Course Details

Pros: 1. No peer review. Everything was auto-graded objective-type questions or computations. 2. Straightforward quizzes and questions especially in statistics portion. 3. If you have good undergraduate probability and statistics background (at the level of science majors), very easy. Cons: 1. Some homework assignments and major exam questions are challenging especially in the probability portion. 2. If your background in probability and statistics is of social science, humanities, or no background at all, this course might get tough. 3. Teaching quality between the probability and statistics portions can vary. Detailed Review: The course was pretty much like your typical foundation course on probability and statistics, although the way it was originally taught (Prof. Parker and Prof. Muller) was it was pretty much like two courses merged into one. Common to both segments: General long homeworks (encouraged to answer these in study groups), followed by individual quizzes and 2 major exams. The probability segment covered different types of probability distributions and had a required book (although not all of the book was required. Not all was useful eithe). Some challenging questions were from the book or variants of questions/book examples. The statistics segment was much easier, but uses a textbook-based simplistic software (statkey) instead of typical R or Python for data science majors. I liked a few short segments where Prof. Parker explained the meaning of test statistics, and p-values. Far too many people take that part for granted, resorting to mechanical p-value hacking when people apply inferential statistics in research. The textbook for the statistics portion is optional (wasn't crucial for the course, except for the statkey software hosted in the bookwebsite). Overall, a straightforward class in terms of lectures (good enough at times, but at some times could also bore you. Not too bad compared to regression). In Spring 2025, a new prof took over (Prof. Linares) so I am not sure if there are changes to these features.

Pros: 1. You are learning a lot of theory and practice in probability and statistics. 2. The part with Statkey is very manageable, though tricky. 3. The lecturers are interesting, all the tasks are being fairly managed, on a good and reasonable timeline, there is always OH, and ed discussions. Cons: 1. It's a heavy course with a lot of calculus. I needed to shake the dust from my 20-year-old calculus knowledge. It started to make sense at some point, but it was a little tricky as I was rusty on those. 2. Basically, every week has HW that takes a few hours. Sometimes it took me 8ish hours. Statkey HW took me less, but the Quizzes on Statkey were too quick for me. Detailed Review: Decent but time-consuming course, be sure you have time to deal with it. You need time for lectures, HW, and maybe a calc refresher. Join a study group on Discord. All HW have solutions, you can learn from evaluating those after they are published. The course is mandatory, so if you have time, you can't just swing by; join a study group. Generally feels interesting and fundamental. 2 worst HW and Quizees are dropped so that helps.

Pros: 1. Key concepts covered 2. Statkey is helpful in understanding CI & hypothesis 3. Lecture slides are detailed enough Cons: 1. Lots of technical glitches re. grading 2. Instructions scattered everywhere, platform is confusing to navigate 3. Theoretical distributions are dry and too abstract. Terminology and notations cause more confusion than help Detailed Review: First impression is important. This course could be made better to create a great impression for the whole program, or maybe move it later in the order.

Pros: 1. Professor Mary Parker is very helpful, engaging, and responsive. 2. The students are knowledgeable and class community are collaborative and added significant value. Students actively engaged in clarifying content and correcting errors, particularly on Ed discussions 3. Statistics component is much more application based and the overall class did enhance my understanding of statistical concepts. Cons: 1. The probability content leaned heavily on theory, often making it challenging to grasp. The lecture notes and videos, being directly derived from the textbook and therefore does not add additional value. 2. Need external resources to supplement learning and understanding. 3. Homework are very theory based and requires a lot of time to do. DSC381 was a challenging yet rewarding course, deepening my understanding of both theoretical and practical aspects of probability and inference. While there were gaps in the execution of the probability section, Mary Parker’s support and the collaborative class environment were invaluable to my learning. The required textbooks, available online for free, were a mixed experience. The probability textbook was highly theoretical and lacked practical applications, making it difficult to follow. A more balanced textbook or supplementary materials focusing on real-world examples would improve the learning experience. Mary Parker’s lectures were clear, well-paced, and engaging, providing solid explanations of the material. However, Peter Muller’s lectures were dry and at times unclear, which hindered engagement. More examples and practical applications in these lectures would have made the content easier to understand. The lecture notes closely mirrored the textbook but did not provide much additional insight. They lacked sufficient context or application-based explanations, which would have been helpful for students struggling with the theory. More detailed, application-oriented notes would have been beneficial. The workload was substantial but justified by the depth of the material. Study groups were essential for managing the workload and filling in the gaps left by unclear examples in the lecture notes. Professor Mary Parker’s support was excellent—she was always available during office hours and went above and beyond to ensure students understood the material. I highly recommend attending her office hours. There was no office hours for Professor Muller. Some of the TAs were helpful but not exceptional. While they provided basic support, they were not always effective at addressing common questions. I found attending Professor Parker’s office hours to be more useful. Grading was delayed, which made it difficult to assess progress in a timely manner. While answer keys for homework and quizzes were provided, detailed feedback on specific mistakes was lacking. Timelier grading and more thorough feedback would help students identify knowledge gaps and improve performance. Overall, the course content was strong, particularly in the statistics section. However, the probability portion was more theoretical and could have benefitted from more practical applications to align with the applied nature of the statistics content.

Pros: 1. Quizzes were a breeze 2. Learned a lot of basics re: statistics 3. heavy use of statkey for homework which was convenient Cons: 1. Disorganized 2. Disconnect between material and exams 3. Hard to interact with staff Detailed Review: I thought this would be a good first course back into school to get the hang of things. Knowing what I know now, I would have selected something else like data viz. It's important to note that although the material and assignments themselves weren't hard, the sheer disorganization of the course and fickle auto-grading system were what made it more challenging than necessary because I often found myself losing points on technicalities. Coupled with the fact that hundreds of other students were experiencing the same issues, it was extraordinarily difficult to get corrections. It felt as though you needed to have a near-psychopathic relentlessness to advocate for your true score, which left me feeling so defeated I often just accepted spurious results. My biggest issue with the course was the unnecessary volume with which Dr. Parker posted additional course pdf's and other materials -- some of which was mostly irrelevant, others which were crucial. For example, I missed one pdf which contained specific instructions for the exam that ended up costing me a ton of points, the difference between an entire letter grade in the grand scheme of things. The document was sandwiched between seven or eight others with titles like: "pre-lecture notes" "lecture notes" "post-lecture notes" ... why not just make a singular post called "lecture notes" and avoid cluttering the work space? Better yet, post such important info in the exam instructions. When I discussed this with Dr. Parker, I could tell that she was overwhelmed with the amount of feedback from students related to re-grades and disorganization and had lost patience. Although I sympathized with the situation, I felt that it would have been appropriate to seek help from other instructors on how to manage the course, or to change assignments to completion credit -- rather than deflect blame back on the students for struggling to deal with such a chaotic class. Don't bother with textbooks, you can learn all of the concepts in this course with a simple google. The most you need for this class is familiarity with statkey and loads of patience.

