't technical, you may have a tougher experience than others have indicated.
It's not like I would even disagree with the sentiment that this is an easy course. When there's no project to be working on, the class is <4 hrs/wk of commitment, no sweat. The problem is, the projects are fairly long reports that can get quite involved if you're trying to get a good grade. One of mine was >20 pages, and I felt quite miserable working on it by the end. We were not given any clear indication of what level of effort would be considered acceptable for that assignment, so part of that was me erring on the side of caution.
Granted, also, that there were only two such papers in the course, so the average workload was still fairly low. But those papers easily took 16-24 hours each, and it was a complete slog for me. I did not have any fun at all working on those papers, although I really enjoyed watching the lectures. So, if you're on the fence about taking this course because you're used to programming assignments and this is "different," consider my experience a data point in favor of taking something else - in my case, this was me trying something a little different, and it was not nearly as fun for me as I had hoped. But your mileage may vary, of course.
Rating: 3 / 5Difficulty: 2 / 5Workload: 10 hours / week
LJKfaWW6btBGa/b7qEB9UQ==2025-08-11T01:50:53Zsummer 2025
Digital MarketingOverall it is an easy A course for sure and a good fit for summer semester:
It is not a CS or CSE course so you should not expect much coding experience around. However, it is a good summer course for anyone wanna take a mental break.
Rating: 3 / 5Difficulty: 1 / 5Workload: 4 hours / week
KFrNxTRmsMecLDSsPl0/0Q==2025-08-10T22:35:14Zsummer 2025
Human-Computer InteractionOverview This was my first completed class after dropping my first semester last year, and it’s a perfect beginner course for learning how OMSCS works or easing back into education.
Course Structure The summer session is very front-loaded with heavy readings, videos, and assignments early on. Prof. Joyner’s videos are excellent, though the readings can feel scattered and cover many topics without much depth.
Assignments Homework is straightforward—follow the rubric, stick to JDF format, and use Overleaf (LaTeX). Start the individual project early, as the team project is essentially the same.
Team Project Tip Pick your team members early if possible. My proactive group started early and finished most of the project well ahead of time, which made the process much smoother.
Exams and Quizzes Tests are open-note—merge PDFs for quick searching. Quizzes are heavily weighted, so avoid doing poorly on more than two areas to protect your grade.
Final Thoughts Overall, it’s an approachable class if you stay disciplined through the intense start.
Used ChatGPT to enhance my message for clarity and conciseness.
Rating: 4 / 5Difficulty: 2 / 5Workload: 10 hours / week
78Rx++Fcja9Dy/E2nOh1ng==2025-08-10T20:19:48Zspring 2025
Database Systems Concepts and DesignThis was my first OMSCS class and I found previous reviews about it to be pretty accurate. I spent 15-20 hours per week on this class and got an A. However, take this with a grain of salt as I work as a full stack SWE and was already comfortable with databases, backend, and frontend before starting the class.
To echo other reviews, I strongly recommend learning backend and frontend development before taking this class, and/or finding teammates who have this skillset. This will save you a lot of time and headache for the group project, particularly Phase 3. Luckily for my group, I'm comfortable working across the stack and was able to unblock the team on database, backend, and frontend issues, so our development went smoothly and we did very well on the project.
This is a class about databases, so I'm still confused why students are expected to know frontend development and build a fully functional full stack web app. There is zero lecture or reading material about API or frontend development, so it doesn't make sense why so much of the project grade hinges on having a functional app and UI.
Regarding the book and lectures, I actually did find much of the material to be interesting and useful. Of course, you won't use relational algebra and calculus directly in a SWE job, but it's important to develop analytical thinking and understand the concepts behind database design.
Lastly, the exams. I didn't feel that they were intentionally trying to trick students. It's just that missing or misinterpreting a single word can change the meaning of the entire question/answer. So be sure to carefully read and re-read all questions and answers before submitting. Exams 1-3 were fair. Exam 4 seemed to be intentionally difficult, possibly to bring the class average down? I scored in the 90s on exams 1-3 and all project phases, and a 60 or 70-something on exam 4.
TLDR: Grade: A Hours: 15-20 per week Difficulty: 3/5 Overall: 4/5 Tips: Learn full stack development and carefully read all exam questions
Rating: 4 / 5Difficulty: 3 / 5Workload: 15 hours / week
wLMhvXbZKQ3IINBkNGi+iw==2025-08-10T16:54:11Zsummer 2025
Machine LearningI'll start with the good:
The topic is interesting. The course is divided into three units: supervised learning, unsupervised learning, and reinforcement learning.
The lectures are OK - they are at a high level and not very rigorous but mostly easy to understand.
I wasn't a big fan of the textbook (Mitchell) - it felt too verbose, spent a lot of time focusing on areas that are not relevant to modern ML, and didn't have a lot of opportunity for practice.
The bad:
The instructor: while nice, he was very disorganized, changed the syllabus multiple times, took a long time to grade the assignments, picked out terrible datasets, and set up this bizarre system where the rubric for the assignments is not given out to the students.
The TAs: they let students respond to most posts on Ed, and there was this one guy (student) who responded to nearly every post with half true information that he copy pasted from Chat GPT. When they do respond to questions they give a vague response and do it several days later. During office hours, the head TA acts like he is God's gift to ML, and will be dismissive if you question his answers. There were errors in their keys to the practice tests.
The assignments: as above, bizarre that the instructor wouldn't release the rubric to the students for the assignments. How are you supposed to know what you are being graded on? Eventually you can kind of guess that they are looking for you to vomit out key words from the lectures and readings, even though the explanations are incorrect / imprecise. The "model" assignments that they released after grading were riddled with errors and omissions but at a superficial level sounded good. The grading is extremely subjective and basically depends on which TA you get assigned. They will comment that you left out XYZ, even if you didn't, and if you want to get points back you have to submit a two page written response as to why you actually did include XYZ. One of the datasets is synthetic and entirely implausible -- it was supposed to be a dataset that represented the incidence of cancer in different populations. As an example, there were equal numbers of men and women with prostate cancer. We were told to basically ignore any understanding of the data and just crunch the numbers and report the results, even if the analysis didn't make sense. What's the purpose of that? It is, again, bizarre, that the students are being told to ignore outside understanding of the problem they are trying to solve to just get the "right" answer for the course. The other dataset is another low quality dataset from some Taiwan Banking journal that appears artificially manipulated with numerous mislabels and errors, and we are supposed to use this dataset to perform our analysis. They are looking for you to try and BS your way into a cogent analysis of a garbage dataset. The un-supservised learning assignment was a particular waste of time because both of these datasets had trivial, noisy clustering, but the instructors were looking for students to try and interpret the noise to stitch together a plausible-sounding analysis. You can try and get a better idea of the assignment if you scour through 100+ ed posts and listen to the office hours for hidden clues as to how to pass the assignment. Most of the time you spend on the assignment will be guessing requirements, guessing what type of analysis they are looking for, interpreting noise, formatting figures, and padding your word count so that the report seems more legit.
Quizzes / Exams: they were riddled with errors and vague questions. they very loosely corresponded to lectures and some of the quiz questions were on topics listed as "optional" on the syllabus.
The class had to be heavily weighted because the instructor can't figure out how to make a course that is challenging but fair, so instead he throws people into a stressful stochastic simulation of a class that results in a large standard deviation for course performance and then adds a generous curve so it doesn't look like everyone is failing the course.
You may need to take this course for your specialization, but otherwise I would suggest self study. They need a new instructor to revamp the course and make it more worthy of OMSCS.
Rating: 1 / 5Difficulty: 3 / 5Workload: 16 hours / week
WY9ryZpTlXAEbBawHnpegA==2025-08-10T13:51:39Zsummer 2025
Artificial IntelligenceThis review comes from the perspective of someone with a full time job and a wife and child. In essence, school comes third for me, even though it's still extremely important. Additionally, after getting my first degree in Software Engineering, I joined the military for 4 years - so my software engineering skills had 4 years to atrophy. All this to say: I may have less time to devote and fewer skills developed towards this class. The average GA Tech student is likely more well-equipped than I am.
The material itself is really cool. The assignments (weekly or bi-weekly projects, basically) walk you through the things you need to do at a high level. The implementation can be tough, but it's not unreasonable. I was really impressed by most of the projects as far as "I never thought I could do that on my own." I did well enough on the first 5 assignments that I could skip the last one. This gave me breathing room to study for the final.
Besides assignments, most of the rest of your grade will come from the midterm and final. You may notice that the course is curved. Don't do like me and assume that, just because the course is difficult, that the curve will be substantial. In reality, it only shifts the grading buckets by at most a few points. I didn't put nearly enough effort into the midterm and got a correspondingly low grade. Luckily, I was able to do well enough throughout the rest of the course to recover with a B.
Some people complain about the plagiarism warnings and threats of OSI violations. It's not that bad, you just can't use google and ChatGPT. Which is fair. When you use approved libraries, you are authorized to read the documentation, which is more than enough to figure things out (remember, I'm probably less skilled than most of y'all). The inability to look up examples outside of what the book or TAs provide makes things more difficult, but really that's a good thing. Instead of copy/pasting someone's code, you'll write your own and learn it.
The course is tough, but it's not unreasonable. They provide enough resources to figure things out. My biggest advice would be to start things early enough to look through their resources and learn them. It's a huge disadvantage to wait until the weekend before the project is due in order to start the assignments.
Before the midterm, we had about 1 assignment every 2 weeks. After the midterm, assignments overlapped and I ended up doing 1 assignment per week for the rest of the semester. That was rough, as I got myself into the aforementioned and warned situation of working the project the weekend it was due. Despite work and life commitments, though, I was able to figure it out.
The lectures provide high-level intuition, and you'll have to go into the textbook to learn details and specifics. I could not have finished this course without the textbook. Having said that, the lectures will inform you on where to go to look up information in the book - that is, you don't have to read hundreds of pages. You just go to where the information is and read a few paragraphs, maybe a couple pages at most for a single topic.
The course is tough, but it's doable. If you have a lot of personal-life stuff going on, perhaps save this course for another semester.
