Course Details

Detailed Review: Pros: 1. High quality lectures, clear example code 2. TA's really responsive on Ed 3. Homework fairly easy, exams are essentially take home Cons: 1. Lighter on theory 2. 3. Detailed Review: Overall, this was a really well-delivered class. Prof. Parast's lectures are well-paced and clear, with accompanying lecture notes and thoroughly documented R code. She gives a good overview of biostatistical concepts, though she occasionally hints at their applications in other disciplines, like economics. Not a con, per se, but compared to other classes in this program, it's lighter on the theory. She doesn't always elaborate on the inner workings of some of the more complex R functions and packages, though she does an excellent job of describing their applications. The TA's were really helpful on Ed, especially the lead TA who provided detailed answers, often with links and citations, to every question, including the more involved ones. The 6 homework assignments were mostly simple (i.e. 15 questions of basic definitions, calculations, and R programming), and the lowest grade gets dropped. I thought the midterm and cumulative final were considerably more difficult. Many of the questions either required additional steps and calculations compared to the homework or weren't covered in the homework at all, but I thought they were overall fair. There's a 48-hour window to complete both exams, and they don't have to be finished in one sitting. Overall, the workload was fairly manageable, at the lighter end for this program, but I still felt like I learned a considerable amount.

Pros: 1. The code is provided in R format. However, you can adjust as needed. I also use Python to double-check at times. Code is not evaluated; it is used to obtain results. 2. 6 HW throughout the semester + two exams 3. The professor provides interesting anecdotal and empirical examples of the analysis's application and makes an effort to translate statistical methods into meaningful mathematical and practical applications. I liked the professor's lectures, I enjoyed doing HW in R, it's nothing too crazy, datasets are clean and given.n Cons: 1. The math part in the beginning was intimidating, I thought I had two probability courses at once :) However, ultimately, for HW, you need all the proofs and calculus. R/Python if you wish 2. The course is both easy and complex at the same time; it is recommended after a probability course, but can be taken separately. 3. Solutions are not published; you need to attend OH. We discussed results after grades are published in a group, and I have adjusted the code if the answer was incorrect. However, if you need accurate answers, you will either need to attend OH or join a study group and discuss them afterward. The midterm exam wasn't actually like homework. While homework would take me 1-2 hours, the midterm took me 8 hours, yes I did recheck some parts of it, and had issues, there is no way reverse engeener and check the answers so you have to know what you are doing, my way is to use pythin/collab at times to check if I am getting the same results. On the good side, they are not time-bound. But exams have only a 48-hour window. Together with the Data Viz course - my fav elective so far: interesting, practical, time manageable.

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

Pros: 1. Pretty straightforward assignments/tests 2. Great & organized lectures and course notes 3. Responsive TAs/LFs Cons: 1. Some repetitive plug and chug in R 2. 3. Detailed Review: Overall, I really enjoyed this class. I have been dreading taking the more stats-heavy classes in the curriculum but this was a nice surprise as it was a new class added this semester. The lectures and accompanying notes are really well done and organized. There's quite a bit of lecture content to watch, but I don't think it was super necessary watch them all if you just want to read the notes. Well documented starter code and R-code walkthrough lecture videos as well. The homework assignments were designed to take you 60-90 minutes and that was pretty much on-point or an overestimate for most of them, and you get one drop on top of that. The 1 midterm and 1 comprehensive final were untimed and open notes but you had a 48 hour window to complete them. I wish more courses in MSDSO had untimed exams as they were significantly less stressful and you could actually check your work. I think this course would probably pair well with any of the other courses that don't have tests like DL, regression, APM, and data viz even if you are working full-time. Taking this course over CI for elective group 1 is probably a no-brainer as this course also contains a whole section on CI and is likely way easier (just based on the reviews, I haven't taken it). The TAs/LFs on Ed were really on top of things responding to questions most of the time within minutes of being posted. I thought the reliance on various R packages for most of the later segments got repetitive. The first few segments had some hand calculation examples which I thought were really great at making sure I understood what was going on under the hood of these packages. I just wish some of that was carried forward to the later half of the course. Also, I think it would be great to show more examples outside of the healthcare space as I'm sure most students are not in that industry. I like being able to attempt to apply what I learn during each course at work but I didn't really feel like anything applied for me (I don't work in healthcare/medicine). Maybe adding a lecture section showing some applications outside of healthcare would be cool.