Pros: 1. Professor Parker’s section on statistics was insightful 2. 2 lowest homeworks and quizzes dropped 3. Fairly broad overview of probability and statistics Cons: 1. Autograding on short assignments gave very little room for error 2. Low staff engagement 3. Organization - way too many documents, announcements, and other materials distributed via PDF’s and individual posts on EdX Detailed Review: My background: It’s been a decade since my last undergraduate math course, though one of my previous jobs involved a lot of statistical analysis in a research setting. It took some effort to get used to mathematical notation again, and I didn’t feel like I always had the strongest grasp on it but still ended up doing well in the course. However, there are plenty of opportunities in later courses to reinforce the material. Overall, I felt the class was good for easing back into school, and I learned a lot, in probability by sheer volume of content and in statistics because of how it built on previous knowledge. Mueller breezed through the probability lectures, often skipping steps in the proofs and calculations, and making in infrequent "goofs" on his slides (though he was self-aware of it). His section covered a lot of topics, some of which he overcomplicated. We were quizzed and tested on very few of them, making it hard to know what to study or care about for the first exam. It didn’t help that the homework assignments were heavy on the proofs (which were easier because they were multiple choice), while the quizzes and exams had none whatsoever. The quizzes were fairly easy. The exams were tough and a bit of a time crunch, but mostly fair with the exception of a question or two. Rounding correctly is really important for the assessments, though the drop policy made up for the draconian autograding. Parker went more in-depth in her lectures, focusing on the motivation behind and interpretation of statistical analyses and not just rote calculations and definitions, which I appreciated. This section was easier, and she was very intentional about what she tested us on. She did hold office hours, but the staff in general weren't very present on the Ed discussion board. Thankfully, many students in the class were active and helpful. The only thing that really bothered me about her section was that she provided a lot of miscellaneous materials and reminders scattered in the form of PDF files, EdX “assignments”, and announcements that I felt could have been organized more neatly and concisely. For example, there was a formula sheet we’re supposed to use towards the end of the course, and for some reason, the 6th page is a separate PDF.

Pros: 1. Approachable professor and TAs 2. Learned new things 3. Could drop the worst grades 4. Books were helpful Cons: 1. Sometimes, a lot of workload 2. 3. Detailed Review: I enjoyed the class.

Pros: 1. Material is interesting (for the most part) 2. Exams are fair 3. Cons: 1. Dry lectures 2. Ed discussion board is mostly student-driven 3. Detailed Review: For context, I don't have any prob/statistics background. I never took an undergrad class and the little that I do know were pieces I picked up from work or math classes. I thought the material was really interesting. A lot of the results in the probability portion was counter-intuitive and learning about statistical methods was satisfying. That being said, I started having difficulty in the later portions of the probability section when the math started getting more involved. Definitely took me some time, re-watching the lecture or reading supplemental material to get a grasp on the more math-heavy sections. The lecture itself is conceptually interesting, but very dry during the maths/proofs. I agree with the other reviews here that say skip through the proofs sections in Prof Muller's lectures. The proofs are there for your understanding (or confusion) and do not show up on the homework for the most part. The examples in the lectures are the most helpful portion, and the homework problems are generally an extension of the lecture examples. The statistics portion with Prof. Parker was more practical. Once you figure out the pattern for what to do for each type of problem, it becomes plug and play. The ed discussion board was helpful, but mostly thanks to the students that stepped up. The TAs and Profs were not helpful. The exams are pretty much homework problems. I reviewed the homework covered in each exam and I did just fine. I took this class with DSA, and I ended up spending more time in this class on average, unlike the general consensus. Other swe-type folks might agree.

Pros: 1. Dr. Parker (who taught the statistics portion) is an engaging professor. 2. The curriculum for the applied statistic portion of the course is well designed. 3. Cons: 1. Lack of engagement from the TA. Responses are infrequent and often lacking in clarity. 2. Answer keys do not provide a detailed solution. 3. Detailed Review: To preface, I have previously taken graduate classes in math and use some statistics in my current job working in Data Science Analytics. As others have mentioned, the difficulty of this course will depend on your familiarity with the topics. I have given the difficulty of this course a 2 out of 7, not based on my background, but relative to the difficulty I expected from this course going into it. Compared to a graduate level course, this course was easy. A lot of the more difficult questions and content were excluded or modified to make it more digestable. For example, proof-based questions were reduced to multiple choice questions, which you could solve by spotting the errors in the wrong answers. Other more challenging questions borrowed from the textbook were assigned but ungraded. Some of the quizzes can be easily finished in under 5 minutes when 30 minutes are allocated. I have given the Piazza (now EdStem) community a 2 out of 7. The TA was absent in EdStem, but there were some good responses from classmates that were helpful. I was skeptical about the applied statistics curriculum to start, but have really come around on it. There isn't really a standardize method of using applied statistics in industry; the course taught us how to think about applied statistics and how to create simple applications in StatKey. There is some ambiguity around inferences, wording, and rounding when it comes to applied statistics, but I think overall Dr. Parker does a good job navigating around it.

Pros: 1. It covers well the basics on probability and statistics, acting as a leveling course or to brush up the rust. 2. The statistics part provides a good practice on simulation methods, particularly bootstrapping. 3. The exams are compatible with the other assignments and material presented on lectures. Cons: 1. There are some lectures on probability that seem to overcomplicate simple topics. 2. Little to none staff presence on Canvas for the probability part. 3. Detailed Review: For reference, I had a good previous exposure to math and probability, on a graduate level, and limited exposure to statistics. I spent about 5 hours per week on this course and got a A. TLDR: The course is overall a good one if you are not very familiar with probability and statistics or if you need to brush up some concepts. Adivisable for a first course in MSDS for most. The course is divided between Prof. Muller (probability, first part) and Prof. Parker (statistics). It has two exams (two hours each), first being exclusively on probability and the second mostly on statistics. Both exams were very practical in nature and reflected well the lectures and other assignments. There are roughly weekly homeworks (13 in total) and biweekly quizzes (30 minutes timed assignments, 6 in total). There are some other small proforma assignments (8). The grade breakdown is like this: HW: 29% Others: 1% Quiz: 30% Exam 1: 20% Exam 2: 20% All assignments are open book and notes. For timed assignments you cannot use others people help, but you were allowed to discuss HW in small groups os students (we arranged this in a discord channel). There is no curve, but the two worst assignments from each class except for exams are dropped. You need a 93 for a A and a 80 for a B-. There were a few instances where we could use either R or Python (mostly in MLE) but they are not strictly necessary. For the simulation methods Dr. Parker used StatKey, a free online application. While this is not a industry standard, it provides a straight forward manner for us to perform the needed simulations without loosing to much time on the implementation, which I see as a plus at this point. Just be careful to avoid input rounding whenever possible (this was a headache for some in this course). I found very helpful the course Canvas because one could ask and respond questions from fellow students, which I think is a great method for consolidating understanding. The TAs were not very present. I have not seen Prof. Muller there once. Prof. Parker was aways there on statistics weeks. The books are fair. Because I had very little experience in statistics I used the lock book the most. Anyway, I don't think they are necessary, but a good complement. Regarding the lectures, Prof. Muller based his on math derivations followed by applications. I enjoyed the derivations but some complained about notations and unnecessary complications. I idivise to watch the lectures, but avoid getting stuck if you cannot spare the time. Professor Parker was a little philosophical at times, but overall her lecturers were useful.

