MATH 42
Introduction to Data-Driven Mathematical Modeling: Life, Universe, and Everything
Grades
3.68
Average grade awarded in MATH 42. Not a student rating: this is what the grades were.
In the A range
76%
of the 391 letter grades
D, F or W
1.3%
of every letter grade awarded, plus withdrawals
Taken pass/no pass
2%
6 of 6 students passed
The registrar's own records: 391 grades over 8 terms, Fall 2021 to Fall 2025. Every instructor is pooled here.
What is inside that record
No summer session is in that record.
The table below splits them, and term averages across those 8 terms ran 3.44 to 3.84.
Historical grades for all professors, across recorded terms. The course summary above includes all professors.
- A+ 7 · 1.8%
- A 231 · 59.1%
- A- 58 · 14.8%
- B+ 40 · 10.2%
- B 29 · 7.4%
- B- 11 · 2.8%
- C+ 6 · 1.5%
- C 5 · 1.3%
- F 4 · 1.0%
- P 6 of 398
- DR 1 of 398
Percentages are of the 391 letter grades. Grey bars are non-letter outcomes: passes (P) and withdrawals (DR).
How the non-letter outcomes are counted
They are excluded from the GPA entirely rather than scored, because counting a P as a 4.0 would be a fabrication.
Withdrawals are the exception to "excluded": DR is not scored, but it IS counted in the D, F or W figure above, which is reported over the letter grades plus the withdrawals. Pass/no pass and S/U outcomes are outside that figure on both sides.
By instructor
| Instructor | Predicted GPA | Raw | n | Terms | A range | Workload | Summer Session C 2026 |
|---|---|---|---|---|---|---|---|
| ENAKOUTSA, KOFFI reviews ↗ | 3.78 | 3.79 | 136 | 2 | 88% | light | not teaching it |
| WONG, KA WAH reviews ↗ | 3.69 | 3.69 | 91 | 2 | 70% | very light | not teaching it |
| JOHNSON, CASEY LYNN reviews ↗ | 3.51 | 3.47 | 66 | 2 | 59% | heavy | not teaching it |
| ARANT, TYLER JAMES reviews ↗ | 3.62 | 3.60 | 53 | 1 | 70% | - | not teaching it |
| CONLEY, WILLIAM JOSEPH reviews ↗ | 3.71 | 3.72 | 45 | 1 | 82% | - | not teaching it |
Why Predicted is not the Raw average
“Predicted” is shrunk toward the course average, which is itself shrunk toward the department, so an instructor with a handful of students sits near the course mean rather than topping the list.
Grade records describe outcomes. For what a course is like to take, read student reviews written on dibs below or follow the separate external review link.
Workload and assignments
Work types reported in BruinWalk reviews of this course, across recorded instructors and terms. They may differ from this term's syllabus.
dibs has not read this course’s reviews, so it cannot say what kind of work it sets. That is dibs not having looked. It is not a course without papers or exams, and the reviews linked below are the answer until dibs catches up.
What comes first
The chain dibs can see is 5 courses long before MATH 42. Read from Fall 2026, because dibs has not read a class page for MATH 42 in Summer Session C 2026 and a department can change what it requires. This is a floor, not a deadline: dibs has not read a class page for MATH 3B and STATS 12, so the real chain can only be longer than this, never shorter.
Counted in courses rather than quarters: dibs does not know which of them a department lets you take together.
From the registrar's catalog
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.
Sections in Summer Session C 2026
From the people who took it
Student reviews
What taking MATH 42 was like: the work, the teaching, and what students wish they’d known.
Your experience could be the first.
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