I honestly felt that she was very dismissive. I felt very unsupported by her and my TA. I felt like I was treated as if I should have already known the material... except I thought this class was for people that have never coded before? Of course after lecture I would understand the concepts, but when it came to applying them to the homework, I often thought I needed a push or at least a similar example. When I would reach out, she would say to come to her in person instead of messaging her. However, there are only so many times I can call off from work or ask to switch shifts. When I would message her she would leave me on read for hours only to tell me again to go to office hours. Little did she know I was only able to attend my TA's office hours for the first few times.

Asking her about any code that she didn't write in her office hour will result in being told to ask the person who wrote it, not me. The person who only answered the code she written. The person who let you lose 5% of your total grade for computer compilation problems in one hw.

This was one of the most fun classes I've taken in UCLA! Although I've had plenty of prior experience in Python, I still gained a lot from this class, especially in reinforcing concepts that I did not fully grasp previously. As Professor Keating's lecture notes were well-written, I stopped going for lectures after Week 2. Homework and discussion assignments were extremely fun to work on, as they often involved real-world applications of Python. Midterm was really easy with most of us completing it within 20 minutes, so she made the final slightly harder, but it was still manageable. The only downside was the project - my group put in a lot of effort into it, but perhaps we focused too much on the code rather than the explanations, so we were rather harshly deducted 20 marks for insufficient explanations, so do be wary of that.

I was searching for data science internships while taking PIC 16A, and it has been extremely helpful in tackling Python interview questions. I cannot stress enough how important the split-apply-combine technique we learnt for Pandas turned out to be. Now that I have secured an internship, looking back in hindsight, this class has played a pivotal role in my success. For anyone looking to enter the field of Data Science, I would highly recommend this class with Professor Keating.

This was her first quarter teaching PIC 16A, but I felt like she handled the class decently! I stopped attending class after the midterm but her lecturing was okay, a bit unclear and not beneficial to my learning imo. Here's important points of the class:
- classes are NOT recorded but she posts lecture notes afterwards by the end of the day, so I never went to class and did fine (it's coding so really all you need is the syntax to do what you want, imo class isn't necessary)
- discussions aren't mandatory but turning in discussion notebooks during discussion gave us extra credit so i'd recommend going! my TA was also AMAZING and helped clarify a lot of topics so that's another benefit of discussion
- there's a group project at the end of the quarter (3 people in a group) and tbh it was pretty easy, you can honestly knock it all out within a day or two
- homework was weekly and ngl this was probably the TOUGHEST part of the course. the homework often felt WAY harder than what was covered in lecture, so DEFINITELY start early and attend office hours for that. they grade pretty generously but the homeworks can definitely not be grinded out in an hour or two. (in fact, the professor has said to allocate at least 5+ hours per homework -- which I feel is true).
- exams were easy since they were the typical pen-and-paper exam, just write down EVERYTHING on the cheat sheet and you'll be good to go (don't really have to study imo).

Overall, I liked PIC 16A much more than PIC 10A. The content was not overly difficult and felt more applicable. Professor Keating is a very nice person, and definitely cares about ensuring that her students understand everything.

I enjoyed this class a lot more than PIC 10A, as I felt the content was more applicable and the graders weren't as nitpicky on coding style. The first half covered most of what we did in 10A (except in Python, of course) while the second half emphasized visualization and data science/machine learning.

The class was structured in a way that I believe allowed students to absorb the material well--everything was relatively easy to follow, and students had the chance to practice most (if not all) of the material learned outside of lecture. It definitely did amount to the workload of a 5-unit class, so expect to still put in effort. Professor Keating was, in my opinion, the best coding professor I've ever had. She explained everything relatively in-depth and structured her lectures into well-organized Jupyter notebooks. Homeworks were often challenging, but she and the TAs were pretty active on Campuswire to help students out. Exams were not too difficult, but you still need to study to get an A.

The machine learning project was fun but time-consuming. I sort of had to rely on machine learning knowledge from other classes for certain parts, but it was manageable. It was interesting moving through everything from exploratory analysis to modeling, and it wasn't too high stakes (~15% of our grade). However, I was slightly disappointed that (at least for our quarter) she didn't show us our grade on the project after posting the final grade. Because of this, I can't really say anything specific about how strictly it was graded, but I can say that it was a great way for us to apply introductory machine learning topics.

DO NOT TAKE THIS CLASS IF YOU CAN AVOID HER!! otherwise you could survive but it will be very hard

She teaches this class like you're supposed to know the material even though it's supposed to be for beginners. She throws the terms and concepts out there in lecture without explaining well or at all. Sometimes it's like she doesn't even know her lessons and code (probably because she doesn't write her own code most of the time). She is dismissive and her attitude when writing tests can be described in one quote: "I knew no one would get full marks on this question when I wrote it". I don't think that's how tests are supposed to work... The only thing that saved me in this class was my TA, Ely Jrade. If he TAs, try to get in his section!!! He is so helpful and nice, so clutch.

This class was OK given that this is her first time teaching. The lectures were quite straightforward. There're 7 homework assignments worth 50% of your grade, and the lowest one was dropped. HWs were not long/hard, but since there were no autograders set up on Gradescope, it can be tricky when it comes to edge-case testing and might lead to some points deductions. Plus, there were a lot of typos in the HW prompts, which might cause some confusion (she borrowed HW questions from another professor). Exams also contained some tricky questions but were doable for the most parts.

Contrary to some of the other reviews, I really enjoyed taking this class with this professor. I'm currently taking PIC16A with her because I appreciated her way of teaching that much. Her lectures are efficient and understandable, and she posts the code in case you miss class. The homework can be difficult, but they are definitely doable and related to the class content. The exams definitely can be tricky but still fair and based off the content you learn in class. She also gives you a double-sided page of cheat sheet. I had a medical emergency this quarter, and she gave me a VERY gracious extension which was incredibly appreciated. I would take any other PIC class offered by her if I could.

Really fun course. We got a little behind by the end which made the Penguin Project a lot harder than it probably should have been.
The homework assignments are super fun and challenging but I did most of them in one sitting. She uses Jupyter Notebooks which has proven to be the best lecture mode for these kind of coding classes.

Good course and extremely applicable to real life problems, we frequently used real data sets to model. If you didn't like PIC 10A, try 16A if you need additional coding experience.