I love Mike omg he's one of my favorite professors in the stats dept. He really cares about his students and wants us to deeply understand the material that he's so passionate about (he literally said every method we learned in this class was his favorite lol). I also had him for Stats 20 in Fall 2019 (which is when the homework/tests changed although it was still a great class, most people just complained cause of grading and making you think critically in a different way imo). Mike's 102C is more on the theoretical side than Prof Miles Chen, but there is still programming in R for simulations and sampling. Def brush up on 100B as you'll need it. Mike is helpful in OH and on Campuswire, and we got 1% EC for Campuswire or attending discussions (grading scheme 40% HW 60% final). He gave us 48 hours on our take home final (no internet, only class notes) which was incredibly generous. His homework and exams are just extensions of his lecture slides. Lectures were prerecorded and during our "lecture time" it was basically OH. Would recommend taking a class with Mike if you can!

Look, I hear Stats 13 is the easiest pre-req for LS majors. I am usually an A student, and was expecting this class to be a breeze. But, due to online learning, Tsiang made his exams a lot harder than it was in person (he used past exams as 'practice tests' even though they were wayyyy easier than the actual exams.) Recorded lectures were pretty straight forward, and I thought that I was well-versed in the material. Fast forward to Exam 1, I got a 77% (class average was 78%) and I was shook. Most questions were short answers, so I got many partial credit, and only got 1 multiple choice answer wrong. For the final I got a 75% (have no idea what was the class average), but same deal. To be fair, the exams are open note that's why they are harder, but even then, I was surprised since I'd do very well on the homework. According to syllabus, I had an 85% in the class, but Tsiang adjusted the grading scale to fit distributions, so I ended up with an A-. I am not too upset, since I was expecting a B, but I wish I would have been prepared on how detailed I needed to be when it came to short answer questions. Also he bombarded us with lecture videos Week 5, and it was just a bit too fast-paced and it was frustrating. I had many peers feel the same way as well. But at the end of the day, I really liked Tsiang, he's a good professor and cares about his students, I just didn't have that much interaction with him due to chaotic home life and to be honest, laziness to attend office hours.

The practice midterms and exams are definitely not reflective of the actual midterm and final, which really threw me off since they were more difficult...almost ambiguously and confusingly difficult. Exams were hard and I get why Dr. Tsiang made it harder due to remote learning. Nothing can really prepare you for it since there's no "practice exams/midterms" for remote learning. Recorded lectures were long and he read from the slides but it was digestible and Dr. Tsiang just followed the textbook (that is not required!) I really appreciate Dr. Tsiang not mandating purchasing textbooks and materials for the course.

Aside from emails, the primary mode of correspondence for this quarter was a Discord server which I really didn't go on much except to clarify a few things that someone else already asked.

His love for Parks and Rec definitely permeates into the course, so if you're a fan, you'll appreciate the incorporation of the show into the work...if you're not, just nod and smile.

Labs are easy since the TA just uploads the video doing the lab step by step, question by question. No problems with that. Labs were probably the easiest thing to complete from this course.

This is a class many of y'all probably need to take, so just bite the bullet now and take it and hold on. Don't let go or fall off the wagon mid-quarter and don't let time slip away.

This was the worst class I took at UCLA. I changed it to P/NP because I literally thought I was gonna fail. The highest grade I got on an exam was 35%. Yes, in the end it was curved. But the stress/depression that this class caused me was not worth anything. The TA (I had Jake) was intimidating, not helpful whatsoever, and smug! The lectures were actually super clear and the notes were helpful so you'd think it would be straightforward! But it wasn't at all...it was like learning the ABCs in class then having to write your own code for ranked choice voting for homework. For an introduction to programming class, it was absurd. They said you didn't need any prior experience coding to excel in the class but this was not the case at all. Not to be dramatic but I wouldn't recommend this class to my worst enemy

This class is an intro to R but it did not feel like that after the first week. I felt like I should've showed up with coding experience even though it wasn't required. Grading scheme is made up of homework (15%), discussions (3%), quizzes (10%), midterms (20% each), Campuswire participation (2%) and a final (30%). The homework is graded on completion. Discussions got full credit even if you turned in a blank document. Quizzes were very easy if you read the notes. The midterms and the final were very difficult.