Cons (no chance of learning from the garbage instructors and garbage course material with no other place to turn?) 1. Limited engagement from TA and staff on discussion boards 2. Lots of typos, mistakes, and errors (written and verbal) throughout lectures, homework, quizzes, and other course material so you don't even know what's right 3. Unclear and incomplete explanations of many classroom concepts that are eventually tested Continuing Cons: 1. Small hundredth or thousandth place rounding issues cause you to lose points 2. Lots of proofs... as if we all want to really pursue PhD-level statistics... we just want to do better in industry 3. Crazy high passing requirement Detailed Review: It's a shame this is a forced foundational course or there would be 0 enrollment. I think the cons covered almost everything that is wrong with this course, so there isn't much to add here other than a plea to fix this course (switch out the professors for better ones, hire TAs that do more than respond with "email the course email" because that is just so helpful obviously..., and fix the curriculum which is so wildly inappropriate) so that the UT MSDS degree is not a sham.

Pros: 1. Second half of the course is very simple 2. Fair exams 3. Cons: 1. Overcomplicates basic topics 2. Doesn't cover several important topics 3. HW is a headache Detailed Review: My undergraduate class was much better than this course. It's a disappointment in every way. The first half of the course was just convoluted. Every topic could be explained in a better, simpler way than how was presented. It just felt like he was reading from the textbook (which was also a terrible choice. Try Wackerly). Many important topics weren't even covered or just barely touched upon such as hypergeom, neg binom, hierarchical models, mixture models, order stats, WLLN, pdf and cdf methods of transformations etc. Some were only touched on HW- here's how this is defined, go learn it on your own and come back and answer this question. The second half of the course was better, but I feel like a bit more theory there would've been good. StatKey (the simulation software used) was generally good but some questions had frustrating rounding errors done by StatKey to where if you calculate the same test statistic, you'll be off. I ended up not watching many lectures after some point and just working off my undergrad notes which were more than sufficient to get close to a 100% in the course. Absolutely disappointing, wouldn't call this graduate level by any means.

Pros: 1. The class goes over a wide range of important topics including Simulation-based Inference 2. There are very good materials and support in this class Cons: 1. The first half of the class was very math notation and proof-heavy 2. The skills built in the class for the statistical software used, StatKey, aren’t very transferrable 3. The textbook is very dense Detailed Review --------------------------------------------------------------- Pros: I was impressed by how many topics were covered in this course. Some people might find the topic load a bit overwhelming, but I thought it was very comprehensive (in terms of frequentist statistics). I also really enjoyed the focus on Simulation-based inference because of how flexible that sort of analysis can be. I hadn’t ever been taught those more modern techniques until this class. The class itself was very organized and I appreciated the effort there. The class provides lots of handouts and lecture notes to reference. The support on edX was also great. When I had trouble, it almost always seemed like someone had mentioned the same exact issue on the discussion boards. The discussion board community was great for this class. I often contributed and was happy to know that others did as well. Cons: The first part of the class was frustrating to me because I had difficulty reading and interpreting the mathematical notation. Even though I learned the material and knew what to do conceptually, the mathematical notation hindered me. In my undergrad program, I didn’t have a big focus on notation or proofs, but that knowledge will definitely help in this course because it isn’t taught explicitly. The course uses a statistical software called Statkey (https://www.lock5stat.com/StatKey/). The software is great for solving problems in this class, but I wished the class would have taught these concepts in R or Python, where I’ll be employing these statistical methods. I suppose I could whip out StatKey in the middle of a team meeting and upload some datasets to get calculations, but it isn’t very convenient or automate-able. I tried to refer to the textbook a few times when I had questions, but I never got very far. The textbook itself is very dense and I didn’t find it helpful. Usually, I just Googled my questions and found YouTube videos about them when I could.