Oh, for real review Linear Algebra. If the last time you took it was 7 years ago, like me. It'll pay dividends in the later assignments.
Rating: 5 / 5Difficulty: 4 / 5Workload: 24 hours / week
4v+GPNibbZHV4cDoKVlvvg==2025-08-10T02:50:01Zsummer 2025
Computer NetworksLectures are definitely on the dull side as others have mentioned, but not necessarily poorly written. They still taught me a lot, and I felt good about the amount I learned in the course.
Each lesson's lectures took ~3 hours, and projects took anywhere between 5-8 hours. Some weeks there were multiple lectures assigned, but this might have been because I took it in the summer.
Regarding exams: They could be a little better written, but you don't need to go crazy and memorize everything in and out like some people would have you believe. I answered the questions in the study guides provided by instructors, and got 90%'s on both exams. Exams only count for 30% of your overall grade, and getting near 100% on the other 70% (projects, open-book quizzes) is straight-up easy if you put in the time needed. That's not to say you won't struggle on the projects - I was banging my head against the wall for the first project for a couple hours, but then I read some forum posts, thought about it, figured it out, and got a 100%. So outside of the exams, you shouldn't miss many points.
On top of all of that, you can get another 3% back from extra credit assignments. That means you only need to get 17% of your overall grade to come from the exams; 17/30 = you need a 56.6% average score on the exams overall. Anyone should be able to do this, even if you're not great at memorizing.
Point being, reviews that note the exams' focus on memorization make a point that has some truth, but it is nowhere near to the point where it should affect your grade unless you really mess up in other areas of the class.
Rating: 4 / 5Difficulty: 2 / 5Workload: 8 hours / week
7o9Z/9juCh8XotdKJOWDiA==2025-08-09T15:24:54Zsummer 2025
Human-Computer InteractionI took CS 6750 HCI during my last semester, Summer 2025. I really liked the content from day 1 and it was so much of learning. The invidiual project was good and I got 100 out of 100. The team project was also good and got 98 out of 100. Overall, I got grade A in it. I will surely recommend this course to everyone
Rating: 5 / 5Difficulty: 3 / 5Workload: 8 hours / week
sO8OJlQ/P8sVDM5eftGHRA==2025-08-08T20:01:42Zsummer 2025
Introduction to Information SecurityI took this course because I work full-time and don't like to overload myself with something overly difficult during the summer semesters. I chose IIS because I’d heard it was “not terribly difficult,” and since I work in cybersecurity, I figured it wouldn’t be a problem.
Well, maybe that's true during a regular semesters. The summer session, however, turned out to be pretty challenging at times. Each lab varied in difficulty, ranging from trivial (PCAP lab – about 5 hours total) to demanding (Binary Exploitation and Web Security – 12 to 15 hours each). Binary Exploitation in particular seemed to be the most objectively difficult, as many people struggled with it. The course covers a lot of topics, so chances are you’ll hit something that stumps you for a while.
In the summer semester, you complete one lab per week, and the short turnarounds on the labs made the course more challenging than I expected. That said, I still enjoyed it because it gave me the opportunity to practice on my skill sets. For someone who has never taken a security-focused course, it offers a clear look at why secure development and coding practices matter, and how things can go oh so wrong (Log4Shell).
Overall, I’d give the course a 4/5. The machine learning component felt more tacked-on than security-focused. Also, there’s plenty of room to earn an A even if you bomb one assignment, you just have to work for it.
Rating: 4 / 5Difficulty: 3 / 5Workload: 12 hours / week
4rk4PJ3PGaGuqNEnhVBcMw==2025-08-08T17:59:11Zsummer 2025
Introduction to Graduate AlgorithmsThis class is tough, as others have mentioned. I believe it’s designed this way so that earning the degree truly feels like an achievement. Having all exams for grading makes it even more stressful especially since, for many of us, this is the final course before graduation.
Don’t lose hope if you score low on one exam as you can still recover. In my opinion, Exam 2 is the easiest, while Exam 1 is the toughest as getting that optimal solution is most. Think through every possible variation of the homework problems for exam preparation, and review all the materials and announcements posted by the course staff.
This is one of the most organized courses I’ve taken, and the Head TAs are extremely dedicated working days and nights to help us. I even got a reply at 1 a.m (Thanks Jamie). after Exam 2 grades were released. Also, thanks to all TAs who worked hard for getting HW and exam graded out in short summer period.
Rating: 5 / 5Difficulty: 5 / 5Workload: 23 hours / week
ZduXZVe+NcBIRDua2dy92A==2025-08-08T14:15:23Zsummer 2025
Computer NetworksTook this course over the Summer to catch my breath after AI (Fall 2024) and DL (Spring 2025) and before ML (Fall 2025) and IGA (planned for Spring 2026). For my purpose, the course almost worked.
The good:
The bad:
The final note: some reviewers compare this class to KBAI. I strongly disagree. There is no class (or at least I have not taken one) worse than KBAI. In CN, at least I learned something and it was not nearly as stressful and required nearly as much stupid work as KBAI. I really cannot understand why KBAI class even exists, it has no reason to.
Rating: 3 / 5Difficulty: 3 / 5Workload: 15 hours / week
AqdW0TmiDdSgsdG+xFWuYA==2025-08-08T04:54:15Zsummer 2025
SimulationI must say, this class took me by surprise. It was my 7th in the OMSA program and the first where I earned a B (all A’s until now).
Like other students, I found Dr. Goldman’s style and humor made the lessons genuinely enjoyable. A big plus.
That said, there’s room for improvement. First, I wish the Arena lessons were updated to reflect the current interface. Using recordings from years ago, when Arena looked very different, was both confusing and frustrating. Honestly, it might be worth replacing Arena entirely with a more relevant workplace tool (e.g., Python). The Arena material felt caught in between: too advanced for a simple intro/visualization, yet too basic to make us proficient. Even now, I couldn’t build more than the simplest models. I also disliked that the exams included questions requiring memorization of Arena-specific syntax, concepts or naming. In today’s world, this information is seconds away with a quick search, so testing recall over application felt outdated.
The exams were also more challenging than expected. We could bring a limited-size cheat sheet, but many questions relied on very specific formulas: if they weren’t on your sheet, you were out of luck. Again, it felt more like a test of memorization (or cramming as many formulas as possible in your cheat sheet) than understanding, which I find less relevant in a real-world context.
I certainly refreshed and strengthened my math, statistics, probability, algebra, and calculus skills (and would even say that I look forward to doing more in these fuilds!). However, as someone noted on Piazza near the end, I felt there was a lack of a holistic, “big picture” view tying it all together. The sheer volume of content (especially in the shorter summer term) may have been a factor, but I would have liked more emphasis on connecting concepts across topics. This was the first OMSA class where complex demonstrations weren’t just shown in the videos for you to follow along. Instead, you actually had to work them out yourself -pen and paper in hand- calculating antiderivatives to find probabilities under the curve.
Overall, Dr. Goldman is an exceptional teacher with great presence and delivery. But the course content could benefit from updates, a more modernized toolset, and stronger integration of concepts into a coherent whole.
Rating: 3 / 5Difficulty: 4 / 5Workload: 15 hours / week
C3Idv8ylYpFDQqlwsq904A==2025-08-07T21:47:44Zspring 2025
Graduate Introduction to Operating SystemsThe course lectures focus on OS design, but the programming assignments are more typical of a network + systems programming course using APIs rather than implementing an actual OS/Kernel. As other reviewers suggest, Beej's Network Programming guide, TLPI, and OSTEP are good resources.
Each assignment took me around ~60 hours to complete, even with prior systems courses and C/C++ exposure (no professional systems/SWE experience though). The write-ups seem like a good idea initially but end up being an annoying after thought when most of the design choices are made for you in the assignment instructions.
IMO going over the lecture videos and making sure I understood the concepts well was enough to do well in the midterm and final. The midterm and final questions mostly seemed fair. However, the instructor sort of flubbed sharing exam answers after grading. Not sure if this was a dragged-out accident/oversight or intentional (if she considers the exam questions to be for evaluation purposes only and not for students to learn their mistakes from, that's fine with me, just say that)
Worth noting, the course slack is the best I've seen in OMSCS. The TAs range from (a) being helpful to (b) going above and beyond their call of duty to (c) sometimes trolling you. I didn't really mind (c).
Rating: 4 / 5Difficulty: 4 / 5Workload: 25 hours / week
C3Idv8ylYpFDQqlwsq904A==2025-08-07T21:12:04Zfall 2024
Introduction to Theory and Practice of Bayesian StatisticsPros:
Cons: 4) Should fit a bit more material within the topic schedule/assignments to make the course slightly more challenging 5) Should add proctored closed-note exams or quizzes to hold you accountable and force you to study more.
Mixed / advice: 5) TA's notes website -- there's a few questionable code snippets or pieces of material. It's also clear the website was a work in progress. However, I recognize the TAs went way above and beyond their duty to create and update this website which ends up being way more valuable for most students than the lecture videos. For that, Aaron deserves a promotion. 6) I skipped the lecture videos so can't comment on them. I followed another reviewer's suggestion and read Student's Guide to Bayesian Statistics by Ben Lambert -- its fairly approachable, gives good intuition, lines up somewhat with the course schedule, but math-light. Therefore, I definitely recommend reading through Aaron's notes, relevant parts of other textbooks, OH notes as necessary for the assignments and general understanding.
Rating: 4 / 5Difficulty: 2 / 5Workload: 20 hours / week
6v6NWG6Kl/hPv2eJJuS8gA==2025-08-07T19:45:08Zsummer 2025
Network Science: Methods and ApplicationsI went into Network Science with a strong interest in learning more about scientific computing, and in that regard it did not disappoint. It feels like field has been heavily influenced by physics (this is the only OMSCS class where I’ve seen any differential equations pop up, for example), and it was really interesting seeing how physicists think about things and model problems mathematically. Some of the material related to percolation and phase-changes, for example, was very cool to learn about. I also really enjoy studying mathematical optimization, and that came up quite a lot in several areas, like the community detection algorithms. Overall, I think if you like applied math, this will be an interesting class for you.
