COMPTNG 16A
Python with Applications I
Description: (Formerly numbered 16.) Lecture, three hours; discussion, two hours. Requisites: course 10A, Computer Science 31, or equivalent, with grades of C- or better. In-depth introduction to Python programming language for students who have already taken beginning programming course in strongly typed, compiled language (C++, C, or Fortran). Core Python language constructs, applications, text processing, data visualization, interaction with spreadsheets and SQL databases, and creation of graphical user interfaces. P/NP or letter grading.
Units: 5.0
Units: 5.0
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Most Helpful Review
Fall 2024 - The homework wasn’t too bad but the professor is very confusing. The TA was amazing and very helpful. The professor honestly was very type a personality, disagreeable. She wizzed through everything in class. The lectures were really boring and she expected you to know everything ahead of time despite being an introductory class. I really would not recommend this class.
Fall 2024 - The homework wasn’t too bad but the professor is very confusing. The TA was amazing and very helpful. The professor honestly was very type a personality, disagreeable. She wizzed through everything in class. The lectures were really boring and she expected you to know everything ahead of time despite being an introductory class. I really would not recommend this class.
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Most Helpful Review
Fall 2020 - If you are deciding between professors for PIC16A, Phil should be your choice. He cares so much about his students - both in whether you're grasping concepts and actually learning and in your mental health. His emphasis was not on grades but more on actually learning the material - he drops your 2 lowest homework grades, 4 discussion grades (which you complete with the same group of one to two people through the whole quarter), and 5 quiz grades. So if you're not doing too hot one week, don't stress. I came into the course not too confident in my coding skills (I only took one PIC class before this) but I had all the resources I needed to get through this class. Challenging, but not to the point of insane stress. I really liked the final project we did surrounding data science and machine learning - you complete this project by week 10 and is done with the same group from your discussion. I like that the project was based on the students' interests that were surveyed at the beginning of the quarter. It seemed like a lot when we were first introduced to it but a lot of the work for the project is done in discussion and through homework already. Campuswire was a huge helper for me in the class to ask questions about homework or other general questions. You could receive up to 2% extra credit from posting thoughtful questions and answers on there, and up to 3% through a short essay surrounding equity, justice, and algorithms. Phil was always available and open for questions. He held office hours at normal hours and also later in the evening to accommodate for students in different time zones. If you emailed him he'd reply quite quickly and always obviously took time to respond thoughtfully. He also checked CampusWire frequently for questions to answer that other students couldn't answer. The midterm and final did take much longer for me than the estimated time given, but I feel this is common for all 24hr tests at this point. He even emailed us after the final saying that he has heard this concern and would take that into consideration while grading (what a gem), and had a whole extra part on the final to explain your progress through this class as a way to give him more insight into you when he considers your final grade. If you do take this class with Phil (which you should if you want to learn in a really engaging way), make sure to ask him for his memes.
Fall 2020 - If you are deciding between professors for PIC16A, Phil should be your choice. He cares so much about his students - both in whether you're grasping concepts and actually learning and in your mental health. His emphasis was not on grades but more on actually learning the material - he drops your 2 lowest homework grades, 4 discussion grades (which you complete with the same group of one to two people through the whole quarter), and 5 quiz grades. So if you're not doing too hot one week, don't stress. I came into the course not too confident in my coding skills (I only took one PIC class before this) but I had all the resources I needed to get through this class. Challenging, but not to the point of insane stress. I really liked the final project we did surrounding data science and machine learning - you complete this project by week 10 and is done with the same group from your discussion. I like that the project was based on the students' interests that were surveyed at the beginning of the quarter. It seemed like a lot when we were first introduced to it but a lot of the work for the project is done in discussion and through homework already. Campuswire was a huge helper for me in the class to ask questions about homework or other general questions. You could receive up to 2% extra credit from posting thoughtful questions and answers on there, and up to 3% through a short essay surrounding equity, justice, and algorithms. Phil was always available and open for questions. He held office hours at normal hours and also later in the evening to accommodate for students in different time zones. If you emailed him he'd reply quite quickly and always obviously took time to respond thoughtfully. He also checked CampusWire frequently for questions to answer that other students couldn't answer. The midterm and final did take much longer for me than the estimated time given, but I feel this is common for all 24hr tests at this point. He even emailed us after the final saying that he has heard this concern and would take that into consideration while grading (what a gem), and had a whole extra part on the final to explain your progress through this class as a way to give him more insight into you when he considers your final grade. If you do take this class with Phil (which you should if you want to learn in a really engaging way), make sure to ask him for his memes.
Most Helpful Review
Winter 2018 - Professor Haberland is an amazing professor. He makes you do a lot of the work before class with his flipped lecture style, but the material is extremely interesting and useful. He covers a lot of ground (so much that he splits the course into two tracks and you can choose one). If you do both tracks, you learn an unbelievable amount. Additionally, he writes extremely engaging exams that make you think and solve interesting problems. Probably the best PIC professor out there.
Winter 2018 - Professor Haberland is an amazing professor. He makes you do a lot of the work before class with his flipped lecture style, but the material is extremely interesting and useful. He covers a lot of ground (so much that he splits the course into two tracks and you can choose one). If you do both tracks, you learn an unbelievable amount. Additionally, he writes extremely engaging exams that make you think and solve interesting problems. Probably the best PIC professor out there.
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Most Helpful Review
Fall 2019 - Professor Ji isn't the most engaging lecturer but she gives you all the materials you need to do well in this class. The homeworks are very helpful and pretty interesting to do. My only complaint with this class is that there are a lot of different library functions and function parameters to memorize, which can be difficult to recall on the tests, which might ask you to use specific functions in certain libraries. Professor Ji is very good at answering questions and really wants everyone to succeed in the class. I definitely recommend this class.
Fall 2019 - Professor Ji isn't the most engaging lecturer but she gives you all the materials you need to do well in this class. The homeworks are very helpful and pretty interesting to do. My only complaint with this class is that there are a lot of different library functions and function parameters to memorize, which can be difficult to recall on the tests, which might ask you to use specific functions in certain libraries. Professor Ji is very good at answering questions and really wants everyone to succeed in the class. I definitely recommend this class.
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.
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