I will try to give as straight a review as I possibly can. Pros: 1. Good start to the program; lots of detailed information on how to use the edX platform. 2. Lots of very interesting math in the probability section 3. The statistics section teaches some *very* powerful modern techniques right away, such as bootstrapping, which are so powerful they almost make the older theoretical methods seem irrelevant (but it teaches those as well) 4. The statistics section cleverly - maybe inadvertently? - takes you on a tour of just how easy it is to screw up p-value calculations, in subtle ways that you'd never be able to see before taking this course. It is crazy how rounding things to three vs four decimal places can change evaluations of statistical significance 5. The teachers are very knowledgeable on both the history and theory of the topic Cons: 1. Some of the probability theory proofs weren't that well explained; they often involve just staring at some problem with infinite series until the magic trick to solve it pops into your head 2. I would have preferred the class be built around Python or R rather than the StatKey software, although the latter was a useful pedagogical tool 3. The class focuses exclusively on frequentist statistics. I would have been much happier if some Bayesian stuff had been thrown in there. In general, the statistics professor seems like she is very knowledgeable about some very deep mathematical statistics but was required to keep some of it fairly lightweight for this course. Detailed review of pros: I thought this was a good start to the program; it had lots of detailed information on how to use the edX platform, the discussion forums, etc. There are lots of quirks to the edX platform: for instance, if you're in the US, and if a HW is due on some day, it's typically due at 6 AM on that day for the benefit of people in e.g. India, so it's really due the night before. The course explains all of these and other snags in detail. In general it seems built to be the first class you take in the program. There is lots and lots of very interesting math in the probability section: Markov's inequality, Jensen's inequality, Chernoff bounds, convolutions, etc. Most of this was review for me but I still learned a lot of new and interesting things. Lots of famous problems in standard probability theory are discussed. It was good to patch up my (somewhat non-standard) way of having learned things in the past. People who enjoy math will enjoy this class. The statistics section deep dives into powerful modern simulation-based techniques such as bootstrapping, and provides a strong foundation on making sure that results are built with proper numerical precision, clearly showing many pitfalls that can happen with rounding errors and etc. The professor has a very good perspective on the central issues with using p-values and etc and how to use them properly (and how they can be often improperly misused). On some level, the central lesson I got from this part of the course is just how easy it is to inadvertently screw up p-value calculations. It is almost incredible, in fact, just how many ways there are to screw this kind of thing up. There were situations where rounding things to three or four decimal places totally changed statistical significance results; situations where adjusting the value of the null hypothesis by 0.001 changed statistical significance results; situations where internal precision errors in the software to *six* decimal places changed statistical significance results. The professor doesn't really hide her distaste for the typical state of just using p < 0.05 as the cutoff for determining if some statistical result is "significant" and after this class it is very clear why. In general, if you've always been mystified by the talk about p-values and the complexities thereof, this class will clear all of that up for you. Detailed review of cons: I thought some of the proofs and derivations in the probability section weren't that well explained. Some of the proofs involve doing long algebraic manipulations with infinite series, some of which seemed difficult to grasp for me. In general, many times these proofs often involve some magic, nonobvious trick that makes it all work out. On the other hand, the problems you are being asked to prove are often very famous, classical probability theory problems, so it is good to get some knowledge of them. You would do well to have some background in real analysis (although if you don't, you'll still probably get by alright). I would be much happier if the StatKey software that we use were replaced by something standard like, let's say, R, or SciPy in Python, etc. We spent *a ton* of time in the statistics section just learning to use this software. And, worst of all, the software has lots of bizarre "quirks": computing confidence intervals, doing hypothesis tests, etc often involve sequentially clicking several unlabeled buttons such that if you screw one thing up, the entire result is wrong. When doing multiple simulations, some parts of the interface reset, but not others, and you have to remember things like this. This part of the class is basically an exercise in seeing if you can follow directions when using a very poorly-labeled interface and keep all of that stuff in your head correctly and not screw it up - which apparently I can't very well - so the only way I was able to pass this part of class was to end up basically rebuilding all of the functionality myself in Python anyway, just as a way to "sanity-check" my results. However, even this wasn't a perfect solution, as StatKey has some internal precision quirks that often cause things to be rounded incorrectly to three decimal places, and they grade on the StatKey value, not the true value. However, I would push back on this really being a "con" as it ends up being one of the most important lessons you learn in the entire program. Some of the "problems" are not just problems with the StatKey software, but with the entire p-value procedure. It is *so* easy to get the wrong value for a statistical significance result (!) - making something significant that would be insignificant - because you put "0.33" rather than "0.333333333333..." = 1/3 for the null hypothesis and had a relatively large sample. There were situations where rounding things to three rather than four decimal places changed statistical significance results. The class somewhat-but-not-so-subtly guides you to these realizations by actually walking you through all of the things that can go wrong when you compute stuff. I'm not sure if this was the intended result, but after taking the class I am not surprised at all that there is a "replication crisis" in scientific research publications. The last criticism I have of this class is that it takes a 100% frequentist-based view of things. They really wanted you to get up to speed on the basics of confidence intervals, hypothesis testing, etc, which are standard in most statistical publications and scientific research, as well as modern simulation-based methods for doing these things. The older frequentist methods took a very narrow view of what is possible at times, whereas the modern Bayesian methods are much more powerful and standard in machine learning. The teacher is clearly quite knowledgeable about all forms of mathematical and Bayesian statistics and left a few clues for us who are interested, but mostly stuck to frequentist methods.

Pros 1. Comprehensive coverage of probability and statistical inference, ideal for newcomers and as a refresher. 2. No specific software or coding expertise was required for the Statistical Simulation segment. 3. The lowest two quiz and homework scores are dropped (2 Quiz, 2 HW). Cons 1. Probability lectures are fast-paced and theory-intensive, challenging for those without a solid math foundation. 2. The course doesn't focus on coding skills development, which may be a drawback for some. 3. High precision required in answers, necessitating careful attention to methodologies and rounding off details. 4. Extensive handouts and documents to manage, potentially overwhelming. Detailed Review As my introductory course in the program, I found the probability section quite rigorous, particularly since I needed a calculus refresher. The initial complexity of lecture and textbook examples was daunting. However, the statistical inference portion was more approachable. The course effectively deepened my understanding of various statistical concepts like hypothesis testing, chi-square tests, ANOVA, and the Central Limit Theorem. The probability textbook was less helpful, leading me to rely on alternative resources and YouTube. Coding knowledge isn't required to pass this class. While the lack of coding focus helps concentrate on theoretical aspects, it limits practical application. However, for those with prior coding experience, implementing the course concepts in R or Python is feasible and not overly complex, especially with available statistical packages like "infer" for R. Personally, I wrote my own Python scripts for several probability calculations.

Pros: 1. Easy to get a good grade 2. Not time consuming 3. Professor engagement on Piazza Cons: 1. Little to no real world application 2. Uses a web-based software that is used nowhere in industry 3. Grading requires ridiculous accuracy (up to 5 decimal places at times) Detailed Review: Especially if you have a probability/stats background, you shouldn't expect to take anything away from this class. The probability portion of the course is highly theoretical, with no software usage whatsoever. You'll be doing plenty of calculations by hand. The grading also frequently requires a numerical answer to several decimal places, so a simple typo can land you a 0 on a problem. The stats portion of this course is ridiculously easy. It uses a web-based software, which I consider a con because it is useless in the real world. But, this portion of the class is unbelievably easy. You can spend <2 hours per week on this portion of the class on most weeks.

Pros: 1. The probability part is well structured and explained 2. 3. Cons: 1. The statistics part is somewhat not well-organized 2. The TAs have been absent from Piazza for the majority of the semester. 3. Detailed Review: While most homework problems are straightforward, some probability problems can be a bit tricky. Quizzes are generally easier than homework, but mid-term and final exams can pose a time management challenge, although they are not particularly difficult. Although the professor carefully explains all the details, the lecture slides and handouts for the statistics section could benefit from better organization. Having information spread out over 5 or 6 different PDFs each week can be overwhelming. class grade for my semester A,A- 47% B+,B,B- 33% C+,C,C- 6% D+,D,D- 2% F 12%

