Risk Analytics and Behavioral Science
RMI 660 teaches decision analysis skills for navigating business uncertainty, including formulating questions, identifying valuable information, and taking calculated risks.
Summary
Historical reviews of Justin Sydnor: The course includes four difficult Excel-based homework assignments and four online, open-book exams. The workload is manageable if students engage with the well-explained lecture content.
Grade history
↗letter grades
All recorded terms · compare terms & instructors
Prerequisites
Course map(R M I 300 or graduate/professional standing) and (GEN BUS 306, 704, 705,ECON 310,MATH/STAT 309, 431, or MATH 331), or declared in the Business Exchange program
- RMI 300
- graduate/professional standing
- take one
- declared in the Business Exchange program
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
- Any of
- All of
- Any of
- RMI 300
- graduate/professional standing
- Any of
- Any of
- declared in the Business Exchange program
- All of
Professors
Fall 2026No instructors recorded for this selection.
Historical instructors & teaching patterns
Historical reviews of Justin Sydnor: Justin Sydnor teaches RMI 660 with passion, blending psychology and economics to explore behavioral patterns. Reviewers praise his approachable nature, clear explanations, and discussion-based lectures. While some note occasional over-explanation, his support and engaging content are consistently highlighted.
JUSTIN SYDNOR is recorded teaching in Spring 2016, Fall 2016, Spring 2018, Spring 2019, Spring 2020, Spring 2022, Spring 2023. Recorded history may be incomplete and does not establish a future schedule.
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Fall 2026Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Historical reviews of Justin Sydnor: Justin Sydnor teaches an engaging, discussion-based course blending psychology, economics, and analytics. Reviewers describe the content as insightful, fun, and intriguing due to his passion and humor.
Recent recorded grades — Spring 2024: 3.45 GPA, 65.3% A/AB (n=95 letter grades); Spring 2025: 3.48 GPA, 66.3% A/AB (n=89 letter grades); Spring 2026: 3.49 GPA, 63.0% A/AB (n=92 letter grades).
difficulty & workload
Historical reviews of Justin Sydnor: The course includes four difficult Excel-based homework assignments and four online, open-book exams. The workload is manageable if students engage with the well-explained lecture content.
Historical reviews of Justin Sydnor: Students appreciate Sydnor's approachability, helpful feedback, and desire for student success. Some note that he occasionally over-explains concepts, but the communication-focused environment is generally positive.
Topics
Skills
Grades
Latest available · Spring 2026— not enough history to project Fall 2026.
Grade distribution · % of letter grades
Grades over time
Through Spring 2026
More grade details Grade mix, volume & source data
Where this course fits relative to
Latest available grades · Spring 2026 · all course levels
GPA
Higher than % of other courses in this group.
Course GPAs · red marks this course’s range
letter grades
More recorded grades than % of other courses in this group.
Typical course in this group: letter grades.
About this comparison
1283 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.
Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
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"model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
"model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
"task_version": "14",
"output_id": "9f050fa2724c345d9bd78c60ef20f8ae75b6aa9ce59e250def0996d712553ad6",
"requirements_status": "valid",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}