One big change this semester was the addition of some machine learning sections to the last assignment, which I really appreciated. It seems like the teaching staff has been paying attention to the reviews over the years and is trying to respond to feedback and provide a better experience to students.
If you come into NetSci with a decent level of mathematical maturity, I think it’s a very gentle class. There’s a good amount of work, but the pace was manageable, and it felt like the professor and TAs really wanted us to succeed. The grading policies felt extremely generous and it wasn’t hard to get good marks on the assignments. The quizzes were challenging, but fair. I did spend more time than I expected to on the class, but I think that may have been because I was kind of burned out from having taken HPC and HPCA together the previous semester.
There were more opportunities to attend office hours every week than any other class I’ve taken so far at OMSCS, so no matter where you live in the world, if you need live support from a TA, you can find a time to talk to them.
NetSci really does feel like a broad survey, and since the field of network science is covered from many different angles, none of the assignments feels like a super deep dive into any particular topic, so they can feel a little bit unsatisfying compared with the projects from some other classes in the program. The feeling of accomplishment I got from coding up a working convolutional network from scratch in DL, for example, was just not here. On the other hand, none of the assignments was terrible, and I mostly enjoyed working on them.
Overall NetSci was a good introduction to a field that I knew very little about, and it built a solid foundation for future study.
Rating: 4 / 5Difficulty: 3 / 5Workload: 24 hours / week
G8L8g7pcwFkxJFkUQRdFHA==2025-08-07T00:15:59Zspring 2025
Software Development ProcessGood first class to take in the program. Helped me get the hang of being back in school. Projects were relative easy to complete minus the group projects. I got lucky with my group mates tho as everyone is doing their part but I can see this can be frustrating if there is someone in the team not working their part. Overall enjoyed the class. I did not have background in Java and Android but was able to figure out and finish the projects. However I've been working as frontend engineer for a while now which helped.
Rating: 4 / 5Difficulty: 3 / 5Workload: 12 hours / week
u3nsnLbC8JcfIhZUgs8zlg==2025-08-06T19:32:25Zsummer 2025
Advanced Internet Computing Systems and Applicationsgot 91% in the end but it was quite a bit of work.
I have a non-CS background so I always wanted to take a course that gives me a survey of all modern internet application technologies in an academic flavour. this course gave me exactly that.
I spent 18 hours on average per assignment (either M assignment which is a design doc or P assignment which is paragraph response to questions based on lecture material. 6 hours per week going through the lecture videos and take notes. Questions were fair but the wordings may be confusing an unclear at times which may lead to points being deducted. TAs have very inconsistent marking standards. Sometimes I get a A and other times I get a high B because I get points deducted for being not detailed enough in my explanation. The 8-page limit made it very difficult to cover all question prompts efficiently in detail.
Exams (2 tests) were open-book and we were given 8 hours to complete the exams. The exams are not cumulative. Questions were fair but the wordings may be confusing an unclear at times.
I wanted to take a less intensive course this Summer and CS6675 fitted the bill because the coding project was optional for Summer 2025.
The course covered many major technologies used in web apps but it did not include content on building LLM-based web apps.
Instructor and TAs were nice. Instructional videos were helpful.
Rating: 5 / 5Difficulty: 4 / 5Workload: 24 hours / week
1OdGN3lwKBmyisbd6zXsFA==2025-08-06T16:08:46Zsummer 2025
Machine Learning for TradingPrevious courses: IIS and Software dev.
Great course I really enjoyed it. you'll learn a lot about trading and some of this knowledge can actually be applied in real life.
tips:
enjoy the course, it is fun and insightful
they bump your grade up if you are less than 1% (I think) away from the next letter grade. (not a tip but a happy surprise)
even if you didn't do well in the exams you can still manage to get an A by doing exceptionally well in projects (aim to have 100% in almost ALL projects) --> this will also help with P8.
exams are tricky. I am a non-native english speaker and while I did all the required readings that were recommended, I still got wayyy less than the average and median grades in both exams because my vocab is NOT rich. --> there's a lot of word play
to get 100% in the projects, do NOT skip any tiny detail. download the instructions for each project and highlight every line you completed so that you're sure you didn't miss any requirement.
P8 is the longest. I wouldn't say hardest because looking back at it, it's not hard it's just time consuming and you'll get tired halfway there. So, here's what worked for me: finish P4 in the same day as P3 (P4 took literally 30 mins), if you did that, you'll be one week ahead for each submission and hence you'll end up with 2 full weeks to complete P8.
Another tip for P8, clean your codes for P5, P6 and P7 so that you don't waste time with them while working on P8.
I enjoyed P8 ALOT and I really recommend going for the Q-learning algorithm. most believe that going for it is the hardest choice but in my opinion it wasn't. I loved it and loved how I got my Q-learner to beat the market.
Rating: 5 / 5Difficulty: 4 / 5Workload: 35 hours / week
AxYFpSchvpxC8OeaySvFQA==2025-08-06T04:05:36Zsummer 2025
High Performance ComputingThis course was very challenging but rewarding. I came into the course with no HPC background and I didn't take Graduate Algorithms or Graduate Operating systems, but I did complete undergraduate algorithms and OS. This course teaches you how to be a better engineer and think differently. You will learn about how to write programs that work with different memory levels, shared memory and distributed memory. This exposes you to different frameworks like OpenMP, MPI, and CUDA. You get to develop on GaTech's PACE Cluster.
If these sound interesting to you, consider these warnings as well:
If you can get past those warnings, there's definitely a bright side. The instructors are very encouraging in getting you to learn. Each lab becomes less time consuming as the class progresses. On one lab, the grade thresholds were loosened after we submitted it. Each exam is curved and the class final grade itself has a curve. 85 was the cutoff for an A and 74.5 for a B this summer semester. If you are coming in with GA experience, you might be able to find this course less intense. I would not recommend taking this course in summer if you truly want to take your time and learn the topics. For me, I just wanted the difficult parts over with as fast as possible and this is an elective for the ML specialization. There were times I dreaded being in the course. My hopes are to apply HPC topics into distributed GPU training for AI models in the future. I would recommend the course but if you want to focus on AI, you might get away with just learning CUDA and PyTorch Distributed Parallel modules on your own time instead of taking this.
Rating: 4 / 5Difficulty: 5 / 5Workload: 20 hours / week
lVaErvC+H+S2COrzPeOalw==2025-08-06T03:33:39Zsummer 2025
Data Analytics and SecurityFinished the course with an A, achieving a 88.81% (A: 86% and above, B: 76% - 85.9%, C: 66% - 75.9%)
Background: Bachelor's degree in Computer Science from a university ranked #377 out of 436 National Universities in U.S. News English is my second language (TOEFL score: 95/120) 1 year of experience as a full-stack developer and 6 months of experience in data analytics
Overall: This is the sixth class I’ve taken in the OMSCS program, and honestly, it’s been the most frustrating one so far. The course content is disappointing. I don’t even know how to describe it. I signed up for “Data Analytics & Security” hoping to learn the basics of data analysis, how to make data more secure, and how to write solid analytical reports. None of that was covered in the course. I took the ML course in OMSCS, and this class title should be changed to "Intro to R, Python, and ML algorithms" to avoid misunderstandings about the class content.
The assignment instructions are vague and hard to follow. You have to read them multiple times to figure out what’s being asked. It feels like the focus is more on guessing what the instructors want than actually learning anything meaningful. They try to frame the assignments as “open-ended,” but the grading doesn’t reflect that at all, so you’re left completely unsure of how you’re doing or what you’re supposed to be getting out of it.
The TAs in this class honestly have no idea what they’re doing. They respond very slowly and often can’t answer students’ questions. You’re better off searching Google or Reddit than relying on them. The TA group seriously needs to be replaced. If you're too busy with your personal life, then don't apply for a TA position and use "I'm busy" as an excuse.
Lecture Activities (28.78 / 30%): This assignment combines a quiz and a discussion, but don’t be misled by the title. It should be called "homework." The quiz part is easy, but the discussion can be tricky. Some prompts require you to provide actual examples, even though that’s not clearly stated in the instructions. That’s frustrating. You can see the impact in the score breakdown; discussions with these hidden expectations tend to average 80% or lower, compared to those with clearer requirements.
Computing Activities (18.2 / 20%): This coding assignment is worse than anything I did in high school AP Computer Science. It asks students to “explore” and modify code without clear direction or explanation of what’s expected. Many of us ended up confused, and when our exploration didn’t match the TAs’ expectations, we lost points. It’s hard to believe this is a master level class. If the assignment is open-ended, grading should be based on what students submit, not on a hidden rubric. This is also the first time I have submitted a coding assignment as a PDF since high school. Back then, we even submitted .cpp files when GitHub wasn’t popular yet, so this feels very ridiculous. You also have to make sure the margins of the PDF layout fit properly on the page, including the parts from the original layout provided by the TA. If not, they will deduct points for that too.
Project Activities (41.83 / 50%): This section is the worst part of the course, and it counts for up to 50% of the grade. They expect you to write an analysis report, but the course content doesn’t teach you how to do that at all. Fortunately, I had a great teammate who explained what makes a good analysis report and what doesn’t. Otherwise, I wouldn’t have had any idea what I was doing wrong. If your group isn’t clear on the expectations, this part can be extremely frustrating.
Rating: 1 / 5Difficulty: 1 / 5Workload: 4 hours / week
lVaErvC+H+S2COrzPeOalw==2025-08-06T03:31:32Zsummer 2025
Software Development ProcessFinished the course with an A, achieving a 90.81%
Background: Bachelor's degree in Computer Science from a university ranked #377 out of 436 National Universities in U.S. News English is my second language (TOEFL score: 95/120) 1 year of experience as a full-stack developer and 6 months of experience in data analytics
Overall: This is the fifth class I’ve taken, and I find it interesting how it teaches both basic concepts and what to focus on as a software engineer. Now that I’m writing test cases, I’m more aware of what needs to be covered and which parts of the code aren’t. Even if you don’t know Java or Android development, this class can motivate you to learn—especially since it's graded. I’d say that after taking this class, I have a good understanding of Java and Android development syntax and how they work together.