Pros: 1. Great professor/TA engagement on Piazza 2. Very generous grading policy 3. Long windows to complete assignments Cons: 1. Lots of rambling in stats lectures 2. Better (free) textbooks out there 3. Statkey (and the approach to statistics problems more generally) Detailed Review: Overall, I did not find this course to be very difficult, but it's not a cakewalk either. As others have mentioned, the probability sections are notably harder than statistics, but both will be mostly review if you've taken the respective undergrad courses. The grading policy is quite generous - they drop your lowest two quizzes (out of six) and lowest two homeworks (out of 13). It's also pretty flexible time-wise - though the quizzes (30 minutes) and two midterm exams (2 hours) are timed, you have a week-long window to complete them. Homework assignments are usually out at least two weeks before their due date as well. Dr. Parker and the TAs were very engaged on Piazza - pretty much every post got an instructor answer within a few hours. Lectures are ok, though the professors have very different styles. Dr. Mueller (probability lessons) tends to be very straightforward, almost to a fault, and had me pausing and rewinding quite a bit. Dr. Parker (statistics), on the other hand, often over-explains things, and I found myself just going to read the slides instead (the stats slides are good reference material, to be clear). The probability textbook was somewhat useful, but it's dense and can take a couple read-throughs to make sense. I found myself referring to Blitzstein and Hwang's Introduction to Probability (free online) when I needed a clear explanation of something. I barely used the recommended stats textbook at all, but to be fair, it's listed as optional. My biggest frustration with this course comes with how the statistics problems (homeworks, quizzes, and exams) are structured. I think Statkey is a good tool for demonstrating concepts, but not so great to be graded on. At a certain point, you're being graded on whether you can repeatedly click buttons in the right order, not on how well you interpret the concepts. The delicate balance of machine grading vs. statistical variability is also problematic - I found myself running 20+ iterations of a simple trial on more than one occasion because I didn't get a clear alignment with one choice over the other. That's not because of anything I did, it's just that the answer choices aren't always well calibrated for the data in the problem. Bottom line, the only thing Statkey really made me an expert in was clicking "generate 1000 samples" and force closing my browser when it ran out of memory. The thing that makes even less sense to me is why a statistical programming language like R isn't used instead. The course actually introduces some problems in R in the first couple of weeks...but then you never hear about it again. That's not to say there aren't challenges to be overcome with using R as well, but at least your parameters don't get reset every single run. R doesn't go offline in the middle of timed assignments either. But in this class, you MUST use Statkey - there are problems that you will get wrong if you use any other method. As an aside, I took this together with Data Viz while working full time. I found that to be a very manageable combo - about an hour each weeknight and 3-6 hours total on the weekend.

Pros: 1. Builds a good foundation in many aspects of probability and statistics 2. Simple workflow of weekly homeworks, roughly bi-weekly quizzes, and two exams 3. Quizzes and exams easier relative to homeworks (with 2 homeworks and quizzes dropped) Cons: 1. In the probability section, the lectures are high-level and hard to follow 2. In the statistics section, the materials are highly disorganized 3. Lack of depth and purpose in some of the material 4. From an editing and delivery standpoint, the lectures across the course are lacking Detailed Review: The first half of this course focuses on probability and the second half builds on probability through statistics of many forms (simulation-based, theoretical, mathematical). The professors claim the textbook for the probability section is required, but the problems from the book on homeworks are given in the homework documentations and on EdX. Sometimes the answers to the homework could be found directly in the book. The probability lectures essentially paraphrase the book, so the book can be helpful if you want more detailed explanations. The lectures in the probability section are pretty lackluster, comprising of concepts being introduced, proved, and then applied on repeat. The professor paraphrases the material of his slides and goes quite fast making lectures hard to follow. Also, there were very few useful examples in the lectures, leaving many concepts to be solidified in the homework. The homeworks in this section are overall much more difficult than the homeworks in the statistics section. The difficulty level of individual problems in these homeworks is all over the place. Questions about proofs tended to be more difficult, of which there were many. The lectures in the statistics section were better, though it seemed these lectures were done all at once in a single take (also true for the probabiltiy section). The professor would often fumble over words and ramble which was distracting. However, her perspective on simulation-based statistics was very informative and compelling, making this section of the course more engaging. The homeworks in this section are much easier. Once you learn how to use Statkey, the simulations are all just variations of the same process. The theoretical statistics portion is nicely summarized in a doc with all the formulas and methods for every problem and more. The big issue with this section of the course was how unorganized the materials were. Information was spread across up to 10s of PDFs and multiple paragraphs of text across EdX. However, if you do take the time early on to absorb all of that information, the rest of the course will take minimal effort. Quizzes and exams in this class were easier than the homeworks. The material on the quizzes were well-defined, and reviews for the exams were given so there were no surprises. In general, everything in this class is machine-graded, but on the higher stakes assessments the questions are often multiple parts building on each other so that you can get some form of partial credit. The expectations on how to answer the various types of questions were exceptionally clear, and I had no issues with the machine-grading in this course. Piazza in this course was not super helpful. The responsiveness of the instruction staff was mixed, though when it comes to logistics you can expect an answer in reasonable time. Several homeworks in the statistics portion of the course encouraged discussion on Piazza. Unfortunately, the quality of this discussion was low and it just crowded the actual questions out on Piazza. In conclusion, this course felt like several undergrad introductory probability and statistics courses combined together to make an average class that covered a lot of material, but none of it really in-depth. The expectations of the course are well-defined and assignments are structured in a normal way making succeeding in this class very possible.

Pros: 1. Plenty of drop homeworks and quizzes 2. Very manageable workload, particularly during simulation 3. Cons: 1. Math can be tricky if out of school for awhile 2. 3. Detailed Review: This was my first formal course work since completely undergrad nearly 9 years ago. I came from a chemical engineering background with no formal coursework in probability and only an applied stats course in undergrad. Overall I thought it was a great course. The probability section could be pretty tough at times though. However I felt like I am now much more comfortable with that subject matter now and feel more confident when seeing it in other courses or in textbooks/literature. You need to be pretty comfortable with calculus (up to multivariable level) to complete the homework, quizzes, and exams. Definitely worth brushing up on that if it's been awhile since taking it. It also worth brushing up on some math notation like sets, logic, and proofs prior to taking this course, otherwise it will be difficult following the lectures in the probability sections. I recommend Book of Proof by Richard Hammack, which is freely available online. If you have both of these covered, you will likely be able to follow along the lectures and complete the homework without too many issues. The homework, particularly the probability portions, can be challenging and take up a good deal of time. The work load does seem to ramp down as the semester progresses though. Some of the quizzes were pretty tough as well. I felt the two exams weren't too bad, but I know it did give some people some issues. The simulation/stats sections were much easier than the probability sections. However the material present in these sections was still great and useful. The way the professor explains doing statistical test makes it much easier to understand what is really going on when generating things like confidence intervals, t-test, and p-values. That all seemed like blackboxes when learning it in undergrad. Like someone else mentioned, don't let the reviews on this site mislead you too much. Nearly one-quarter of my class either dropped or got less than a B- (meaning they will have to retake the course and are likely on academic probation). But if you invest some time brushing up on calculus and mathematical notation ahead of time you'll likely do well in this course.

Pros: 1. Statistical coverage was interesting and not difficult to grasp 2. Exam was open notes, open book 3. Cons: 1. What application I can pull from this to future courses feels thin 2. Any application from this course to projects or future work is also thin 3. It's a math class. Detailed Review: I felt the first half focused on mathematical proofs with no application so it was difficult for me to try and piece together how I'd actually use the info later on. I've taken a graduate-level probability course before for my last master's and it was for engineers so much more application-based. I feel like this course would benefit if it were taught by someone with a different background. I ended up getting a good grade for the course but didn't leave feeling like I learned much- there was a lot of struggle to stay interested in the first half. I'm not a math proofs for the sake of proofs person. An application-based course would have been more useful. Sometimes I found myself solving a problem by following the steps but without much comprehension. I end up with the "right" answer but wouldn't have been able to explain it to anyone- which tells me I wasn't learning.

