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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

COMP2123: Data Structures and Algorithms

This unit will teach some powerful ideas that are central to solving algorithmic problems in ways that are more efficient than naive approaches. In particular, students will learn how data collections can support efficient access, for example, how a dictionary or map can allow key-based lookup that does not slow down linearly as the collection grows in size. The data structures covered in this unit include lists, stacks, queues, priority queues, search trees, hash tables, and graphs. Students will also learn efficient techniques for classic tasks such as sorting a collection. The concept of asymptotic notation will be introduced, and used to describe the costs of various data access operations and algorithms.


Academic unit Computer Science
Unit code COMP2123
Unit name Data Structures and Algorithms
Session, year
Semester 1, 2021
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

INFO1105 OR INFO1905 OR COMP2823
INFO1110 OR INFO1910 OR INFO1113 OR DATA1002 OR DATA1902 OR INFO1103 OR INFO1903
Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Andre van Renssen,
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam hurdle task Final exam
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO8 LO7 LO6 LO5 LO4 LO3
Tutorial quiz Quizzes
10% Multiple weeks n/a
Outcomes assessed: LO4 LO5 LO6 LO7 LO8
Assignment Assignments
30% Multiple weeks n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8
hurdle task = hurdle task ?
Type D final exam = Type D final exam ?
  • Assignments: There will be five assignments in total, due every two weeks. These assignments will focus on the design and analysis of algorithms, alternating between paper based and programming. 
  • Quizzes: There will be 10 weekly multiple-choicequizzes in total. 
  • Final exam: Final written examination.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

For more information see

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

These penalties apply when written work is submitted after 11:59pm on the due date. Deduction of 20% of the maximum mark for each calendar day after the due date. Quizzes can do done any time during the week they are released.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 1. Administrivia; 2. Definitions and precision regarding scalability and analysis of algorithms; Lecture and tutorial (4 hr) LO1 LO5 LO8
Week 02 1. Abstract data structures; 2. Stacks and queues Lecture and tutorial (4 hr) LO5 LO6 LO8
Week 03 1. Tree concepts and definitions; 2. Recursion on a tree; 3. Binary tree implementation, general tree implementation Lecture and tutorial (4 hr) LO3 LO5 LO6 LO7 LO8
Week 04 1. Binary search trees; 2. Balanced binary search tree (AVL tree) Lecture and tutorial (4 hr) LO3 LO5 LO6 LO7 LO8
Week 05 1. Simple map implementation by list (sorted and unsorted); 2. Priority queues, heap-as-a-tree and heap-in-array, sorting using priority queue Lecture and tutorial (4 hr) LO5 LO6 LO7 LO8
Week 06 Hashing Lecture and tutorial (4 hr) LO5 LO6 LO8
Week 07 1. Graph representations; 2. Graph traversals Lecture and tutorial (4 hr) LO3 LO5 LO7 LO8
Week 08 1. Shortest path algorithm; 2. Minimum weight spanning tree algorithms Lecture and tutorial (4 hr) LO5 LO7 LO8
Week 09 Greedy method Lecture and tutorial (4 hr) LO2 LO4 LO5 LO8
Week 10 Divide-and-conquer Lecture and tutorial (4 hr) LO2 LO4 LO5 LO8
Week 11 Divide-and-conquer Lecture and tutorial (4 hr) LO2 LO4 LO5 LO8
Week 12 Randomised algorithms Lecture and tutorial (4 hr) LO2 LO4 LO5 LO8
Week 13 Review of Unit of Study and exam preparation Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Prescribed readings

Recommended reading:

  • Title: Algorithm Design and Applications
  • Author/s: Michael Goodrich; Roberto Tamassia
  • ISBN: 978-1-118-33591-8
  • Publisher: Wiley
  • Publish Year: 2015

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. demonstrate proficiency in organising, presenting and discussing professional ideas and issues in oral, written and graphic formats. Thorough descriptive reporting. With thorough consideration of format and audience requirements. Fluent presentation of engineering/IT concepts and issues to professional and non-professional audiences, using a varied range of professional communication tools and formats
  • LO2. design an algorithmic solution to a problem, coding it, analysing its complexity, and evaluating its suitability to a context
  • LO3. write code that recursively performs an operation on a data structure
  • LO4. apply basic algorithmic techniques (e.g. divide-and-conquer, greedy) to given design tasks
  • LO5. use notation of big-Oh to represent asymptotic growth of cost functions
  • LO6. understand commonly used data structures, including lists, stacks, queues, priority queues, search trees, hash tables, and graphs. This covers the way information is represented in each structure, algorithms for manipulating the structure, and analysis of asymptotic complexity of the operations
  • LO7. understand basic algorithms related to data structures, such as algorithms for sorting, tree traversals, and graph traversals
  • LO8. use mathematical methods to evaluate the performance of an algorithm.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
No significant changes were made compared to last year.

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.
In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.
Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see, or the similarity report available in ED ( These programs work in a similar way to TurnItIn in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.
All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.
There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.


The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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