Grading and Weight: Participation (3 / 3%): I answered and asked a few questions on Ed Discussion (around 10 in total) and received full points.
Assignment 1 (2 / 2%): Just a survey.
Assignment 2 (5 / 5%): Introduction to Git. As long as you follow the instructions, it’s not difficult.
Assignment 3 (10 / 10%): Introduction to Java. Similar to Assignment 2, just follow the instructions. Since I was new to Java, I had to look up some syntax online. But if you know any programming language, this should be pretty easy.
Assignment 4 (10 / 10%): Introduction to Android Studio. I spent more time on this one because I was completely new to both mobile development and Java. This assignment requires you to pass objects between classes, so I recommend starting early if you have no prior experience like me.
Assignment 5 (2 / 2%): As long as you present a reasonable design, the TA likely won’t grade too harshly. There’s no “correct” solution as long as you can justify your design choices, you should get full credit. Based on Ed Discussion, most point deductions came from syntax issues.
Group Project (17.19 / 18%): This depends heavily on your group. I was lucky to have a responsible team that everyone contributed, discussed implementation ideas, and divided the workload fairly. Even though all four of us were new to Java and Android Studio, we worked well together. I can imagine it would be a bad experience if your teammates don’t participate.
Collaboration (Group Project) (9.9 / 10%): Since our group worked well together, I believe we all deserved full marks. However, many people mentioned they received scores around 9.5 to 10, so I suspect there might be a hidden rubric involved.
Assignment 6 (8.745 / 15%): This is a very important and challenging assignment, as it determines the final grade. Unfortunately, I didn’t perform well on it, and even after reviewing the solution, I still don’t fully understand what the assignment is about. I understand this may be important if you're pursuing a role as a testing engineer, but my focus is on full-stack development and data analytics. In my job, the kinds of tests we write aren’t closely related to the material covered in this assignment.
Individual Project (22.975 / 25%): I really liked this project. It felt like solving puzzles or LeetCode problems. To score 100% (or even extra credit), you need to carefully consider edge cases. While not everyone may enjoy it, I did my best and was happy with my result. The answers aren't revealed, but you’ll get some feedback on Gradescope.
Rating: 4 / 5Difficulty: 2 / 5Workload: 10 hours / week
Jy7GP7iZJBU851z4KuQpbw==2025-08-06T02:06:34Zsummer 2025
Computer NetworksOverall, I found this to be one of the least valuable courses in OMSCS. The course materials are essentially straight from the textbook, and students are expected to read everything on their own. The lecture videos consist of the professor reading the material aloud with little enthusiasm or added insight. I ended up learning more from YouTube than from the official course content.
Rating: 2 / 5Difficulty: 4 / 5Workload: 15 hours / week
rAngTVy2/3GaDPtZHiAVQw==2025-08-05T17:26:08Zsummer 2025
Machine Learning for TradingPrevious courses: RL Final Grade: A (93.2%)
Prerequisites: Python, Pandas + Numpy. They have Pandas and Numpy review in the first week, but it helps coming in already being familiar.
Lectures/Content: The lectures were probably the strongest part of this course. Similar to other OMSCS courses, they are fairly old, but Professor Balch is honestly an excellent teacher. If anything, I wish there were more lectures to cover other parts of the course content.
The readings are decent. I only did the required readings. The Intro to Statistical Learning is a solid textbook for the ML portion. I also enjoyed Professor Balch’s What Hedge Funds Really Do.
Projects: Eight projects with varying levels or work. I’d rank them from least to most work as follows: P4, P2, P5, P1, P7, P3, P6, P8. The projects generally all build up to the final project, P8. The projects are fine, and I did learn from them. However, I think the most common complaint is the very rigid structure. For the most part, you are producing the same exact analysis and report as other students, besides for P6 and P8. Because of this, there are very detailed and long guidelines to follow for each. You are constantly checking to make sure you implement everything exactly as described. Even minor deviations from the project requirements can cause you to lose points. For example, I lost points, although a small amount, because I didn’t return my dataframe in the exact format they wanted. Did the format of the dataframe affect the results or any of the rest of the analysis? No. It simply wasn’t the exact format they wanted. The requirement was tucked into a single sentence in a very long project description and the nuance was captured by only a couple of words, which I know other students missed. Ironically, my dataframe was more memory efficient but I digress. The point is READ THE REQUIREMENTS MANY TIMES OVER! It is very annoying, but you will lose stupid points otherwise.
Exams: Two closed book exams. These are multiple choice, multiple answer, but each individual answer counts as points, so it essentially becomes a massive true false exam. The questions can be very wordy at times. I am confident you could easily cut down on the wordiness, and it would have zero effect on the exams. Unclear if they do this just to make it more difficult, but it does line up with the general theme of being overly verbose in the projects. Do the required readings and lectures. Study a little and you will be fine. I got a 99/110 (90%) and 92/110 (84%). The median and mean were 85/110 (77%) and 83/110 (75%). Exams seemed to be where most students lost the majority of their points, so it is worth putting some time into them.
TL;DR: Solid course overall. Didn’t hate it or love it. I think mostly the intensity of the project requirements only soured my view a little.
Advice: Do the required readings and lectures. Start the projects early unless it is a super easy project (e.g., P4)
Rating: 3 / 5Difficulty: 3 / 5Workload: 16 hours / week
BzgeW/scm38rRv2p5kpjlw==2025-08-05T13:55:14Zsummer 2025
Introduction to Analytics ModelingAs an OMSCS student this was a great course to take and is basically a statistical learning / ML 101 course. It doesn't dive too deep into the math behind any models but will build a good foundation of how to approach modeling and what models are appropriate for what scenarios.
The lectures were a 10/10 and extremely informative, the homework could take a while but were really good for solidifying information. Although homework was only 15% of the grade and is peer reviewed (a 90 is for a complete homework, 100 is only if you go above and beyond). I found that even if I went above and beyond sometimes I'd get graded a 90 and other times a 100 so it's a toss up.
The course is exam heavy (75% of your grade across 3 exams) and 95% of the information is from the lectures. The median grades were low to mid 80s and I found t exams to be fairly challenging. Some of the wording for the questions threw me off but you get used to it after the first exam.
My advice would be to just do the homework to completion and put the rest of your effort into learning the material for the exams. There also seems to be a small unofficial curve (I got an A with an 89% actual grade). This course is an easy B if you do the work and have decent exam grades. To get an A you really need to crush the exams or get an 87+ and hope for a curve.
Overall it was a great course to take, just be prepared to get a B if you struggle on the exams.
Rating: 4 / 5Difficulty: 3 / 5Workload: 10 hours / week
ec5OlUAcFatt6Pa6LV7DXQ==2025-08-05T13:37:43Zsummer 2025
Machine Learning for TradingThis course is about stock trading and a intro to machine learning. It doesn't teach deep theory but more about apply Decision Tree, Random Tree, Q-Learner into stock trading. You will learn how to use the stock indicators to make trading decision.
The projects are the highlight of the course. You will learn each component step by step and use them in the final project. The requirements are very clear to follow. And TAs are always very helpful and responsive.
The videos are easy to follow. But you should also read the paper/materials provided to get good grades on exam.
They are just fantastic. They try to answer every posts on Ed forum and provide quality answers. Best TAs I've ever seen in OMSCS.
Rating: 5 / 5Difficulty: 4 / 5Workload: 15 hours / week
+nzeCBUmALuR4svysa4e/w==2025-08-05T13:15:58Zsummer 2025
Computer NetworksMe: I had an undergrad in CS. I started in Fall '24. This was 3rd semester. I've taken:
I'm a full-time student.
CN Grade: A (90%)
It's a good course. I don't think any prior knowledge is required. Just know python and be intermediate at least. The course is very easy. Assignments are easy. Exams are MCQs, closed book and easy. There are weekly open book quizzes which are also very easy. I was very lazy over this semester. I did everything, literally, one night before. Every quiz and assignment. The lectures are not too necessary to do the assignment, but you should still watch them before starting the assignment. Alot of the time I didn't, and watched + did the assignment over night. I don't recommend it because sometimes I'd have to submit late with minor penalties (-10, - 5, depends how late you submit). I also studied for the exams one night before and did relatively well and above average.
Obviously, I don't recommend any of this. But this just goes to show that it's pretty easy. There's also an extra credit assignment that I didn't do which would've made things even easier.
All in all, decent class. I just took it for an easy A. It's not very relevant to me and as a result I found it pretty boring.
I've decided I don't really care about my GPA as long as it's not too bad. I'd rather take hard classes that'll teach me valuable things. If you're looking for an easy A, I'd recommend you take up my new thinking. Because from what everyone says, no one cares about GPA. Unless maybe you're going to pursue even higher education.
Courses I've taken in order of difficulty: AIES (1/5) < CN (1.5/5) < HCI (2/5) < RAIT (2.5/5) < KBAI (3/5).
Rating: 3 / 5Difficulty: 1 / 5Workload: 3 hours / week
0VarVHQOP+Gjd1Cz61euWg==2025-08-05T02:57:27Zsummer 2025
Special Topics: Introduction to Computer LawTLDR: The lectures and assignments were excellent, but the course was super light. If you are looking for an interesting course with a low workload, then this is the perfect course.
Finished the course with an A. Spent around 7 hours per week on average, but the workload varied from week to week.
Course Structure: Each week, we had to watch lectures, respond to a discussion post, and take a quiz. There were two projects, an acquisition project and a code analysis project.
Lectures: The lectures were fantastic. The majority of the course was focused on intellectual property, which could be a dry topic. The instructors did an excellent job at making the content interesting and exciting. I watched the lectures at 2x speed, so it only took about 30 to 60 minutes per week to watch the lectures and take notes.
Quizzes: The quizzes were open book and open note and were fairly straightforward. The quizzes were timed, but they only took about 15 to 30 minutes to complete.
Discussion post: I really liked the discussion posts. While they only took about 30-60 minutes per week to complete, they required us to really think about the material.
