STATS 102C
Introduction to Monte Carlo Methods
Grades
3.36
Average grade awarded in STATS 102C. Not a student rating: this is what the grades were.
In the A range
59%
of the 1,303 letter grades
D, F or W
4.1%
of every letter grade awarded, plus withdrawals
Taken pass/no pass
<1%
3 of 3 students passed
The registrar's own records: 1,303 grades over 9 terms, Fall 2021 to Fall 2025. Every instructor is pooled here.
What is inside that record
4 of those terms are summer sessions, and summer sessions are counted here.
The table below splits them, and term averages across those 9 terms ran 2.94 to 3.81.
Historical grades for all professors, across recorded terms. The course summary above includes all professors.
- A+ 79 · 6.1%
- A 517 · 39.7%
- A- 170 · 13.0%
- B+ 135 · 10.4%
- B 121 · 9.3%
- B- 85 · 6.5%
- C+ 64 · 4.9%
- C 46 · 3.5%
- C- 32 · 2.5%
- D 30 · 2.3%
- D- 6 · 0.5%
- F 18 · 1.4%
- P 3 of 1,309
- I 3 of 1,309
Percentages are of the 1,303 letter grades. Grey bars are non-letter outcomes: passes (P) and incompletes (I).
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.
By instructor
| Instructor | Predicted GPA | Raw | n | Terms | A range | Workload | Summer Session A 2026 |
|---|---|---|---|---|---|---|---|
| WU, GUANI reviews ↗ | 3.00 | 2.99 | 620 | 6 | 38% | heavy | not teaching it |
| CHEN, MILES SATORI reviews ↗ | 3.62 | 3.64 | 223 | 3 | 73% | average | not teaching it |
| TSIANG, MICHAEL reviews ↗ | 3.74 | 3.78 | 187 | 2 | 84% | light | not teaching it |
| ONYAMBU, SAMUEL ONYANCHA reviews ↗ | 3.65 | 3.68 | 129 | 1 | 74% | - | not teaching it |
| ZHOU, QING reviews ↗ | 3.55 | 3.59 | 77 | 2 | 69% | average | not teaching it |
| WU, YINGNIAN reviews ↗ | 3.70 | 3.78 | 67 | 1 | 88% | light | 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 6 courses long before STATS 102C. Read from Fall 2026, because dibs has not read a class page for STATS 102C in Summer Session A 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 170A, MATH 3B, and STATS 102A, 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 100B (or Mathematics 170S), 102A. Introduction to Markov chain Monte Carlo (MCMC) algorithms for scientific computing. Generation of random numbers from specific distribution. Rejection sampling and importance sampling and their roles in MCMC. Markov chain theory and convergence properties. Metropolis and Gibbs sampling algorithms. Extensions as simulated tempering. Theoretical understanding of methods and their implementation in concrete computational problems. P/NP or letter grading.
Sections in Summer Session A 2026
From the people who took it
Student reviews
What taking STATS 102C was like: the work, the teaching, and what students wish they’d known.
Your experience could be the first.
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