Stats 100C is one of the most important classes you will take as a Stats major. The theory behind linear regression is very important and sets you up well for doing statistical analysis and working with data. That being said, Mike placed emphasis on both the theoretical and applied portion, making sure that we knew about why the theory and "hard" math are important for the applied portions. Think about it as "why Stats 101A works." Mike did an amazing job explaining these concepts to us, through very thorough and well-paced lecturing, copious amounts of office hours, and well-rounded exams. Mike's virtue of valuing learning over a grade really reflected in my performance, and it was especially necessary for this class. I wouldn’t have it any other way.

*online version*
Known as possibly the hardest stats class offered, Mike made 100C very easy to follow and learn, even for his first time. He made clean lecture slides that explained the topics well, and the homework assignments built upon what was taught in the (prerecorded) lectures. The class was graded on homework (30%), midterm (30%), and final (40%), but for this quarter, he made the final no-harm/optional. He offered office hours both during regular times and during class times, and was extremely helpful with questions, though he enjoys helping students come to the correct answers themselves. The tests were fair, as they were similar to the homework, but expanded on different concepts.

Professor Tsiang and Jake reformed the Stats 102A class to be more rigorous and engaging. We created a battleship project with a custom AI from scratch and even made a package in R (a combination of all our homeworks). I learned a lot in advanced R programming and am thankful for the hours the Professor and Jake put in to clarify any confusion I had. Tough class but learned valuable concepts in writing clear, concise, and effective code

I took this class in summer 2019, the slides he used was very clear and easy, but the homework was really unfairly hard and the tests were tough. It's impossible for a student who just finished stats 20 to write two games and other homework like that. I think the TA JAKE made up the homework, he just wanted to show how talented he is. Avoid this professor and the TA JAKE. If you can, just change the major.

Don't take Stats 20 with Professor Tsiang who always has Jake Cramer as TA!!! I dropped this course on Week 2. The workload was unreasonablly large, and all homeworks and exams were very difficult. Strategies for problems on weekly homework were not taught in class, and they needed great amounts of time.

Michael Tsiang provides notes that are very clear regarding R syntax and gives a good overview of R in general, and you should be able to do some simple data projects on your own after this class. His lectures are essentially going through the notes and code on RStudio and explaining slightly more in depth, but not by much. The homework was manageable (medium to hard difficulty), but they are graded based on optimization as well--you will get docked points if your code doesn't solve the problem in the quickest way, which is not something considered in CS 31 or 32. Tsiang was more helpful when I asked him questions in person.

On the negative side, he was slightly passive aggressive in class. He threatened to tank everyone's grades when someone took a photo of his slides during lecture against his wishes. The exams are difficult in that you have to be extremely familiar with the output formats of different inputs, which I don't think is very relevant to real-day usage where you can tinker around with code and see the output, but might be relevant to coding interviews. Most people found the exams hard, and I scored a B to B+ on both midterms and the final and got an A in the class, so it was definitely curved.

Overall, it's a good overview of R, but the exams and instructor are slightly off-putting. It'll be helpful to work through the problems and study with peers.

Note: This review is for Winter 2021, a quarter conducted remotely for this class.

I took STATS 199 with Professor Tsiang because I enjoyed his STATS 20 class and him as a professor and person. He let me take the lead for my directed research--I did mine on fantasy football analysis (in fact, he didn't know much about the topic, just the analysis I was performing). In terms of what is required, the only thing I had to submit was a final research paper.

Professor Tsiang allowed me to meet with him on my schedule. I could meet as often (or as little) as I needed, and he helped me with a few questions I had about the process. I met on a weekly basis (for the most part) to give him updates throughout the quarter.

TL;DR: Professor Tsiang let me run the course; he served as a mentor as I worked on my research. Ultimately, this course is about the student more so than the professor.

If you're here to find an easy class, go find another class lol, this will definitely take some chunk of your week to do the hw. I'll be honest, I like Michael and wow surprisingly unlike the reviewers here, Jake too. I took this when crap hit the fan with COVID. Class wasn't too hard, unfortunately I wasn't as focused as I'd like to be because you really do learn a lot in this class. If you engage with Jake or Michael during office hours or through questions on discussion boards, they will remember you! Michael's notes are very good and R has swirl which is an interactive command line platform that you go through each assigned chapter that Michael has designed. Our final project was made optional because of how hectic everything is and I am very grateful. I will say the best way to study for this class is to just plug in random crap into R and predict what the outcome is. You don't get to have R by your side during an exam and Jake expects you nail the basics. Having some computer programming experience is definitely preferred because how everything in R is stored is probably harder unless you understand that(although you'd just have to put in the effort to read more if you dont have experience). Miles sometimes likes to be cryptic with his answers because he wants you to learn(that definitely frustrated me a lot), but as long as you show that you're trying, he'll "tell" you the answer if he sees you struggling a lot.

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.