Projects: I enjoyed both projects. The acquisition project took about 30-40 hours to complete. The code analysis project took about 20-25 hours to complete. We were given about 4 weeks to complete the acquisition project and 2 weeks to complete the code analysis project. I found the acquisition project to be more interesting, but I felt like the code analysis project required more knowledge of the course material.
Overall: For about half the course, I was only spending around 2-3 hours per week on the course. When I was working on the projects, I spent about 10-15 hours per week, but we only worked on projects for half of the course. If I took this course in the fall or spring semester, I imagine that the workload would be even lower. Even though the workload is light, I really enjoyed this course and learned a lot about computer law.
Rating: 5 / 5Difficulty: 2 / 5Workload: 7 hours / week
WkYpzcmCjQEVKEZYCrKprA==2025-08-05T01:47:51Zsummer 2025
Machine LearningThis was a tough and challenging, but ultimately rewarding class. The course is divided into three major sections: Supervised Learning (SL), Unsupervised Learning (UL), and Reinforcement Learning (RL). Each section includes a corresponding quiz, which contributed 5% each to the final grade (15% total). These quizzes were proctored and allowed a one-page cheat sheet, with three attempts per quiz. Interestingly, the actual quizzes were somewhat easier than the practice quizzes, but still not a walk in the park. Preparing for them was a great way to reinforce the material beyond just watching lectures.
There were three major assignments/reports, each worth 15% of the final grade. This semester, the course introduced a review response mechanism for the first two assignments, allowing students to recover up to 50% of lost points based on reviewer feedback (e.g., a 70/100 could be raised to 85/100). Unfortunately, due to time constraints, there was no review response for the third assignment. One major pain point was the use of a hidden rubric, which made expectations feel unclear at times. Additionally, the strict 8-page limit (including references) made the assignments particularly demanding.
Despite the challenges, I learned a lot. The course is intense, but it pushes you to apply concepts rigorously.
My personal strategy:
Week 1: Watch all lectures
Week 2: Begin the assignment
End of Week 2: Take the quiz
Final Week: Polish and finalize everything
Good luck!
Rating: 4 / 5Difficulty: 4 / 5Workload: 20 hours / week
Z0L/0Apuavwi2S8W4mEWFA==2025-08-05T01:22:09Zsummer 2025
Machine LearningPersonally, I found this course to be extremely rewarding and I highly recommend taking it especially if you are going for the ML or Interactive Intelligence specializations.
Positives - The course content and lectures are a perfect segway into reinforcement learning and deep learning courses that you may want to take later on. There are accompanying notes from former students that can be found online and lots of good resources besides the provided textbook and lecture videos that can help you grasp the course material. If you are struggling with course material, there are two weekly office hours sessions you can attend.
They don't really care about your code in this course - meaning you're not graded for writing clean, efficient code that can solve some complex problem. I mean, it is for your personal benefit, but code holds no weight on grade in this course. You're also encouraged to use generative AI assistants to help you write your code.
They started something called Reviewer Response in this semester, where for the first two assignments they gave students the opportunity to recoup half of the points they lost on an assignment if the student submitted revisions based on the TA feedback (i.e. you get a 70, you can resubmit with the suggested revisions and get an 85).
The quizzes were pretty easy for the most part. It is proctored and you get only one page of written or typed notes to use, but you get 3 attempts for each quiz.
The professors and staff also really don't try to fail students - roughly 91% of students get at least a B in this course and 96% typically get at least a C.
Downsides - grading in this course was extremely stressful. Basically there are 3 papers on supervised learning, unsupervised learning, and reinforcement learning (there may be more than this in longer semesters), that added up to 45% of the grade. There is a hidden rubric on the assignments, and while they do provide you with a document about what they expect you to write about in each assignment, make sure you do a good job of identifying the "why" behind your reasoning. You're graded on how "interesting" of a paper you can write from an ML practitioner standpoint, so make sure you cover all aspects and explain your findings thoroughly when writing your papers.
The final was tricky. I studied quite a bit for this but the burnout was real.
I somehow ended up passing with an A at an 83%.
Cutoffs for the summer were a 77 for an A, 61 for a B, and a 46 for a C.
Bottomline: If you take this course, make sure you put in the weekly effort, try to think like an ML practitioner when you write your papers, and remember - trust in the curve. If you put in legitimate honest effort (around 20 hours a week) it's really, really difficult to fail this course, and your chances of getting at least a B are very high.
Rating: 5 / 5Difficulty: 4 / 5Workload: 20 hours / week
KuCGtJTMqRNrbKT7s+g7gg==2025-08-04T23:29:18Zsummer 2025
Natural Language ProcessingThis class is substantially harder than is indicated by omscentral, unless you you ML/AI experience. The professor is great, but they replaced the easy paper midterm into a miserable proctor exam this semester to try to combat AI usage. I took the course to have a nice summer break between spring and fall where I am taking 2 classes each semester, and I don't feel like it was a break.
Rating: 3 / 5Difficulty: 4 / 5Workload: 12 hours / week
Q3En1OkO9YoXSysoGoMf2w==2025-08-04T22:16:22Zsummer 2025
Machine LearningThis course consists of 3 reports, 3 unit tests (closed notes), and a closed-book final. One major change in this iteration was the introduction of a Reviewer's Response process. After submitting each report, you get your score along with detailed feedback — and you're given a second chance to revise and resubmit a response. The re-submission doesn’t allow you to completely recover your score, but it can significantly improve it. For example, if you scored 70 originally, you can get up to 85 after the response (50% credit for the remaining 30%).
Personally, I scored a 50 on my first report, but thanks to the Reviewer's Response opportunity, I managed to recover a good portion of that. Students' average across reports and the final exam was around 70% to 80%.
The instructor was kind and responsive, and I had a great experience with both the professor and the TAs. The course has a decent amount of work, but it’s manageable if you stay on top of the assignments. Also, I feel like I learned a lot — especially how to write technical reports more effectively, which was a valuable takeaway for me.
Rating: 5 / 5Difficulty: 4 / 5Workload: 20 hours / week
exenhSmf5lOOcSoZUrQd+Q==2025-08-04T20:16:38Zsummer 2025
Machine LearningUnless you have a lot of free time I would strongly suggest do not take this during the Summer semester. We covered three major topics; Supervised Learning, Unsupervised Learning and Reinforcement Learning. Every 3 weeks we were required to write up code to experiment with given datasets/models and write an academic paper (8 pages) worth 15%. Also note there is no clear rubric for how the report will be graded. There is an opportunity to submit report reviews to claw back some marks lost on the papers but you could be putting yourself at risk of falling behind in the next topic. In addition to the papers, we had a quiz due on the same day as the academic paper which is worth 5%. I liked having the quizzes but would have appreciated an extra week between project delivery particularly so it would be easier to help balance demands from work like business trips. The final was worth 30% and was not difficult since the quizzes prepared me for what to expect and we did not have a report due at the same time.
Content was interesting but lectures did not go in depth and you are required to go through additional textbook and other readings which is imo the most difficult part of the course. Quizzes and final exam definitely touched on topics not covered in the lecture videos. TJ was phenomenal throughout the semester, he would reply to students on Ed Discussions and Discrod. The TA team were also very responsive! Some office hours were spent answering individual questions which I can appreciate but if you already know the content well, it may not be very helpful to you. I scored in the high 80s and ended up with an A but the cutoff was high 70s.
Rating: 4 / 5Difficulty: 4 / 5Workload: 15 hours / week
RI9E60yFzY/ITh3TXK+Irg==2025-08-04T20:14:07Zsummer 2025
Natural Language ProcessingI wish I had taken the course earlier! Prof. Mark Riedl did a great job explaining how different language models and architectures work. The projects were well designed, and the exams were challenging yet enjoyable. Every student could tell how much effort he's put into this course.
Rating: 5 / 5Difficulty: 3 / 5Workload: 11 hours / week
2X4gT7/0utSv0tP6WcTDUw==2025-08-04T18:07:27Zsummer 2025
Knowledge-Based AITL;DR: - OVerall, I liked the course. There is a lot of work involved. TAs are good, there needs to be better connection between lecture material and projects, you will write a lot and the participation portion is not that bad.
I took this over the summer and paired it with AIES thinking a medium and an easy class would go well together. Overall, I was correct. Lots of people have outlined the course contents and what you need to do. For a point of reference, I finished with a 93% for the course. Here are some thoughts on the course:
You will write a lot of reports. I found them to be better than I expected. You are given a rubric to follow. If you follow the rubric, format the report so that sections match the rubric sections and you answer the questions in the rubric in each section where they are asked (even if you occasionally repeat yourself) you will be fine. After the first one or two, you get the hang of what they want. I can't prove it but adding in diagrams and flowcharts seem to help the grade.
There is a lot of (busy)work. Canvas shows 31 graded assignments ranging from a few course surveys to the final exam and final project. You will be busy with multiple things due each week.
There is a lot of "noise" in the course. Ed is filled with lots of extra posts on topical things that are not required but meant to build an online community and discuss ideas. You can decide if they are worth participating in.
The lectures are somewhat dated and Dr.Joyner is working on updates. He released a few of them this semester for us to preview and comment on.
The lectures are very much at the 30,000 ft. level. They cover a topic with examples using Ed Lessons. When you are done, you know how they work but there is no practical "how would I implement this?" or "What does the algorithm for that look like?"
The final project - ARC-AGI was a lot of fun. Unfortunately, many students (including myself) use a more brute force approach to solving the project because there isn't any guidance on how we might connect the dots from the 30,000 ft. level to the 10 ft. level of implementation. On the project implementation I got a 73%. Because the project implementation weight is only 7.5%, it wasn't worth the remaining hours I had left to get to 100%. Time was better spent on the report (which I got a 100% on) which isn't directly linked to how well you did the implementation but is heavily linked to how well you can explain why you chose the method you did, where it worked well, where it struggled, etc. I suggested they cut the project form 96 test cases to 60 for the summer due to the compressed schedule.
