Professor
Leah Keating
1 of 1
Most Helpful Review
Fall 2023 - 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.
Fall 2023 - 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.
Most Helpful Review
Fall 2024 - 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.
Fall 2024 - 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.
AD
1 of 1