Pros: 1. The stats part is actually really interesting and easy 2. There are workload breaks during the semester 3. The exams are very fair and not as hard as the homework Cons: 1. If you're not fond of theoretical math, you're gonna have a tough time 2. If you're a CS guy, you're gonna spend a lot of hours on homework 3. It's obvious the professors have never stood in front of a camera Detailed Review: I frequently hear from my peers who are math type people how easy this class is and it's an excellent first class to get you back into the groove of going to school. I'm not sure if it just comes much easier to them or if it was actually much easier the semester they took it. But it sure wasn't for me. At the time of writing, I am taking data structures and data visualization, and this class was a greater workload for me than these two classes combined. You are allowed a few quizzes and homework assignments to be dropped. USE THEM FOR PROBABILITY. The degree to which the difficulty of the probability assignments is greater than the stats assignments is hard to describe. You will regret saving your drops for later in the semester when it is nearly all stats assignments. Space them out over the probability assignments to give yourself a mental break. You're gonna get 100s on all the stats assignments anyway. Probability Sections: If you're a very practical guy like me and not interested in the heavy theory and proofs, here's my advice: Skip all lectures over proofs. Dr. Muller's lectures follow a pattern: introduce a new idea, use a proof to show why it works, then apply it. Do yourself a favor and just skip to the point where he actually applies it. After struggling for hours trying to understand each new concept, I found this helped me to understand it much better than trying to decode the proofs. Proofs that I might add did not end up on the exams anyway. For the homework, you may have to reference them or perhaps make friends with some of your peers that got a stats degree in undergrad to help you out. But I did better in the class after ignoring the proofs because they ironically confused me more. Despite all of this, Dr. Muller is actually a pretty funny guy and cool personality. I think he's just maybe a little too smart for teaching an introductory class; his expectations are quite high. Stats Sections: Dr. Parker is very nice and sincere and you can tell she really does know the topic well. There are points in her lectures where she talks a little about the theory and the "why" of statistical inference and I actually thought it was quite enlightening. While a lot of this class in general felt a bit flat, the several times I had a light bulb moment that made me say "oh, wow; that makes perfect sense," all took place during her lectures. I think this class really should have just been the stats portions only and simply gone deeper. She does not require any programming and instead encourages you to use a free, web-based stats tool. I have to admit that I was skeptical about it, but I was quickly won over. Even as a CS guy, I loved that thing. It was very easy to use and let me do all the work without fussing with learning weird or new syntax. It really allowed me to focus on the concepts instead. The only downside to Dr. Parker's lectures is that she was perhaps nervous at some points. Again, I'm not sure they have ever been in front of a camera. I don't mind too much personally, but there are times where I can't help but think that maybe they should've just recorded that again. Some of the other students did mention that they found it distracting and had a harder time staying focused on the content, however.

Pros: 1. generally not too difficult 2. a reasonable first class to take in the program 3. Cons: 1. class material is divided and taught by two different teachers, with varying results 2. 3. Detailed Review: The first portion of this class isn't BAD, but it is probably what most students struggle with in this course: getting into probability, combinatorics, throwing in some calculus, etc. The first couple of homeworks are some of the hardest of the semester, but assignments get easier (and more interesting) once you're in the second half of the semester and working on simulation rather than probability. I think the general concensus is that the second teacher (who covers simulation) generally does a better job and makes the material more interesting than the first teacher (who covers probability). The first teacher isn't responsive to questions on piazza and his material is pretty dry, but still workable. The second teacher is much more responsive on piazza and was a generally involved teacher who was open to discussion or questions. If you already have a stats background, a good portion of this class will be review for you. If you're totally new to this material, you'll have to work more, but it's entirely reasonable for any person to get an A in this class with enough effort. Depending on your familiarity with the material, you could probably spend anywhere from 5-15 hours/week on this course to completely cover all the material and do assignments.

Pros: 1. The simulation based approach to statistics was very interesting and practical 2. The midterms and quizzes were fair and the homework problems and lectures prepared me well for the tests 3. The course materials were relatively organized and my questions were answered quickly on Piazza Cons: 1. The probability portion of the class has a lot of room for improvement 2. The probability lectures often didn't prepare me to solve the probability problems on the homework assignments 3. There were more proof problems than I would have liked Detailed Review: This course was divided into a probability section taught by Professor Mueller and a statistics section taught by Professor Parker. Overall, I thought the statistics portion of the course was excellent but the probability section needs to be improved. I loved the simulation-based approach to statistics and the decision to teach this material with Statkey. I have reviewed this fundamental statistics material several times over the last few years but this simulation-based approach helped me see the "big picture" and I am now much more confident about using these methods in my work. In contrast, while the fundamental topics of the probability section were very interesting and useful, there were many other topics that were only briefly mentioned in the lectures (or appeared in the homework with no appearance in the lectures) and were neither explained well enough to fully understand nor motivated well enough to appreciate why they might be useful. I would have liked the probability section to have focused on a subset of the most fundamental material and spent more time on the applications of that material, rather than on theoretical proofs and auxiliary topics which I will likely never revisit. The quality of the instruction in the statistics section was significantly higher than in the probability section. There were many typos in the lecture slides for the probability section which negatively impacted my ability to understand the material, especially since some of the typos were not acknowledged by Professor Mueller in the video lectures. Also, the lectures for the probability section frequently failed to prepare me to solve the problems on the corresponding homework assignments. Both professors were very likeable and I believe they both want their students to succeed, but Professor Parker was a much more effective instructor. Her video lectures did a great job of explaining the material and I always felt prepared to complete the corresponding homework assignments with little to no external references. There were a few typos in her course materials as well but much less than in the probability section. Professor Parker was also very active on Piazza and made sure to answer student questions in a timely manner. I am very pleased with Professor Parker's instruction but I believe the probability section should be improved. I did not interact with the TAs but I saw that they were active on Piazza and regularly answered student questions, which was helpful for me as well. To help put my review in perspective, I received an A in the course.

Pros: 1. Probability parts well organized and delivered 2. Statistics content is good in that it explains the tradeoffs between theoretical & simulation approaches 3. Teaching staff highly engaged in piazza 4. Student community was great Cons: 1. Statistics parts could be organized better 2. Some administrative aspects still needed to be ironed out (mainly around grading). Given the responsiveness of the staff, this was not a big issue. 3. Detailed Review: Both Mary & Peter seemed quite invested in making the course successful. Peter's delivery was more structured and easy to follow (if you have the mathematical background). Mary's style was harder to follow IMO, but you could still tell that she has good insights that you should pay attention to.