Participation - You need 90 points to get full participation credit. If you participate on the forums, ask questions, answer questions, make comments, you can get 40 points. If you join one of the reading groups and make it to all five meetings, you can get 25 points. They have a wide range of books and I found this to be a highlight of the course. That leaves you with 25 points to get. Each peer review that you leave if done in a timely manner is 1.5 points and they take about ten minutes to write. My suggestion is to do reviews on the ARC-AGI reports that you write for each of the five deliverables. You can do extra ones up front and get done early. This is a good way to see how others are implementing their project and get ideas you can use for yours. You can get the last 25 points from doing 17 or 18 peer reviews. I found them to be helpful.
With so many small assignments, one bad assignment is not going to kill your grade. I see this as a positive.
Rating: 4 / 5Difficulty: 3 / 5Workload: 20 hours / week
2X4gT7/0utSv0tP6WcTDUw==2025-08-04T17:35:15Zsummer 2025
AI, Ethics, and SocietyI took this course to pair it with KBAI this summer because I do feel that ethics are important and also a big challenge with the exponential growth of AI.
Honestly, I was very disappointed with the course content. With so many aspects of Ethics and societal impacts of AI, the course was overly/totally focused on DEI and data biases. The course content could have been covered in about three lectures and the remaining time left to cover the many other aspects of ethics in AI.
The course is composed of many small submissions. There were 22 graded submissions ranging from the syllabus quiz to the Final Exam/Project. All of them except surveys and the midterm were writing projects and you had to use Python or Excel to generate the information you needed for the reports.
Lots of reviews comment on the assignments and the work involved so I am not going to duplicate that effort other than to emphasize two things:
If you answer the questions in the rubrics, even if you are repeating yourself at different points, you will do fine. Give them what they are asking for. In the entire course I missed 5 points on the midterm exam and 2 points on one of the written assignments. This strategy works.
The final exam which is a project was the only significant challenge in the course and 90% of the challenge was finding the topic and data I wanted to use as the basis of my final project submission.
In the end though, I was able to accomplish my goal. I had an easy course for the summer to pair with a more involved course.
Rating: 1 / 5Difficulty: 1 / 5Workload: 8 hours / week
QgCO8k4SW9IBApbwXzrv9g==2025-08-04T15:28:22Zsummer 2025
Information and Communications Technology PolicyOverall I think this course is a great course to do in the summer. It comprised of graded readings (a first for me) and lecture videos which are then followed by quizzes. The readings and lecture videos increase in volume each module so expect to spend more time but they also increase the time to complete which is great. We then had a final assignment to do a presentation based on a criteria you're assigned and it's worth a 100pts. Great Course overall.
Rating: 5 / 5Difficulty: 1 / 5Workload: 6 hours / week
lu8ymwn8zN9MapWs1tWnnA==2025-08-04T14:55:09Zsummer 2025
Natural Language ProcessingThis course was really interesting. Like many others, I must agree that the Meta lectures were quite hard to follow and cumbersome to watch, but Dr. Reidl's lectures were some of the best lectures I've seen in this program. The content is pretty interesting overall and the programming assignments were not too difficult. There are recorded office hours that will go through the pseudocode for some of the assignments which makes it easier to follow and complete the assignments.
There were 4 programming assignments, a mini project, a midterm, and a final. There are also weekly quizzes based on the lectures for that week. The midterm and final were both open note/ open book, but I suggest going through the lectures again before the exams and really understand the content. This semester they made a change in the exam format where there are more short answer questions and you will need to write a sentence or two explaining your answer. Knowing the material will definitely help so you don't have to shift through all the notes looking for the right content (there is quite a bit).
Rating: 4 / 5Difficulty: 3 / 5Workload: 10 hours / week
4rk4PJ3PGaGuqNEnhVBcMw==2025-08-04T14:46:51Zsummer 2025
Introduction to Graduate AlgorithmsThis course is not easy, and all the reviews are justifiable. Start working hard from day one there should be no rest days for this course. Make sure you understand Dynamic Programming, Binary Search, and Graph Algorithms in depth if you are planning to take this course. If you're new to these topics, learning them will significantly help you in Exam 1 and Exam 2.
Think through every variation of the homework problems. The exam questions will be similar to the homework. Also, note that you won’t be writing code. Instead, you'll be describing algorithms in detail. This can be tricky, as you might lose points even if you think your answer is correct small details matter.
This is the class where 90% of the grade comes from exams: each exam has two long answer questions worth 20 points each, plus 20 points of multiple-choice questions = 60 points. Grade heavily depends on the long-answer questions. Lastly, in my opinion, don’t drop the course if you think you can get at least C it will help if you need to retake. Don't let negative comments demotivate you. Ofc, it will be tough what else you expect because this is the final class before graduation
Rating: 5 / 5Difficulty: 5 / 5Workload: 35 hours / week
1cKcz1OFPhLMJoBDcfufvA==2025-08-04T08:05:05Zfall 2024
Graduate Introduction to Operating SystemsFirst course in the program, non-CS STEM undergrad, got an A. Other reviews / reddit posts etc. will tell you how to succeed in the course. This is what you should look to get out of it:
Rating: 5 / 5Difficulty: 4 / 5Workload: 20 hours / week
aj84VcWYhasQA+nR6bVnxQ==2025-08-04T04:33:45Zsummer 2025
High-Performance Computer ArchitectureThe bottom line: This course is well-taught and well-run, with some minor gripes that you can read about in other reviews. However, the course doesn't ask very much of you, and you'll want to go above and beyond (e.g. read the textbook) if you want a solid handle on computer architecture.
I am a full-time SWE with 4 years of experience and no CS degree, no architecture experience, minimal C++ experience. This was my first complete OMSCS class. I got a solid A in the course with what I consider to be mediocre effort.
Difficulty
The exams were fair and not too difficult, I got A's on both. There are "problem solving" questions involving calculation and logical thinking applied to the course material, and there are "pure factual recall" questions. I did worse in the latter category, even though the exams were open notes. The provided course notes are insufficient if you're not prepared for the exam.
The projects are not all that hard if you are an experienced programmer in any language. If you're not familiar with C++, you can mostly "code by analogy" based on the existing code. I googled things like "how to use a map in C++" as needed.
Time commitment
If you are not an experienced programmer, expect to spend significantly longer on the projects.
Project 0: 10 hours, mostly configuration (tricky on Apple Silicon) and a weirdly difficult question Project 1: 8 hours, a lot of it is just making sense of the codebase Project 2: 7 hours, getting in the groove Project 3: 6 hours, at this point I had the hang of it
Spent ~30 hours on the lectures, ~15 hours reading various bits from Hennessy and Patterson (I wish I had spent more time with this, but I had preordered the next edition of the textbook and it kept getting delayed!), and just 3 hours studying for exams.
Adding everything together, I spent about 90 hours on this course. I wish I had spent more time engaging with the material so that I understood it more deeply. I'm hoping to go back and read the textbook more thoroughly at some point in the future.
My time commitment was pretty clumpy. There were weeks where I spent the whole weekend and a couple hours a day working to get ahead, and there was a stretch of four weeks where I spent a total of 5 hours on the class. IMO this is a great course to take if you plan on traveling or otherwise taking a week or two off.
Rating: 4 / 5Difficulty: 3 / 5Workload: 8 hours / week
fMl+Z8xULfQAeHXi9hZS1A==2025-08-04T04:06:24Zsummer 2025
Natural Language ProcessingI was misled by previous reviews that claimed this was an easy course. It’s not. I had no prior experience with ML or DL before taking this course, and I struggled throughout. It was only a generous curve that saved me from an F or D.
I've taken GIOS (A), SDP (A), KBAI (A), and CN (A) so far, and NLP had by far the most material and the most difficult exams.
Here’s the entire material you need to review for the exams, keep scrolling, note that you have to memorise and understand all of it: https://monzersaleh.github.io/GeorgiaTech/CS7650_NLP.html
There was a lot of content that was unfamiliar to me, and I often had to look online to better understand the concepts. The lectures are very dense, and the exams are brutal. You'll find your notes mostly useless. You need to memorize a lot of material and understand it deeply just to score around average on the exams. This requires thinking and reviewing lectures multiple times to obtain a comprehensive understanding of the subject. My experience with rest of the courses was very different, and you could answer the exams much easier. The exam questions are mind-bending. Some questions were two paragraphs of explanation, and you had to write an essay to answer. You may be asked to explain an entire pipeline to accomplish a task, and sometimes the questions touch on topics that are still active areas of research. I honestly don’t know how someone without real production experience could answer some of them.
Rating: 4 / 5Difficulty: 4 / 5Workload: 20 hours / week
8l3KuswRrb/OgeowBu/tDA==2025-08-04T03:01:26Zsummer 2025
Introduction to Graduate AlgorithmsSummer 2025, got an A. (92%).
I really enjoyed this course... it challenged me more than I think most other courses in the program (last one!), but I sincerely feel that if you put in the work, you will do really well! The staff is constantly trying to improve the course to make it better. They now have more seats available for people to take earlier in their grad. career.
My advice: Do every homework problem, and do the "recommended homework problems". Then go to Jove's office hours and check your work. Go to every office hour! Before every exam, look at your stack of homework problems and try to notice the similarities, patterns, and differences between them.
I listened to a podcast about this course that said it well: Do every suggested problem and homework problem, go to office hours, and you will not be surprised by anything on the exam!
I spent so much time worrying about this class, but it is honestly very fair if you put in the effort.
WIth that... I'm done with OMSCS!!
Rating: 5 / 5Difficulty: 4 / 5Workload: 25 hours / week
XzosBcdhnDW15JHR+dckJw==2025-08-04T02:54:57Zsummer 2025
Special Topics: High-Dimensional Data AnalyticsI finished with a 100%, but realistically it felt more like I deserved a 60%. This course is extremely challenging—by far the hardest class I've taken in the entire OMSA/OMSCS program. It’s the first time in OMSCS I felt like I was genuinely doing graduate-level math and facing graduate-level demands. Each module required roughly a hundred pages of dense textbook reading (ESL—not ISL) and/or multiple research papers.
Lectures themselves weren't bad, but mostly focused on mathematical derivations of various algorithms. Unlike CDA or ML, where you can rely on lectures recorded at other schools like Cornell, MIT, Stanford, or even YouTube (StatsQuest, Ritvik Math), the algorithms in this course are pretty obscure (things like tensor decomposition, B-splines, SVT) so you're often stuck with just the provided materials and maybe some obscure online resource from a lesser-known university. ChatGPT was probably one of the most useful resources for clarifying these concepts.
