Algorithmic Thinking
Algorithms course covering divide and conquer, dynamic programming, and greed paradigms, plus intermediate data structures and program development.
Summary
Past student reviews · Lydia Zepeda
Students reported frequent pop quizzes nearly every day and noted that exams differed significantly from the provided practice questions.
What you’ll be able to do
Formulate abstractions of computational problems.
Design and implement efficient algorithms based on the paradigms of divide and conquer, dynamic programming, and greed.
Analyze worst-case running times and memory requirements of algorithms using asymptotic notation.
Know, evaluate, and apply common algorithms and intermediate data structures dealing with numbers, strings, and graphs, understand their basic efficiency trade-offs and appropriate use cases.
Carry through the process of program development from nontrivial real-world problem descriptions, over precise specifications and algorithmic solutions, to implementations.
Source: UW Guide
Grade history
↗No recorded grade history.
All recorded terms · compare terms & instructors
Prerequisites
Course mapDeclared in the Capstone Certificate in Computer Sciences Kickstart Program for Professionals
- Declared in the Capstone Certificate in Computer Sciences Kickstart Program for Professionals
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Prerequisite text tree
- Declared in the Capstone Certificate in Computer Sciences Kickstart Program for Professionals
Professors
Spring 2027No instructors recorded for this selection.
Historical instructors & teaching patterns
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Spring 2027Schedule 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
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Student experience
the class
Past student reviews · Lydia Ashton
The course featured a supportive instructor who offered extra credit for participation, though the online format meant no exams were administered.
difficulty & workload
Past student reviews · Lydia Zepeda
Students reported frequent pop quizzes nearly every day and noted that exams differed significantly from the provided practice questions.
Past student reviews · Lydia Ashton
Weekly group work was a recurring requirement that could be frustrating if assigned to a difficult group, despite the instructor's approachability.
Topics
- Algorithm design paradigms including divide and conquer, dynamic programming, and greed
- Discrete mathematics and intermediate data structures
- Data structures and algorithms for numbers, strings, and graphs
Skills
- Design and implement efficient algorithms using divide and conquer, dynamic programming, and greed
- Analyze worst-case running times and memory requirements using asymptotic notation
- Apply common algorithms and intermediate data structures for numbers, strings, and graphs
- Develop programs from real-world problem descriptions to implementation
Grades
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Sources & history
Catalog & offerings
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Student reviews
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Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesRecorded 2026-10-14
Model 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": "Qwen/Qwen3.8-27B",
"model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
"task_version": "17",
"output_id": "0bc067c3db5347d008575cfbc3fe407f454b41a8c1e268f49aac12c2ba33e273",
"requirements_status": "valid",
"dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
"observed_at": "2026-10-14 17:50:09.728220+00:00"
}