Pros: 1. Broad and complete enough 2. Good refresher on math 3. Focused more on application than theory Cons: 1. Felt like 2 classes were compressed into one 2. Could have done with a little more focus on the details of building a simulation 3. Detailed Review: If you are fresh from an undergrad with a stat portion this might seem a bit easy, but it's been a few years since I've had to do calculus, formulate a mathematical proof or even read mathematical notation. Others have said it's like an undergrad course, but it's definitely more like a refresher, covering a lot of ground in probability in 5 or 6 weeks. I hear that later courses will assume familiarity with the math (and obviously the probability concepts) so I was glad of the reintroduction. I found the stats portion to be valuable - I've done some simulation before, but didn't quite recognize how powerful it could be. It could be compressed a little though, with maybe a little more depth given to the "how" of simulation, e.g. different methods of randomization, and there could probably have been a note on modelling multi-stage processes with monte carlo simulation (as simple as that might be). However, many in the class seemed to have trouble with this portion, so maybe it's not quite as straightforward as I thought it was. Overall, this is definitely the best class to start with. I thought there was a strong learning curve but looking back it seems much simpler than I found it at the time, which I think reflects well on the lecturers.

It's very good organized course that touches all concepts in probability including discreet and continuous random variables, and import statistics and how to calculate them for various types of RVs. I think that the inference part of the course could be organized better.

Pros: 1. Piazza discussion was excellent. Professors are very responsive and help students work through the thought process on HW problems. 2. Quizzes and tests were very fair and straight forward. 3. Workload is pretty light. Cons: 1. Typos in slides and on homeworks. 2. Plenty of math proofs and notation, not a great deal of practical examples. 3. Multiple choice can be brutal, no partial credit. One wrong answer on a quiz and your quiz grade can tank. Detailed Review: Good first course to take in the program. Workload is reasonable. If you do not have a math background, expect to study the proofs and notation a bit more. Really no programming is required, just a few problems in R throughout the entire semester. I recommend getting a study group together through discord or Piazza to discuss HW problems, really helps. Probability portion is much more difficult than the statistics proportion of the course. The textbooks really are not necessary. Mitzenmacher book is almost useless, except they do pull home HW problems from that book so it can lead you to the answer in a few cases. Lock textbook is good, but not really necessary as everything you need can be found in lectures.

Pros: 1. Professors were very active on Piazza and encouraged students to discuss problem sets. 2. Reviews for midterms were made available to students. These combined with quizzes and homework meant the midterm was never a surprise. 3. Course is a great review/intro of probability and statistics that should set a strong foundation for continued success in the program. To be determined. Cons: 1. Statistics lectures didn't really add much on top of the provided notes and lacked many examples. 2. Sometimes, the hints harmed more than helped. I suggest attempting to work the problems before reading the hints. 3. Detailed Review: For what it's worth, I scored a 100 in the class, so I felt it was mostly easy. There was one quiz that I had a brain lapse, but it was dropped as part of their generous grading policy. I'm not sure what the distribution of grades was, but it felt like everyone else scored very high, too. This class is a great stepping stone from undergraduate classes to graduate classes. The professors are very responsive to both e-mail and Piazza and welcome questions. I never attended office hours, so I cannot speak to those or the TAs, but I did see a TA response on Piazza occasionally. Given the disparity of foundational knowledge in the student population, the professors have adapted the course content so that it meets the needs of all of the students. Enrichment opportunities exist if you find the entire experience droll. Assignments are all graded using a novel multiple choice scheme. This seemed to be a huge source of confusion for the students for a very long time. General rule of thumb: work the problem, get an answer, and then choose the number that is closest to your answer. Don't overthink it beyond this. I thought this method was a clever way of easily grading hundreds of assignments while keeping the students from working backwards from the solutions. The exception is the proof-based questions on the homework. Good news for future generations, we probably found most of the bugs, and they will already be corrected for you. Textbooks were useful if you knew how to use them. The probability text was less useful, but it did have some information that helped with a few homework assignments, but you likely could find this information in other probability textbooks. The statistics textbook helped me a lot with understanding some statistics principles that weren't thoroughly covered in the lecture/notes. As a supplementary textbook, I often referred to Statistical Inference by Casella and Berger.

Pros: 1. 2. 3. Cons: 1. 2. 3. Detailed Review:

Pros: 1. Workload is pretty light 2. Professors are very active on Piazza and will readily give their time to help you 3. Requires little programming for those with minimal coding experience Cons: 1. Typos found almost every week on lecture slides and homework 2. 3. Detailed Review: Professors were very nice and communicated actively on Piazza to help students understand. It will depend on your background how difficult this course is for you; for those with a math/stats background fresh in their mind, this will be mostly review and not that difficult. Probability section is much more difficult than the stats section. Problems are all problem sets/proofs, no real practical applications. Textbook is available for free if you google Z library and download it there. Was able to do most of the course without looking at the textbook, was helpful for some probability homework problems, as the full proofs were given in the textbook.

Pros: 1. This is a good first class to take. 2. Requires some calculus/algebra skills, but if you're a bit rusty like I was (e.g. haven't solved a double integral in ~7 years) it is a good class to brush up and become fluent with the math. 3. Professors were very helpful and active 4. Material is interesting, both the theory and application. Cons: 1. Few minor mistakes/typos in the material - but this didn't really cause any problems. Detailed Review: My back ground as undergrad is computer engineering and biology. I work professionally as software engineer. This was the only class I took this semester, and the first class I took in the program. I was working full time, so I wanted to test the waters with this class. In undergrad I took calc based prob/stats but the material was very dry - it was mostly just proofs and theorems (which I mostly forgot by now), and I never really felt super good about applying it in real problems. However, after this semester I feel very good and confident about the material and applying it. I found that I could usually get the homework and lectures for the week done in 1 day on the weekend, it would usually take about 7 hours or so (sometimes more sometimes less). We were able to drop the lowest 2 homeworks and quizzes so getting an A is very attainable. I didn't actually use R or python very often, I mostly just used a spreadsheet+statkey, which it worked well for all the exams/quizzes/homeworks. You certainly can use R/python if you want but you don't need to. At the beginning of the class some of the math was a little challenging for me since I was rusty, but after some practice, by the end of the class I felt very good and confident about doing the math, and that it is not too hard. I'm really glad I was able to brush up on math this semester so that in the up coming classes I will be able to jump right in. Overall - loved the class!

Pros: 1. Great community of students who share their ideas and approach on Discord. 2. Flexible and responsive professors. 3. Reviews for the exam. Cons: 1. Learning philosophy relies on studying for exams and doing homeworks as opposed of doing something practical. No projects and no research. 2. Feels like undergrad class. 3. Detailed Review: If you come from a stats/math background this will probably be easy for you. I come from a computer engineering background and haven't really touched stats/calculus/theorem proofs in a while, so it was a bit difficult to catch up with some of the problems. The stats parts was easy for me but probability was a bit more difficult. The professors are experts and good but I wish they relied less on quizzes and midterms. The graduate courses that I've liked the most are about doing research or doing projects. Nevertheless, I did ok in the course with some effort which can be challenging if you are working full-time. I learned a lot from the other students and external resources. Don't get misled about the difficulty of the course by the previous reviews. Some students didn't pass this course. For some it was really easy, for others very difficult and for others like me it was average difficulty. It depends on your math background.