Assignments are pretty tough, although grading is extremely lenient (probably as lenient as CDA but without bonus points). You get some skeleton code separately provided, which helps tremendously, and there have been one or two questions where I just couldn't understand the material but was able to just reuse the example code and still get full marks. Without it, homework difficulty would easily hit 10 out of 5. Overall, assignments vary quite a bit in difficulty, but that probably depends on your background as well. For example HW2 is pretty easy if you've taken DL/CV. Exams are significantly harder and more time-consuming than homework assignments, and unfortunately feedback on your work is basically nonexistent.
The instructor is completely MIA. However, one thing that stood out positively was that all TAs were actual PhD students—something rare in OMSCS. Unlike some courses where you can tell students know more than the TAs, here the TAs clearly knew their stuff. Office hours were pretty just quick Q&A sessions, nothing instructional or proactive like in DL. On the upside, TA responses on ted were fantastic. The smartest students in the program seemed drawn to this class, so the forums had unusually high-quality discussions. We had one student, Joey, who basically rewrote and updated large portions of example code, which was extremely helpful.
If you found CDA remotely challenging, avoid this class. For students who thought math in introductory courses (like SIM) was rigorous—definitely skip this one. To realistically prep for this class, Gilbert Strang’s linear algebra lectures on MIT OCW or Steve Brunton’s SVD playlist on YouTube are good starting points.
TLDR The class is genuinely very tough, the instructor is absent, and the grading is absurdly forgiving. Still, this was definitely one of my favorite courses in OMSA/OMSCS because it felt like authentic graduate-level learning. Highly recommended, but only if your math skills are already very solid.
Rating: 5 / 5Difficulty: 5 / 5Workload: 16 hours / week
gfiGuaL+UAyy5nXKbM5cOw==2025-08-04T02:04:54Zsummer 2025
Machine LearningJust for setting context, I am a working professional working day in and out on ML Research. Managed to get an A.
TLDR: Please take this subject in Spring / Winter and for god's sake pair it with an easy sub if you must take 2
What Sucks: The course difficulty doesn't kick in due to the inherent difficulty of the subject, but rather the lecture quality and the mountain of endless requirements. Most of the duration of summer, I didn't get time to breather. PLUS: THE LECTURES ARE NOT GOOD.
Here's what got all of us through the course, so imma just give it a boost here: https://teapowered.dev/assets/ml-notes.pdf
Improvements: The report requirements are more clearer than the past terms and there are additionaly unit quizzes added to help ya with the final quiz.
Rating: 3 / 5Difficulty: 5 / 5Workload: 24 hours / week
8MY5UH9bQUcNzUOoFDzcgQ==2025-08-03T21:44:12Zsummer 2025
Natural Language ProcessingThe assignments for this class are barely programming assignments. They are similar to Game AI where its more of a "fill in the blank" within Jupyter notebooks.
There was a new exam format this semester and the exams were tricky. The weighting was also increased. I'll squeeze out an A, but only barely.
Overall, if not for the exams, its a 1 in terms of a difficulty, but with the exams, you should be able to get a B with ease. Getting an A would require a bit of preparation for the exams, but completely achievable.
I read the reviews prior to taking this course and they were spot on about the Meta lectures. Modules 1-6 are some of the best lectures in OMSCS, but a few hours into the Meta lectures and you'll be wondering if there is even any point in watching them.
I've given the rating a 3, because it is clear that the frustrations with the Meta lectures have gone on for multiple semesters and I don't believe that people have not complained about them in the CIOS survey, yet they remain. If they replace these lectures, the class would easily be a 5.
Rating: 3 / 5Difficulty: 2 / 5Workload: 4 hours / week
FMNslC4RWdacX7iQ5ONhdw==2025-08-03T20:45:26Zsummer 2025
Software Development ProcessOverall, this is a fair introduction to software development. For transparency, I took this course mostly to avoid having to take GA because it seems like a ton of work with overall poor experience and I've already taken an algo class in undergrad. For context, I'm almost done with this program.
For every course, I think it's important to try to understand what are the "skills" that a course teaches or helps reinforce if you choose to take it. SDP provides you with some basic experience in the following skills:
I finished with a high A in the course and my strongest advice to anyone (which goes beyond just this class tbh): learn to read assignments carefully. To those complaining about the TAs, the student body in this course was the worst case I've seen of people constantly re-posting redundant questions or things that are clearly outlined in the assignment instructions. The staggering amount of poor posts is a canary to me that people are clearly not reading the instructions.
It's probably true you need to be extra careful in this class compared to others. If you misread or interpret something, it can definitely cost you some points. Want a tip? Before you start and prior to submitting an assignment, read the Ed Discussion board posts to go over anything that you may have misinterpreted or that the staff has posted about. I've made this mistake in other classes but it's become a best practice now to always review the Ed Discussion board.
With regards to the Group Project, overall I would say it was fine. I wouldn't say I had the best group but we got by. Honestly the hardest part are the non-technical and kind of awkward factors of figuring out how to collaborate, divide up work, coordinate when to meet, etc. Start early and be proactive.
I personally rated this a 2/5 difficulty but if you do not have much experience with software development, this class could definitely be a lot of work. However, I'd argue that if you don't have much software experience, then the opportunity to have a good return on investment with this class is high.
Don't be afraid of the other reviews complaining about this course. An A is totally achievable; you just need to read the damn instructions.
Rating: 3 / 5Difficulty: 2 / 5Workload: 5 hours / week
j+rann0g9HeMxS4wpX+LXw==2025-08-03T16:43:23Zsummer 2025
Deep LearningI’d like to confirm the other reviewers’ assertions that there seemed for me to be a disconnect in my prerequisite knowledge and the expectations of this class. As an OMSA student, I took ISYE 6740 a year beforehand and got an A, but it wasn’t fresh in my mind, and I wouldn’t say that I entered the deep learning course strong enough in machine learning and analytic modeling to get an A for this more advanced material. However, it was pretty straightforward to get a B. It really comes down to the oddly difficult quizzes for the difference (I ranged from ~40-90% on them) if you don’t ace the assignments.
Pedagogically, this class isn’t so good; the lectures in the first two modules ranged from pretty good to mediocre, and the third module just had a lot of poor lectures (especially the meta ones). They slap a bunch of research papers, textbook reading, and other random resources on, then tell you to figure out how to read them and discern information without much, if any, guidance at all. With the class being so demanding, the expectations for what skills were necessary were just taught vaguely and it was definitely a course where one is expected to do a lot of self learning. It’s either figure it out or ask questions repeatedly on Ed and there’s not much scaffolding, so I found myself using a lot of resources outside of the class to learn. The first two assignments are helpful and interesting, at least, but like the other reviewer stated, they still hadn’t fixed A4’s weird debugging issues as of summer 2025.
The summer felt like a frenetic pace, especially by June, where there was a proposal, quiz, and A2 due the same weekend, so this arrangement definitely is not for the light of heart. If you’re willing to bust your butt (20-30 hr a week, which I definitely did not do), you will get something significant out of this course because the expectations are pretty high, but since I had to teach myself so many things, it left me wondering what I was getting that I couldn’t get from some of the free deep learning resources readily available on the internet.
Rating: 3 / 5Difficulty: 5 / 5Workload: 12 hours / week
pz3P6gL6b/Oj8NdJKl7PTA==2025-08-03T13:00:59Zsummer 2025
Statistical Modeling and Regression AnalysisI was for sure nervous coming into this course a year after hearing many bad things in Summer 2024. Overall, the revamped version of this course has led to slight improvements. I am pleased to get an A (93%) but there are still issues that bugged me and fellow peers. Please take note that the comprehensive review (pros and cons) I provide below is for a summer course, so your experience might vary in a Fall/Spring semester.
PROS:
The course content is spectacular. They really go into the depths of regression modeling and you learn way more than just what a typical regression equation looks like. The modules covered include simple linear regression, multiple regression, GLMs and variable selection. If you are a junior or inexperienced data professional like me, I would strongly recommend this course because it goes beyond the regression fundamentals covered in 6501 and 6203.
The supplemental coding files on GitHub are top notch. They give you the freedom to use either R or Python for HWs, exams and project. Regardless of your choice of language, the sample code and markdown cells provide great explanation of the deep analysis you learn to conduct in this course. Ultimate resource to use when preparing for the coding midterm. I have certainly leveled up my R game involving EDA visuals, training, testing and evaluating models thanks to this course.
The project is a good opportunity to implement all the technical details you learned in the class. You also get to improve your data joining/merging skills as a requirement is to find multiple datasets from various sources.
Exams (both theoretical and coding part) are open-book. So you can employ the CSE 6040 strategy of storing all your resources in a local folder and refer to them during the exams. No internet browsing allowed, only Stack Overflow is permitted.
CONS:
Be prepared for intensive self-study. I think this applies for majority of OMSA courses. The lecture videos like always are of no use. I got an A without watching a single video and instead relied on the course transcripts. So in a way, I had to learn and understand the content on my own with minimal support. You can always attend OH and/or ask questions on Piazza but I personally did not because of timing issues and cluttered Piazza threads.
Technical problems and errors from the teaching staff. This was a constant frustration throughout the semester. Examples of errors include posting incorrect solutions forcing students to redo peer reviews, switching to Jupyter environment for coding and providing no support for students wishing to use other IDEs, and finally messy project peer reviews that ultimately got scrapped.
Ambiguous wording in exams and homeworks. Another complaint raised in previous semesters that has not been addressed. I once read somewhere that since the exams are switched to open book, this is a tradeoff we simply have to accept.
GENERAL TIPS TO SUCCEED:
Don't waste time on watching lecture videos. Download/print the lecture transcripts. Read them, highlight them, understand them.
Reviewing stats before taking the course will make the understanding of course content smoother as there are many references to estimation. confidence intervals, distributions, degrees of freedom, etc. Calculus and linear algebra not needed.