Pros: 1. Best lectures in the program 2. Solid material 3. Low time commitment but high reward Cons: 1. Lots of questions left about theory 2. Repeat material if you've taken CI 3. Surface-level covering of some topics that could've been expanded on Detailed Review: First off, Dr. Parast is the best lecturer the program has had so far. Future courses (and redesigns of courses) need to follow her example. Her lecture notes and code examples were also very well done (in stark contrast to some courses in this program). The course was split up into 4 parts: Survival Analysis, Longitudinal Data Analysis, Observational Studies, and Randomized Studies. The first two parts are done very, very well. A complaint I have is that there could be a lot more theory. It felt like some topics were just "run this package" and we didn't need to really understand the theoretical principles behind what we were doing. The course assumed a knowledge of probability theory and regression/hypothesis testing but there wasn't really any linear algebra or calculus needed. Similarly, we could've gone deeper into some topics. For example, missing data was covered but the state-of-the-art technique (MICE) wasn't mentioned at all (although MI in general was). The time commitment was overall quite low, so I think there's room to address these complaints. The last two parts are a complete repeat (and not in anywhere as much detail) if you've taken CI. However, they are a great refresher to the basic ideas if you have taken it. Similarly, there are a few topics sprinkled in throughout that you might be able to skip if you know some machine learning (cross-validation, ROC, etc). The 6 homeworks were a breeze. I only missed one point all semester due to a silly mistake and one homework was dropped in the end. Exams were also quite easy: 48 hour take-home. The final exam required more thought but in the end wasn't significantly harder than the midterm. Median was around 93% for both. I wouldn't be surprised if over half of the class got an A (no curve, of course). There could be more difficulty in the homeworks. On the final exam, there was a question that required some thought on a formula that was used in the class. While doing the final I remember thinking "I wish the homeworks had stuff like this" rather than plug and chug. The TA/LFs were also very good and active on Ed. Prof wasn't active on Ed but was available to field any questions that the TA/LFs were unable to answer. This is the first semester that it's being offered so I wouldn't be surprised if there are a lot of changes before the fall.

Pros: 1. Good balance between theory and application 2. Lectures and notes are well-structured and easy to understand 3. Low time commitment Cons: 1. A "just use this package" approach to some portions 2. Last 4 weeks are a repeat if you've taken Causal Inference Detailed Review: This was my last course in the program, and I thought it was on par with data viz as one of the easiest. If you're coming in with a strong stats background, you can probably jump right into this course and do fine, but if not, I would take Probability and (arguably) Regression first. Knowledge from those two courses is assumed and generally not reviewed in depth in this class. The course is broken up into 4 segments - survival analysis (6 weeks), longitudinal analysis (2 weeks), observational studies & causal inference (2 weeks), and randomized experiments & statistical power (2 weeks). The last two sections in particular are largely review if you have taken Causal Inference. I thought Dr. Parast was one of the best lecturers in the program for the clarity of her explanations and the way she weaved theory and application together. The TA and LFs were great too and offered lots of helpful explanations. The general formula throughout was for the professor to explain high level concepts, work through a simple example by hand, and then walk through code (R only) showing a more complex example. My one criticism is that she often glosses over some of the more complex math in favor of a "just use the package" approach. While that certainly makes the course easier, you're on your own if you want more depth. (For those who hate R, there is no graded code in this course. You can use Python or another language if you want, but you shouldn't expect any instructor support if you're not using the tools provided.) On that same note, homework assignments are generally not very difficult. The theory questions can usually be answered straight from lecture or the course notes, and applications are usually a matter of replacing variables in the professor's sample code. Almost everything is multiple choice too. The midterm and final are similar in structure but include some fill-in-the-blank numerical answers. This course probably has the lowest time commitment of any in the program. Lectures average about 2.5-3 hours a week if you watch everything, and homework assignments took me about an hour every other week. The only slight timing challenge is that the midterm and final exam windows are strictly 48 hours each - not that the exams take that long, but the open and close times are tighter than for most other courses in the program. Disclaimer: Spring 24 was the first time this course was offered. The professor treated this course almost like a beta version (in the sense of actively looking for user feedback, not lack of polish), so I would expect some changes/refinement in future semesters.