COMPSCI220 Algorithms and Data Structures
The Complete Exam Bible · S2 2026COMPSCI220 Overview
- School of Computer Science, University of Auckland
- Semester Two, 2026
- Stage 2 undergraduate course
- 15 points
COMPSCI 220 Algorithms and Data Structures develops asymptotic analysis, sorting, recurrences, trees, heaps, hashing and graph algorithms. It is taught within School of Computer Science, University of Auckland. It is Stage 2 undergraduate course. It carries 15 points.
- Three pass checks A student must pass practical work, pass the tests-and-exam theory component and pass overall; strength in one side cannot erase failure in another.
- 15/35/50 architecture Practical work is 15%, the two tests total 35%, and the comprehensive final examination is 50%.
- Scale is the question An algorithm can be correct on a small input yet unusable at scale; state the operation count and the input variable behind every growth claim.
What you get in the full COMPSCI220 Exam Bible
- 6 chapters, 18 pages — every examinable topic, week by week
- 9 original practice questions, each with a model answer in the Final Examination format
- 6 worked examples, solved the way COMPSCI220 marks
- 14 computed figures, redrawn from the course’s own numbers
- Exam-morning plan for the Final Examination (50%) — what to revise last, where marks are lost
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full exam bible.
How COMPSCI220 is assessed
| Component | Weight | Format |
|---|---|---|
| Practical Component · hurdle | 15% | Mathematics prerequisite quiz, written and programming assignments, and tutorial presentations; this component must be passed |
| Tests | 35% | Two midterm tests contributing 17.5% each to the theory component |
| Final Examination | 50% | Comprehensive theory assessment covering the full course |
Practical work contributes 15%, two tests contribute 35% and the final exam contributes 50%. Students must pass the practical component, the theory component formed by tests and exam, and the course overall.
What other students are asking about COMPSCI220
- Where can I find lecture notes or study guides for COMPSCI220 at Auckland?
- The COMPSCI220 study guide in AskSia Library covers Algorithms and Data Structures in 6 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in COMPSCI220, and how much is it worth?
- Yes. In the offering this AskSia Library guide is built from, Final Examination carries 50% of the COMPSCI220 mark; the full split is Practical Component 15%, Tests 35%, Final Examination 50%.
- What does COMPSCI220 cover on sorting algorithms and recurrences?
- In AskSia Library's COMPSCI220 guide, the Sorting Algorithms and Recurrences chapter explains comparison sort · recurrence · stability · trace a divide-and-conquer sort and solve or justify its recurrence at the required scale.
- What does COMPSCI220 cover on heaps and search trees?
- In AskSia Library's COMPSCI220 guide, the Heaps and Search Trees chapter explains heap · binary search tree · tree height · execute insertion or removal and explain how structural height controls running time.
- What does COMPSCI220 cover on hashing and collision control?
- In AskSia Library's COMPSCI220 guide, the Hashing and Collision Control chapter explains hash function · load factor · collision resolution · trace insertions through collisions and relate load factor to expected access cost.
What COMPSCI220 covers
Build the course in three arcs: Asymptotic Analysis and Correctness establishes the frame, Hashing and Collision Control deepens it, and Weighted Graphs, Shortest Paths and Spanning Trees tests the complete method.
Asymptotic Analysis and Correctness
asymptotic analysis · Big-O bound · loop invariant · prove a simple loop result and derive an asymptotic bound from its operation countDefine asymptotic analysis
The course material gives this chapter a concrete anchor: The opening materials connect mathematical prerequisites, program-performance analysis and correctness before individual data structures are introduced.
- asymptotic analysis
- Big-O bound
- loop invariant
- Applying asymptotic analysis
- Limits of Big-O bound and loop invariant
Sorting Algorithms and Recurrences
comparison sort · recurrence · stability · trace a divide-and-conquer sort and solve or justify its recurrence at the required scaleDefine comparison sort
The course material gives this chapter a concrete anchor: Two sorting weeks and the landed solution material connect algorithm traces with recurrence and comparison-cost reasoning.
- comparison sort
- recurrence
- stability
- Applying comparison sort
- Limits of recurrence and stability
Heaps and Search Trees
heap · binary search tree · tree height · execute insertion or removal and explain how structural height controls running timeDefine heap
The course material gives this chapter a concrete anchor: Week 4 and the review material compare tree structures and operations, making height the mechanism behind efficient or degraded performance.
- heap
- binary search tree
- tree height
- Applying heap
- Limits of binary search tree and tree height
Hashing and Collision Control
hash function · load factor · collision resolution · trace insertions through collisions and relate load factor to expected access costDefine hash function
The course material gives this chapter a concrete anchor: The current hashing week and tutorial answers provide collision and table-state practice grounded in actual operations rather than a generic dictionary analogy.
- hash function
- load factor
- collision resolution
- Applying hash function
- Limits of load factor and collision resolution
Graph Traversal and Components
graph representation · breadth-first search · connected component · trace a traversal, record its discovery structure and identify reachable componentsDefine graph representation
The course material gives this chapter a concrete anchor: The course schedule and solution material place traversal and connected-component reasoning before weighted optimisation, preserving the distinction between reachability and path cost.
- graph representation
- breadth-first search
- connected component
- Applying graph representation
- Limits of breadth-first search and connected component
Weighted Graphs, Shortest Paths and Spanning Trees
edge relaxation · shortest path · minimum spanning tree · execute relaxation and distinguish a shortest-path tree from a minimum spanning treeDefine edge relaxation
The course material gives this chapter a concrete anchor: Weeks 10-12 in the schedule cover weighted graphs, single-source and all-pairs shortest paths and spanning trees, so their objectives and assumptions must be compared explicitly.