Ahead of the exams, make organized folders where you can store your files. For the MCQ portion, transcripts, personal notes, printed PDF of HW quizzes would be helpful. For the coding exam, the supplemental GitHub files and HW solutions would be ideal resources.
For the project, find group members that have experience in working with big datasets, joining data, writing professional reports and APA citations. You'll thank me later.
Rating: 3 / 5Difficulty: 3 / 5Workload: 18 hours / week
zIZzXHYQ2jFLtOKoXRineg==2025-08-03T05:26:02Zsummer 2025
Machine Learning for TradingOverall this was a good course. I appreciate how much time and effort the staff put into responding to questions and posting updates. The content is interesting and has a good balance between ML and the stock market. If you are super skilled in ML you might not learn a whole lot on that side, but there is an RL section which is cool. The lectures are a bit dated (like 10 years), but they are taught well. The projects are interesting, but I kind of wish they were more open-ended. This is bc the lectures typically go over the pseudocode required, so you don't need to think too hard about them. The project requirements are also pretty ridiculous, especially for the final project (something like 7k words?!). I felt like I was more focused on following everything from the requirements than learning or exploring the concepts. As for exams, I found the wording of the questions to be confusing at times. I got a pretty comfortable A after getting 100% on all projects except 1, and doing slightly above average on the exams.
Rating: 4 / 5Difficulty: 3 / 5Workload: 13 hours / week
1Pg6oH9Oh6M3xfCDcnfaFA==2025-08-03T02:39:56Zsummer 2025
AI, Ethics, and SocietyThis course is easy but has very heavy workload on assignment. Assignment sometimes doesn't have very clear instructions so you need to confirm with TA and classmates.
It is an easy course but requires a lot of time.
Rating: 3 / 5Difficulty: 2 / 5Workload: 12 hours / week
qATGWtZ1g4E/4np6V1uifg==2025-08-02T20:01:07Zsummer 2025
Network Science: Methods and ApplicationsVery simple class that can be combined with others.
In the summer, there were 14 quizzes, mostly weekly but sometimes 2x a week. These were 35% of the grade and, despite being open notes, could get rather tricky.
There were also 5 assignments, with the lowest score dropped. I didn't even attempt the final assignment because I had an A secured with the first 4 assignments. I thought these were all quite straightforward, and the only difficulty was coming up with appropriate test cases since there is no autograder.
The subject matter itself is pretty interesting but overall not presented well. The majority of the class is just text pages on Canvas in the most non-user-friendly way possible. The worst are the videos, during which the professor mumbles incoherently is his thick accent. I would rather those videos just be converted to text. You will roll your eyes every time a video pops up on a lecture slide.
Overall, I'd give this class a 3/5 rating - it's just okay. It could be better with autograded assignments, fewer weekly quizzes and improved lectures.
Rating: 3 / 5Difficulty: 2 / 5Workload: 10 hours / week
eclWoB6DO9IwxOjxQQAobw==2025-08-02T19:55:15Zsummer 2025
Data Analytics and SecurityIf you want to take this class, ask yourself a few questions:
I took over 15 courses in OMS program. This is the worst course I have ever taken. The material is outdated and is totally useless for data analytics or security. It's a shame that this is a graduate level course. I can most likely teach my 8 years old to get a B in this course.
Adding insult to injury is TA has to randomly taking off points and not follow Rubric on grading. It's almost seem like they are worried about too many people getting A.
They need to CURVE for all these ridiculously simple assignments. So go figure.
The course allows ChatGPT, so this is how you can spend less than 1 hour to take care of everything
All the discussion? Everyone is just posting random ChatGPT nonsense, so just do the same. All the Python , R Assignments? Totally useless with vague expectations. It's like your first assignment you would expect after you learn Hello World in python 101. But the fun part is TA's grading. They wouldn't tell you what they expect, and they don't follow Rubric anyway. So just dump it in R-Studio, export, and ask Chatgpt to expand your code. All the Quiz? It's open book, ChatGPT can solve 99%, or if you feel like too easy, just search in the provided transcript. The material is useless, so don't waste your time Final Project? Good luck getting a team that would work in the last month. Grading is a total joke, highly depending on the TA. I ended up just doing almost everything and put everyone equally contributed as a I need an A.
Did I get an A? Yes, but I felt this class was more challenging than ML, AI and other difficult classes that I spent 30+ hours/week. My struggle is by the design, this course doesn't evaluate your knowledge. You need luck to get an A.
Rating: 1 / 5Difficulty: 1 / 5Workload: 1 hours / week
Y0p+1lfk2jRxT+Y8MpA7lA==2025-08-02T14:31:16Zsummer 2025
Introduction to Graduate AlgorithmsThis is essentially a proof writing under stressed situation course. You will be trained to write proofs in a specific way and will get ample opportunities to practice proof writing through practice problems and homework. However, if you panic in the exams (like I did in Exam 2) or make some mistakes/misinterpret the question (like I did in Exam 3) you may fall short (like I did) which would be a very painful experience. This was my 10th course and I missed graduation by 1.5 marks which really hurts. I have 9 As so far and then this C...lol. Having said that, the content of the course is very good and any graduate student of a computer science program must build good familiarity with the covered topics and there was no way I was going to miss this course (Especially since OMSCS is my first computer science program). However, I am seriously considering taking AI and changing my specialization to II (Now AI) as I don't think I can relive the pain of taking multiple 3 hour long conventional exams again and then giving some random faceless TA the power to lecture me in the feedback.
In all, a useful course but designed to inflict maximum pain on the students which sucks out every ounce of fun from this course. You are always on the edge and can not afford to let your guard down (at least in the summer semester). The course format of 3 exams with 2 long form questions worth 20 marks each and some MCQs worth 20 marks took me back to my country's pedantic school education system where the prime objective of education is to make students prepare for the exams.
Rating: 2 / 5Difficulty: 5 / 5Workload: 25 hours / week
IgZF+mzgltrHiRofRINEfbTEQ78n5z7WJkPj0r3lRFw=2025-08-02T12:29:29Zsummer 2025
Special Topics: Financial ModelingThis course teaches the following:
At the end of the course, you will have one comprehensive excel file that starts with financial statements downloaded from the internet and ends with a full valuation.
There are a lot of topics that are covered. These topics are what you would typically learn in an MBA program, as part of core finance. However, in this course, you will actually implement it.
I liked the course because it reinforced what I had learned in my MBA program.
The assignments are a cake walk because you are actually given the exact instructions that you need to follow and then there is another file that serves as your North Star, so when you compute the numbers, you know what the answer must look like.
There is a ton of Excel use in this course. You will learn a lot of formulae and some cool things like Data Tables and Solve.
The presentation material is really light, since 95%+ focus is on using excel to actually do the valuation.
I wish the course delved into the theory a bit more, so that students develop a strong intuition. For instance, Valuation is both an art and a science (Prof. Aswath Damodaran, NYU Stern). We learnt mostly the science part, but the more important part around assumptions and the rationale behind the assumptions is missing.
You would like this course if you want to actually see how the different finance formulae work together. You will not like this course, if you are looking for a typical analytics or computer science course.
I also feel that given that Python is used in Finance more commonly that the professor considers redoing the lectures in Python. I know that will be a complete re-write, but it will make the course more relevant.
Rating: 4 / 5Difficulty: 2 / 5Workload: 6 hours / week
9AIBpLdKh5gudR0j51RJBQ==2025-08-02T10:13:32Zsummer 2025
Deep LearningThis class was quite pleasant in summer with a practical pedagogical style that is much better than ML's focus on conforming to a set of guidelines.
The assignments, quizzes and projects are designed to test your understanding (vs. how to write reports).
The class is front loaded, with probably the most tedious work in A2 over summer. This assignment seems to be designed to weed some people out before the drop deadline. If you're completely new to DL and can make it through A2, things should be fine.
The quizzes can be quite tough, but are a relatively small percent of total grade, so make sure to get 100% on the assignments and project, which gives you slack for worser performance on the quizzes.
My average for the quizzes was around 80%, but I'll finish with a high A.
Rating: 5 / 5Difficulty: 4 / 5Workload: 15 hours / week
VgBZ5q2cK2PdyQCErWPMmA==2025-08-02T10:07:59Zsummer 2025
Introduction to Graduate AlgorithmsI am writing this review as a student taking this course for the third time, with my first attempt being in the summer of 2024. It has been a long journey, but I've learned an immense amount and feel I have a valuable perspective on the course's evolution and what it takes to succeed.
First, I want to commend the teaching staff for the continuous improvements to this course. I truly appreciate its current centralized and parsimonious state. The efficiency of well-organized, consolidated materials is a massive service to the student body. If the materials save an average of 10 hours for one student, that's a collective saving of 10,000 hours for a batch of 1000 students. This is a phenomenal contribution. Moreover, centralized material makes maintenance far easier and significantly increases the likelihood of keeping the algo content high-quality and up-to-date.
Most importantly, it is mandatory for an algorithms course to keep the bar high; otherwise, the OMSCS degree risks meaning nothing in the competitive market. I sincerely hope that a course with this level of rigor eventually becomes mandatory for all specializations.
For Future Students: To give some context, I have designed and proved the optimality of new algorithms in peer-reviewed computer science publications, yet I failed this course twice. I didn't spend much time this semester but I spent tons in the first. You have to follow the specific instructions to pass.
IF YOUR TIME IS LIMITED: DON'T prioritize the textbook. DO watch all the course videos. DO READ ED DISCUSSION POSTS CAREFULLY. This is not optional. Read every announcement and clarification. Ask questions if you have them—the TAs provide essential guidance here. DO WATCH THE OFFICE HOUR (OH) VIDEOS FROM JOVES. DO ALL LISTED QUESTIONS ON ED. Complete every practice problem provided by the teaching staff. ==TAKE THIS COURSE IN SUMMER IF YOU CAN==.
Homework: I cannot comment on assignments for the current semester, as I did not redo them this time around. However, based on my previous attempts, I was already familiar with the rough structure of the solutions and the required approach.
Rating: 5 / 5Difficulty: 5 / 5Workload: 20 hours / week