Statistical Modeling and Regression Analysis

This class is overbloated garbage. Everything feels like a chore. They shoved 2 midterms, both coding and mc into it and the instructions for the coding portion is a multistep mess. There's a crappy group project but you cannot even pick a topic you want to do. The least offensive portion of the course is the homework, but even that is overly long. Some of the TAs are also very condescending and feel the need to add extra bs to their responses to student's questions. Just take another elective over this trash class.

I was worried about scary reviews but they reorganized this class in 2025, and things improved a lot by the time I took it. Overall it was a nice chill class with rewarding materials. I liked how homework had lots of hands-on coding exercise. It helped me solidify my understanding of the concepts. Also I appreciated how they allowed a choice between Python and R. I went with Python, and it was generally smooth. Overall really positive and productive learning experience.

The 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.

As 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.

I 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:

1. 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.

2. 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.

3. 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.

4. 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:

1. 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.

2. 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.

3. 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:

1. Don't waste time on watching lecture videos. Download/print the lecture transcripts. Read them, highlight them, understand them.

2. 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.

3. 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.

4. 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.

I knew this course had disastrous reviews semester after semester. And wow it didn't disappoint. It was so poorly managed that it was really stressful.

You constantly have to chase every little thing. e.g. the instruction is to submit the code as html file, but the Canvas doesn't accept HTML. Group project peer review setup was messed up on Canvas that students received reviews & scores meant for other students. HW solution had bugs and TA kept fixing & updating the solution file while peer review grading was happening, so many students got graded on wrong solutions. Different TA giving really contradictory answers to the same HW question on different Piazza posts. Million examples like that.

Aside from the poor organization, for the actual content, this course didn't add much to what was already covered by ISYE 6501 & MGT 6203. It's not a criticism of the course. It's just that, objectively, someone who took 6501 & 6203 will have an extremely low ROI.

Overall, I regret taking this class.

I had a very disappointing experience with this course. While I love regression, I’ve come to dislike this course. The homework didn’t align well with the exams, and there wasn’t any opportunity to improve grades through projects or bonus points. It often felt like the goal was to lower grades and diminish confidence. I’ve never had such a negative experience in my entire academic journey.

This course offers a solid foundation in higher-level statistics, and your experience will largely depend on what you value most in a program. Here are some pros and cons to help others decide if it’s the right fit for them.

Pros:

Relevant Material: The course content is highly relevant and provides a strong statistical foundation that I genuinely feel has enhanced my understanding of the subject. TA Support: The TA office hours are very helpful, and the TAs are generous with resources, including solutions to prior exams and homeworks. If you take the time to attend these sessions and engage with the material, you'll find them valuable. R Programming: The R programming components of the exams are manageable if you genuinely learn the material. This section rewards effort and application of class topics. For students who complain about these sections, I’d suggest avoiding shortcuts like relying solely on ChatGPT or copying TA-provided code. Instead, study the homework and practice using R as intended—it’s quite straightforward if you do. Cons:

Exam Design: The multiple-choice sections of the exams can be frustrating. While the questions are technically precise, the wording often feels unnecessarily tricky. You might lose points over nuanced phrasing rather than a lack of understanding (e.g., “this isn’t ALWAYS true, just MOST often true”). This can feel disheartening if you're aiming for a perfect GPA. Videos: The instructional videos can be difficult to follow on your first encounter with the material. However, they become much more useful if you read ahead and gain some familiarity with the topics beforehand. Overall Thoughts: This course is both rewarding and challenging. It’s a great option if you value mastery of statistics and are willing to put in the effort. While the multiple-choice exams could use improvement, the material is excellent, and the programming components are fair and practical. For me, the biggest downside is that the exam design likely cost me my perfect GPA, as I’ll probably end up with a high B or low A despite studying extensively. That said, the knowledge I gained from this course makes it worth the struggle.

I took this course in the summer of 2024. My main suggestion is to change the grade weightage by giving more importance to the four homework assignments. This would really help reduce the pressure to perform well on just the two exams. Also, I have to mention that some of my classmates were really disrespectful to Dr. Serban and the TAs, which made the class pretty unbearable for those of us who were trying to focus and learn. Below are some of the comments:

"Amazing. We are a technology school and we have to grade the papers by hand. Absolutely NUTS. This class is the peak of amateur hour. COMPLETE JOKE"

"Cry me a river - the instructor team signed up for the job. If they didn't want to spend their summer reviewing honorlock videos that's on them."

"Since the TAs took it and some managed it in under 75 minutes when can the videos of their session be released so we can see how they better organized themselves than we managed to?"

"I am literally hoping summer to end now because of this nonsense !"

"Very well pointed out an example of a good and sensibly structured exam: CSE604"

"Yeah total bullshit."