Pros: 1. Both professors were extremely responsive and helpful on Piazza. Went out of their way to make extra materials available to help with certain topics. They genuinely care about helping their students to learn the material and succeed. Very lenient in regards to dropping hw/quiz grades, allowing extra time for HWs due to the Texas Freeze, etc. 2. Workload is pretty light in general, depending on your prob/stats background. Homeworks took some time and gave problems that required more digging into, while the quiz/exam questions were more straight-forward. 3. No video proctoring required; these instructors trust their students to follow the honor code. 4. A lot of the material is review of undergrad prob/stats that is a prerequisite for this degree, so most students will find this to be an easy course to get started in the program. 5. Getting to do statistics via simulation was the most interesting part of the course for me, as I had only ever been taught the theoretical methods as an undergrad. On many problems, it was easy to write some code to simulate it, and the instructors were perfectly fine with us using that method. Cons: 1. The amount of errors in the homework problems and lecture videos was really excessive. I actually started waiting longer to start working on the homeworks each week so that other students would find all the errors before I wasted my time on them. Some of this will probably get cleaned up for future semesters. But I feel like if you make errors in a lecture video, you should at least take the time to edit or re-record that part of the video. Made it feel pretty amateurish and not like I was taking a course in a top graduate program. 2. Multiple choice is a terrible grading method for a graduate course. I understand there are limitations due to the number of people taking these classes, so they all use multiple choice and/or peer grading.. but it would be a huge improvement if UT would cough up a little money to hire some graders. 3. StatKey is a neat tool that is really easy to use, but I felt like a lot of the stats part of the course was "learn to use StatKey", and it would've been better if we had some projects where we coded simulations ourselves in more widely used languages like Python or R. At the end of the class, we can do ANOVA simulations, etc. by just clicking a couple buttons in StatKey but I think most students would have no idea how to do them without StatKey.

Pros: 1. Professors active on discussion forums 2. Quizzes and tests are very similar to problems in HW/lectures 3. Material is interesting Cons: 1. A fair number of typos in the lecture slides occasionally makes things difficult to understand 2. Some homework problems quite challenging - needed to go to discussion forums for guidance 3. Textbook listed as required, only helpful for 4 or 5 homework problems Detailed Review: Class split about 50/50 between statistics and probability. Probability much harder IMO, though still manageable. Stats were a few weeks on intro stats concepts and a few weeks on approaching hypothesis tests and confidence intervals by simulation, which was the best part of the class for me.

Pros: 1. Absolutely outstanding professors that put an impressive amount of effort into engaging and communicating with students 2. Small workload for working professionals 3. Gentle introduction to the degree for people without strong probability and statistics backgrounds Cons: 1. The course has an undergraduate scope of content 2. No programming required 3. Detailed Review: My undergraduate background is in mathematics and statistics. I'm enrolled in Probability and Machine Learning while working full time. People with strong stats and math background will find this course very simple. This course seems like it was geared more towards getting people who have been out of school back into academia rather than building onto the pre-requisite knowledge of probability and statistics. The flip side of this means students without stats backgrounds will be gently introduced into the MSDS program. Students are encouraged to use programming environments explore different ways of tackling problems outside of the methods discussed in the lectures. With that said, no programming is actually required for the course. Students can explore solutions or simulations using Python, R, or whatever they feel comfortable with. Sometimes the professors will hand out snippets of R code for student use. They expect students to use a free web applet called StatKey to answer simulation-related questions. Lectures are accompanied with PDF uploads of the presented slides as well as any supplemental information. People with strong math and stats backgrounds can skip the lectures or reading the textbook altogether - really. The course consists of bi-weekly quizzes, two midterms, and weekly homework assignments. The quizzes and midterms are typically closely related with homework assignments. No curve-balls as far as testing content is concerned. All of this content is graded in EdX multiple-choice style questions. This means grades are all-or-nothing for each question, so be sure you check you entered the correct response before moving on to the next question. The dedication both professors have to communicating with students is outstanding. Both professors will respond very quickly to any question or concern in private e-mails or the Piazza discussion board. The Piazza discussion board is very active with thoughtful discussions and guidance between students and professors. I really cannot overstate just how strongly these two professors wish for their students to succeed. Dr. Mueller put the same emphasis on his students in his undergraduate courses, and it's so nice to see this has extended to an online program. These academics truly want their students to excel. I didn't use the textbook or attend office hours, so I'm not personally capable of commenting on their usefulness in the course. The time requirement for this course is going to be heavily dependent on your background. Those with statistics backgrounds could easily spend an hour or less a week to work on assignments or quizzes, but 10 hours does sound like a reasonable workload for those using this course as a refresher. The gentle introduction this course provides makes it an obvious first-choice for students new to the program that wish to slowly roll into the rest of the program. Students that are well versed in the topics may want to wait to pair this with more difficult or time consuming classes. Chances are you won't learn anything new in this class if you've taken undergraduate statistics classes.

Pros: 1. Very responsive professors 2. Good coursework material with content that allows for collaboration and discussion in addition to the HW/quizzes/exams 3. Reasonable refresher/introduction back to probability and statistics Cons: 1. Could be too easy for those with experience in the subject already 2. First semester had issues with typos (but the professors addressed issues quickly) 3. Detailed Review: A quick rundown / refresher on probability and statistics. Those who had a firm grasp of the subjects from undergraduates will find this course easy. For those who have been working for a few years and may be a bit rusty in math, it would be good to brush up on some basic calculus either before or during. There is flexibility in what programming language you choose to use (none of the HW required turning in code, just interpreting results). For those who are working full time and want a non-stressful start going back to school, this is a good starting class. For those who are trying to graduate as fast as possible and have solid foundations in math already, this is a class that could be taken in addition to another course. Workload estimate is from perspective of someone who has been out of school for a few years, but comes from an undergraduate engineering background.

Pros: 1. Homework 100% prepares you for tests and quizzes. 2. Professors/TAs are very active on Piazza. 3. The coding work in R is straightforward. Cons: 1. Plenty of typos in the slide deck. 2. A few concepts have no examples in the lectures to work before jumping in on the homework. 3. The textbook is a waste of money. Detailed Review: This was my first course and first every graduate level course and I was pleasantly surprised. The early homeworks take plenty of effort and I averaged about 7hrs a week on them. The quizzes and tests are very chill. If you got a 100 on your homeworks and understand the problems you'll finish ur quizzes and test with over half the time to spare. If you don't like the explanation of a certain concept there is an extreme amount of material out there to help you out.