Many of the answers to the tests were based on knowing which tiny changes to code would change the output. The tests were handwritten and heavily weighted on the free response (writing your own code). A lot of what is on the tests is not explicitly taught in the course so doing well on the exams meant that you had to spend a LOT of time playing around in R and noting the intricacies of the program. Homework had easy, intermediate, and challenge questions. Again, to solve these, sometimes you just had to experiment a lot in R.

The concepts were challenging, I wanted to cry for much of the term, and I completely expected to get a lower grade. Overall, not a fun experience. Stats 20 will make you question your intelligence. Greatest part of the class is professor Tsiang's exam curves. If you want to do well, study the notes, ask the experienced students for help, and copy down niche outputs.

Mike is an amazing professor who really cares about your learning and mental/physical health, so don't stress too much about grades, as he curves. You still need to put in the work to do well, as homeworks are long and challenging. You can always ask for help on Campuswire and Mike or his TA will respond fairly quickly. Other than that, he is an engaging lecturer and a great person overall.

I took Stats 20 for the Social Data Science Minor. A few things to start are that this is NOT a stats class. It is strictly learning to program with R. I have taken multiple AP classes for Computer Science and I understand how to code at a beginner/intermediate level. The class consisted of weekly HW and quizzes. The quizzes were easy but the HW just didn't make sense. The HW was always extremely difficult and required a lot of time dedicated to complete these questions that didn't really mean anything since the HW is completion credit. The tests were alright and I received consist low 70s but finished the class with a B+. The curve is extremely forgiving and its very achievable to get an A. I just didn't really practice enough. Professor Tsiang is a great teacher and resource but I just wont get how the hw took so long and didn't really translate on the tests.

I don’t really think that’s an easy A class for people new to programing, but it is normal that learning a new skill can be difficult and time-consuming. Besides the class itself, Mike is an exceptional educator who places equal emphasis on both your academic progress and overall well-being. He ensures a balanced approach to learning, prioritizing comprehension over grades. Mike dedicates his time before exams to answer any questions and is always patient in responding to student queries. As a novice in programming, I was initially apprehensive, but Mike's lectures provided me with comfort and reassurance. In contrast to professors in my economics department who prioritize research and paper writing, Mike stands out as someone who truly prioritizes his students' learning experience.

Tsiang is probably one of the best professors I've had so far. The homework assignments are hard but graded based on completion rather than correctness. The exams are definitely easier than the homework assignments, and the exams consist of multiple choice and free response questions, and you definitely have enough time to finish them within the time limit they give. He curves a lot! I was expecting a B in the class but somehow got an A which was honestly a surprise. Lectures are recorded and he gives lecture notes (which are so useful). Also, discussion worksheets are useful for practicing problems (but I never went to discussion). Overall, the class was great, and you learn a lot about R language, highly recommend it. As long as you watch lecture/read lecture notes and do the homework and discussion problems with the best of your ability, you will do fine!

Mike Tsiang is such a nice guy. He really loves his job, and he treats Stats 20 like his child (he says he designed a lot of this class from the ground up apparently). Grade scheme was 15% homework, 10% quiz, 20% exam 1, 20% exam 2, 30% exam 3, 5% discussion attendance. Our TA said this class, outside of the physics department, has the highest curves of any ucla class he's seen. This is surprising since the averages on every exam was almost exactly 80%. Mike will tell send an email saying something like "if you are in the upper quartile, you are in the A+/A range," etc. so you always have a vague idea of where you stand in the class. I got an A in the class with an average of 83% across the 3 exams, and Mike posts the formula he uses to compute the curves, which is really considerate.
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Expect a lot of parks and rec references (like every example in class, on the exam questions, in his emails, in the syllabus) and Cameos he gets with some pretty neat guests (I love hank green).
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The homework is based mostly on completion. Don't use functions not covered in class and it's basically going to be full credit. Though the homework takes a LONG time and is way harder than any exam question.
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Exams were a mixture of multiple choice and free response questions where you are asked to write functions to complete a certain task. Partial credit is generous here. Multiple choice questions were often trick questions, designed to make you question your sanity (at least for me). They asked about all sorts of edge cases and the inner workings of R that you generally would never encounter in the real world, but are nice to know I guess.