MATH 42
Introduction to Data-Driven Mathematical Modeling: Life, Universe, and Everything
Description: Lecture, three hours; discussion, one hour. Requisites: courses 31A, 31B, 32A, 32B, 33A, one statistics course from Statistics 10, 12, 13, one programming course from Computer Science 31, Program in Computing 10A, Statistics 20. Introduction to data-driven mathematical modeling combing data analysis with mechanistic modeling of phenomena from various applications. Topics include model formulation, data visualization, nondimensionalization and order-of-magnitude physics, introduction to discrete and continuous dynamical systems, and introduction to discrete and continuous stochastic models. Examples drawn from many fields and practice problems from Mathematical Contest in Modeling. P/NP or letter grading.
Units: 4.0
Units: 4.0
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Most Helpful Review
Fall 2020 - Professor Menz is pretty nice and seems to care about his students alot, but the class itself was a bit of a mess because there was literally no syllabus, which I think is the main issue of the class. Because of that, the grading scheme, outline of what material would be covered, etc. was not very clear. Lectures were also a bit all over the place. It was hard to follow along with the content being covered because of how he structured his notes, and I feel like I didn't actually learn much at the end of the class. Also, the lectures never really related that much with the homework/final project. Personally I didn't find the homework or project to be too difficult and thought the grading was pretty generous, but I do have experience with coding in Python and I think that helped alot. If you're a Data Theory major, I'd suggest taking Stats 20/21 first if you can if you're taking it with Menz, as I felt like the homeworks were alot easier to do in Jupyter Notebooks/R Markdown. Basically, if you're taking this class I'd maybe recommend going with someone else. But if you're stuck with Menz (as I think only one professor teaches this class each quarter), it's not the end of the world- just focus on doing well on the homeworks and project.
Fall 2020 - Professor Menz is pretty nice and seems to care about his students alot, but the class itself was a bit of a mess because there was literally no syllabus, which I think is the main issue of the class. Because of that, the grading scheme, outline of what material would be covered, etc. was not very clear. Lectures were also a bit all over the place. It was hard to follow along with the content being covered because of how he structured his notes, and I feel like I didn't actually learn much at the end of the class. Also, the lectures never really related that much with the homework/final project. Personally I didn't find the homework or project to be too difficult and thought the grading was pretty generous, but I do have experience with coding in Python and I think that helped alot. If you're a Data Theory major, I'd suggest taking Stats 20/21 first if you can if you're taking it with Menz, as I felt like the homeworks were alot easier to do in Jupyter Notebooks/R Markdown. Basically, if you're taking this class I'd maybe recommend going with someone else. But if you're stuck with Menz (as I think only one professor teaches this class each quarter), it's not the end of the world- just focus on doing well on the homeworks and project.
Most Helpful Review
Spring 2020 - Mason is one of my favorite professors in the math department. He rarely teaches undergrad courses, but he designed this course for the data theory major. There's a big focus on creating your own math models. This is much different than other lower div math--grading is on your process/assumptions and not necessarily getting the "right" answer. Mason has corny jokes and shirts and showed us his extensive collection of plushies which was awesome. Live lectures and discussions were clear and recorded, although his handwriting is hard to read (which he openly admits several times lol). He posts his notes from class as well. Both Mason and our TA (Gyu Eun is the best!!!) were very flexible about due dates--we had a 1 week grace period to turn in assignments and more if you just talked to them about your situation. They were especially accommodating at the end of the quarter with all the protests which was really nice. We had 2-3 weeks for a group project on one of the MCM problems with a 20 page write up but it wasn't too bad if you split up the work. Some of the HW problems (esp. the project ones) can be tricky and take a few hours but OH and disc are really helpful. Def recommend taking with Mason if you can!!
Spring 2020 - Mason is one of my favorite professors in the math department. He rarely teaches undergrad courses, but he designed this course for the data theory major. There's a big focus on creating your own math models. This is much different than other lower div math--grading is on your process/assumptions and not necessarily getting the "right" answer. Mason has corny jokes and shirts and showed us his extensive collection of plushies which was awesome. Live lectures and discussions were clear and recorded, although his handwriting is hard to read (which he openly admits several times lol). He posts his notes from class as well. Both Mason and our TA (Gyu Eun is the best!!!) were very flexible about due dates--we had a 1 week grace period to turn in assignments and more if you just talked to them about your situation. They were especially accommodating at the end of the quarter with all the protests which was really nice. We had 2-3 weeks for a group project on one of the MCM problems with a 20 page write up but it wasn't too bad if you split up the work. Some of the HW problems (esp. the project ones) can be tricky and take a few hours but OH and disc are really helpful. Def recommend taking with Mason if you can!!
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