- edge relaxation
- shortest path
- minimum spanning tree
- Applying edge relaxation
- Limits of shortest path and minimum spanning tree
It is positioned as Compulsory for Computer Science majors according to the current public course page.
The course combines implementation with hand execution and proof-oriented efficiency analysis; passing requires practical and theory performance separately as well as an overall pass.
Assessment in COMPSCI220 is distributed as follows: Practical work contributes 15%, two tests contribute 35% in total, and the final examination contributes 50%.
The captured detailed breakdown assigns 1% to a mathematics prerequisite quiz, 5% to written assignments, 5% to programming assignments and 4% to tutorial presentations.
The operational assessment conditions matter here. Tests and the final examination form the theory component.
Attendance on campus is required for tests and the examination, but Semester Two sitting details must be checked in the live course site and official timetable.
What makes COMPSCI220 demanding is concrete: The central challenge is to move between code, an operation trace and an asymptotic argument: a correct output on one input is not enough without a reason the algorithm remains correct and scales as claimed.
Students must pass the practical component, pass the theory component made from tests and examination, and achieve an overall pass.
For enrolment planning, COMPSCI 120 and COMPSCI 130 are prerequisites; COMPSCI 717 and SOFTENG 284 are restrictions in the landed course syllabus.
Build the course in three arcs: Asymptotic Analysis and Correctness establishes the frame, Hashing and Collision Control deepens it, and Weighted Graphs, Shortest Paths and Spanning Trees tests the complete method.
Worked example · free
Trace a shortest-path relaxation step
- 1Compute the candidate distance through u as 7 plus 3, giving 10.
- 1Compare the candidate 10 with the current value 12.
- 1Update d(v) to 10 and set u as the predecessor because the candidate is smaller.
- 1State that a non-improving candidate leaves both records unchanged and that algorithm assumptions on edge weights still matter.
Key terms
- Asymptotic analysis
- The study of how an algorithm's time or space requirement grows with input size while ignoring fixed implementation constants.
- Shortest path
- A path between vertices with minimum total edge weight among the paths allowed by the graph and algorithm assumptions.
- Recurrence
- An equation defining a problem's cost or value in terms of smaller instances together with one or more base cases.
- Heap
- A complete-tree data structure whose parent-child ordering supports efficient access to and removal of an extreme-priority element.
- Binary search tree
- A binary tree maintaining an ordering relation between each node and the keys stored in its left and right subtrees.
- Hash table
- A mapping structure that uses a hash function and collision strategy to place and retrieve keys in an indexed table.
- Graph traversal
- A systematic process such as breadth-first or depth-first search for visiting vertices and edges while recording discovery structure.
COMPSCI220 FAQ
What prerequisites or restrictions apply to COMPSCI220?
COMPSCI 120 and COMPSCI 130 are prerequisites; COMPSCI 717 and SOFTENG 284 are restrictions in the landed course syllabus.
Does COMPSCI220 have a hurdle or component-level pass rule?
Students must pass the practical component, pass the theory component made from tests and examination, and achieve an overall pass.
Where do students usually lose marks in COMPSCI220?
The central challenge is to move between code, an operation trace and an asymptotic argument: a correct output on one input is not enough without a reason the algorithm remains correct and scales as claimed.
How is COMPSCI220 assessed?
Practical work contributes 15%, two tests contribute 35% in total, and the final examination contributes 50%. The captured detailed breakdown assigns 1% to a mathematics prerequisite quiz, 5% to written assignments, 5% to programming assignments and 4% to tutorial presentations.
What is the COMPSCI220 exam or final-task format?
Tests and the final examination form the theory component. Attendance on campus is required for tests and the examination, but Semester Two sitting details must be checked in the live course site and official timetable.
Which offering does this COMPSCI220 guide cover?
It is aligned to Semester Two, 2026; confirm your enrolled class and timetable in the current institutional system.
Is this COMPSCI220 resource an official university guide?
No. It is an independent COMPSCI220 study resource; current institutional instructions remain authoritative for assessment operation.
Questions students ask in each COMPSCI220 chapter
- What is the main task in Asymptotic Analysis and Correctness?
- Prove a simple loop result and derive an asymptotic bound from its operation count. Chapter 01 · Asymptotic Analysis and Correctness
- What is the main task in Sorting Algorithms and Recurrences?
- Trace a divide-and-conquer sort and solve or justify its recurrence at the required scale. Chapter 02 · Sorting Algorithms and Recurrences
- What is the main task in Heaps and Search Trees?
- Execute insertion or removal and explain how structural height controls running time. Chapter 03 · Heaps and Search Trees
- What is the main task in Hashing and Collision Control?
- Trace insertions through collisions and relate load factor to expected access cost. Chapter 04 · Hashing and Collision Control
- What is the main task in Graph Traversal and Components?
- Trace a traversal, record its discovery structure and identify reachable components. Chapter 05 · Graph Traversal and Components
- What is the main task in Weighted Graphs, Shortest Paths and Spanning Trees?
- Execute relaxation and distinguish a shortest-path tree from a minimum spanning tree. Chapter 06 · Weighted Graphs, Shortest Paths and Spanning Trees
How to study for the exam
Retrieve the course map, practise the recurring method—state the input representation and correctness condition, trace the algorithm on a boundary case, derive its time or space growth from counted operations and test whether the data-structure or graph assumptions support the conclusion—on changed scenarios, and verify every operational assessment detail in the live institutional system.
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