Unit of study_

# COMP2123: Data Structures and Algorithms

## Overview

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.

### Unit details and rules

Unit code COMP2123 Computer Science 6 INFO1105 OR INFO1905 OR COMP2823 INFO1110 OR INFO1910 OR INFO1113 OR DATA1002 OR DATA1902 OR INFO1103 OR INFO1903 None None Yes

### Teaching staff

Coordinator Andre van Renssen, andre.vanrenssen@sydney.edu.au

## Assessment

Type Description Weight Due Length
Final exam Final exam
60% Formal exam period 2 hours
Outcomes assessed:
Tutorial quiz Quizzes
10% Multiple weeks n/a
Outcomes assessed:
Assignment Assignments
30% Multiple weeks n/a
Outcomes assessed:

### Assessment summary

• Assignments: Short bi-weekly assignment alternating between paper based and programming.
• Quizzes: Short weekly multiple-choice quizzes, during tutorial times, on e-learning.
• 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

Description

High distinction

85 - 100

Distinction

75 - 84

Credit

65 - 74

Pass

50 - 64

Fail

0 - 49

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

### 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.

The Current Student website  provides information on academic integrity 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 integrity breaches seriously.

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

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

## Learning support

### Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

### Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

### Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

## Weekly schedule

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)
Week 02 1. Abstract data structures; 2. Stacks and queues Lecture and tutorial (4 hr)
Week 03 1. Tree concepts and definitions; 2. Recursion on a tree; 3. Binary tree implementation, general tree implementation Lecture and tutorial (4 hr)
Week 04 1. Binary search trees; 2. Balanced binary search tree (AVL tree) Lecture and tutorial (4 hr)
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)
Week 06 Hashing Lecture and tutorial (4 hr)
Week 07 1. Graph representations; 2. Graph traversals Lecture and tutorial (4 hr)
Week 08 1. Shortest path algorithm; 2. Minimum weight spanning tree algorithms Lecture and tutorial (4 hr)
Week 09 Greedy method Lecture and tutorial (4 hr)
Week 10 Divide-and-conquer Lecture and tutorial (4 hr)
Week 11 Divide-and-conquer Lecture and tutorial (4 hr)
Week 12 Randomised algorithms Lecture and tutorial (4 hr)
Week 13 Review of Unit of Study and exam preparation Lecture and tutorial (4 hr)

### 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.

## Learning outcomes

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.

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.