BIOINFO M291
Systems Biology: Practical Data Analysis
Also listed as CHEM M291: the same class, and each department holds its own seats and its own grade history.
Grades
4.00
Average grade awarded in BIOINFO M291. Not a student rating: this is what the grades were.
In the A range
100%
of the 10 letter grades
D, F or W
0.0%
of every letter grade awarded, plus withdrawals
Taken pass/no pass
0%
nobody in the record took it pass/no pass
The registrar's own records: 10 grades over 1 term, 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.
Historical grades for all professors, across recorded terms. The course summary above includes all professors.
- A 10 · 100.0%
Percentages are of the 10 letter grades. Grey bars are non-letter outcomes.
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 | Winter 2027 |
|---|---|---|---|---|---|---|---|
| WOLLMAN, ROY reviews ↗ | 3.98 | 4.00 | 10 · | 1 | 100% | - | not teaching it |
| SHAH, PAVAK KIRIT reviews ↗ | 3.98 | 4.00 | 10 · | 1 | 100% | - | 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.
From the registrar's catalog
(Same as Chemistry M291.) Lecture, three hours. Practical training in the analysis of large-scale biological datasets using Python. Students work with real-world data from single-cell RNA-seq, spatial transcriptomics, phospho-proteomics, and high-content imaging studies. Emphasis on hands-on coding, statistical and machine learning modeling, and clear data interpretation and visualization. A key component is the responsible use of artificial intelligence coding assistants. Students learn how to use tools like ChatGPT and Copilot to scaffold code, debug, and accelerate their analysis, while ensuring transparency and reproducibility. S/U or letter grading.
Sections in Winter 2027
From the people who took it
Student reviews
What taking BIOINFO M291 was like: the work, the teaching, and what students wish they’d known.
Your experience could be the first.
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