"Pathetic timelines for the exam"

"Thats annoying AF!"

"They definitely are - their silence speaks volumes."

"Academia is littered with educators like this and make it unattractive to prospective students"

So do we really have to come on this forum to bash the exam and course again? "Yes I would hope so. This forum is setup to …….check notes….. discuss the class and exams!"

"I included have sometimes been unprofessional in my responses here."

"one of the threads too about some BS etiquette and using that to possibly remove students from the course."

"I request considering the option of requesting additional resources from the ISYE department to meet this minimum educational responsibility."

"Kindly ensure that we do not receive incorrect peer review grades because of inadvertent errors by the evaluators because of "not easy to use" format."

I was part of the summer 2024 section for this course, and if you've read past reviews both on here and on reddit, you'll see that was a bit of a roller coaster. While I definitely think there's room for improvement in this course, and many of the gripes are legitimate, I wouldn't necessarily let those reviews scare you away from taking it depending on your situation. If your goal is to learn regression at a deeper level, this course will definitely provide you that, and I found the workload very light compared to other courses which also may be appealing to some. However, if you're looking for an easy A and care heavily about your GPA, the exams may make that difficult for some.

Starting with the lectures, I didn't experience any issues with audio quality that others had mentioned. The professor has a thick accent, but I was mostly able to understand her and was able to refer to the transcripts if I couldn't make out a word. I also found the lectures to be pretty well organized/structured, as many of the modules follow a similar approach. Despite the chaos, it seemed many students on piazza agreed that they found the lectures informative. I will say that there are certain topics that she could explain better and did involve me having to google to understand it deeper, but I didn't find that to be a huge burden. Additionally, the amount of statistical properties thrown your way may be daunting at first, but it settles down, and you don't really need to understand the "Why" behind those properties to do well in the class. You can just write them down on your cheat sheet for the test questions on them. If you took simulation or the pre req statistics course you'll probably understand it better.

Another thing about this course that many don't seem to mention is the pacing. I took this in the summer and I still found the workload/pacing to be very light compared to other courses. There were four modules released every two weeks (three weeks for module 2), each with a homework. There was also no material released on exam weeks which was really appreciated. I was often able to get the videos and hw done in one week and then have the following week to either review the material or take a break from the course.

Then there's the exams, and this is where the course could really use some improvement. It's 40% theory and 60% coding and people had legitimate gripes with both sections. As many have mentioned, the wording of some of the multiple choice questions seem to be designed to trick you. You may understand the concept, but the way the question is worded may make it hard to follow what it's actually asking. I think these should be improved, although thankfully it's less than half your exam grade. Through reviewing the lectures and making a detailed cheat sheet, I was able to score in the upper 80s and 90s on the two multiple choice sections so I think it's doable with sufficient preparation, but still not the best way to test people IMO.

For the coding portion, there didn't seem to be issues in past semesters but for whatever reason, this semester these exams were also flawed. My biggest gripe is that they don't allow open internet on these, so if you don't know how to code something and it's either not in the lectures or just hard to find, you're kind of stuck, and then subsequent questions are affected. Coming into this class with some background in R would be useful to ease the stress of issues like that, but mostly all the code should be available in the notes. I think making it open internet like in CSE 6040 is the best route forward to address this concern as coding in the real word is also open internet. The big issue with the midterm was timing as many people didn't finish. I barely finished but if you had trouble with your code at any point I think it would've been real difficult to impossible to complete this in time. They seemed to recognize their mistake and gave everyone 5 points back, and additionally offered an open internet replacement midterm if you wanted. This is the period when piazza got pretty nasty between the professors/TAs and the students, as you'll see plenty of mention of it on here and reddit. For the final, I didn't find the timing to be an issue but this is where I experienced a coding issue that wasn't covered in lectures. There was a data type issue that needed to be debugged and again open internet would've been really valuable here. Another gripe people had was the material covered was harder, as an algorithm that was lightly covered in lecture, but never in homeworks, showed up. I personally think anything covered in lecture is fair game and that question could be solved by referencing the lecture code. The scores on the final were also very low and they ended up curving the whole class by 5% in addition to the 5% midterm bump (A is 85+, B is 75+, etc).

In summary, I think there are some improvements needed in the exams such as more fair multiple choice questions, and open internet coding exams with sufficient time offered, but you'll definitely come out of this course with a deeper understanding of a very important topic. My best advice is to ignore the chaos, study the lecture notes and code thoroughly enough so that you feel sufficiently comfortable, and you'll be able to do well. If you're super worried about your GPA and don't want to experience the exam stress, then I also understand why someone would skip this and learn it elsewhere, but I truly believe doing well is possible with the right preparation.