Though I learned a lot in this class, I really did not enjoy it. Here's my summary of this class:
• R is a really slow and not commonly-used language. If you're interested in data science, learn Python and take classes in the CS department instead.
• Professor Tsiang is a nice, helpful professor, but he has some strange disdain for his students that I was never able to quite pinpoint. He strikes me as a professor that loved being a college student and became a professor because that was the closest thing to it.
• The professor really prioritizes student mental health and well-being, which is tremendously refreshing.
• This class is more graded on effort than correctness, so it is relatively easy to get a good grade as long as you try on the assignment and study for the exams.
• His lecture notes are the most wonderful thing to ever be created. They are clean, organized, extremely thorough, and I'd even say sexy. ;)

For some reason, I never clicked with this professor. I tried many times to try and connect with him in class, yet our conversations were always somehow awkward and ended on a weird note. The professor strikes me as someone who really enjoyed being a college student and became a professor because it was the closest thing to it. I feel like he had some sort of disdain for me and something about him always through me off, though I’m not sure what.

However, I felt that the professor was very concerned for his students and always put in an effort to help us learn and master R. He always prioritized our mental health, which I really appreciated. The focus on putting our health and relationships before our academics was very refreshing and something I really appreciated throughout the quarter.

In terms of the structure of the class, I thought that it could’ve been better. I hated R during the first half of the quarter because it seemed very inefficient, slow, and useless. Only toward the end when we started to import data files and make pretty graphs did I see the usefulness of R. It would’ve been nice to see that at the beginning of the quarter so that I would’ve had more motivation to suffer through in the beginning. He also gave really cryptic answers to my questions about the homework, which I did not appreciate. Also, the professor is really picky about using the Tidyverse style guide, which I really hated. THE CURLY BRACES SHOULD GO ON THE NEXT LINE!

I absolutely loved his lecture notes, they were extremely thorough, well put-together, and concise. I stopped going to lecture because he would simply read off the lecture notes and code some things, so there was no point in me going in person. I thought the BruinLearn was chaotic and very disorganized, I always had trouble finding what I needed to there. Materials just seemed to be dumped online, without regard to organizing them in any logical way. How ironic!

He taught the material really well and I definitely feel like I improved in coding in R. But it is difficult with the Homeworks being very long meaning that you have to work ahead on them on the weekends a lot of the time. He curves the tests very generously so you don't have to worry about those. Homeworks are graded on effort/completion, so your grade in the class will be good if you put in a decent effort into learning the material.

Be careful if you have any prior programming experience. He grades you down a lot if you use any functions we haven't learned yet on any homework or exam. We don't learn flow control until the second half of the course so if you have programming experience it can be annoying to avoid using them.

This class actually kinda kicked my ass. Everything seemed straightforward and then wasn't. I walked out of the first midterm thinking I couldn't have gotten anything wrong but I got a C. Lectures are basically useless, and the TAs that we had at least SUCK. Labs and HW are easy, it was really the midterms and finals.

But Tsiang is a really cool dude and obviously cares. I should have gotten a B- and he bumped me up to an A-

Instead of trying to help you out, Tsiang takes the approach of making you feel stupid until youre computer gets fried because of the tears seeping through your keyboard

I really enjoyed taking this class with Tsiang. He explains all the material very clearly and puts in a lot of effort into teaching. Stats is not the most interesting subject (at least to me) but he makes his lectures as engaging as possible. He was also super helpful during office hours and was willing to spend lots of time with students to make sure they understand a concept. In addition, his exams were completely fair and by no means challenging as long as you put in a little bit of effort into studying. He even let us use a cheat sheet which was really helpful.
If you're hesitant about taking this class with Tsiang because of the reviews - don't be! I don't understand why people are upset with his teaching. I thought he went above and beyond to help students and is in general a super nice person. Would definitely recommend this class with him.

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.

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.

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.

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