Course Coordinator:Mingzhong Wang (mwang@usc.edu.au) School:School of Science, Technology and Engineering
UniSC Moreton BayUniSC Adelaide |
Blended learning | Most of your course is on campus but you may be able to do some components of this course online. |
Please go to unisc.edu.au for up to date information on the
teaching sessions and campuses where this course is usually offered.
In the mobile world, devices need to meet hardware requirements for battery life and physical size. As such the efficiency of data representation and algorithm design are of critical importance. In this course, you will learn techniques for designing efficient algorithms and data storage. You will analyse time-and space-complexity of algorithms, identifying worst-case, average-case and best-case complexity. You will also use data structures including lists, stacks, queues, priority queues, search trees, hash tables, and graphs as well as algorithms for recursion, sorting and searching.
| Activity | Hours | Beginning Week | Frequency |
| Blended learning | |||
| Learning materials – Pre-recorded concept videos and associated activity | 2hrs | Week 1 | 12 times |
| Tutorial/Workshop 1 – On-Campus Computer Workshop | 2hrs | Week 1 | 12 times |
200 Level (Developing)
12 units
| Course Learning Outcomes On successful completion of this course, you should be able to... | Graduate Qualities Completing these tasks successfully will contribute to you becoming... | |
| 1 | Demonstrate knowledge of data structure and algorithm fundamentals. | Knowledgeable |
| 2 | Develop and evaluate solutions and systems for computing problems with time and/or space constraints. | Empowered |
| 3 | Select, adapt, and design evidence-based/optimal solutions to complex computing problems. | Creative and critical thinker |
| 4 | Describe and evaluate the impact of data structures and algorithms on resource utilisation. | Sustainability-focussed |
| 5 | Communicate data structure and algorithmic analysis applied to a specific situation in reports, design documentation and specifications. | Engaged |
Refer to the UniSC Glossary of terms for definitions of “pre-requisites, co-requisites and anti-requisites”.
ICT221
Not applicable
Not applicable
Not applicable
Not applicable
Standard Grading (GRD)
| High Distinction (HD), Distinction (DN), Credit (CR), Pass (PS), Fail (FL). |
Students will receive ongoing formative feedback during computer workshop and lecture sessions.
| Delivery mode | Task No. | Assessment Product | Individual or Group | Weighting % | What is the duration / length? | When should I submit? | Where should I submit it? |
| All | 1 | Artefact - Technical and Scientific, and Written Piece | Individual | 20% | Code and report |
Week 5 | Online Submission |
| All | 2 | Artefact - Technical and Scientific, and Written Piece | Individual | 40% | Code plus 1500 words |
Week 11 | Online Assignment Submission with plagiarism check |
| All | 3 | Examination - Centrally Scheduled | Individual | 40% | 2 hours |
Exam Period | Online Assignment Submission with plagiarism check |
| All - Assessment Task 1:Storing and Using Data | |||||||||||||
| Goal: | Understand, analyse, and improve the efficiency of software applications. |
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| Product: | Artefact - Technical and Scientific, and Written Piece | ||||||||||||
| Authorship Statement: | |||||||||||||
| Format: | 1 software code package consisting of two solutions to a given problem utilising different data structures and algorithms, and 1 brief report identifying the algorithmic complexity of each solution. |
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| Criteria: |
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| Generic Skills: | Problem solving |
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| All - Assessment Task 2:Coding project | |||||||||||||||||||
| Goal: | You will explore a case study with high time and/or space constraints in its runtime. You will use your knowledge of data structures and algorithms to design, justify and develop an application to meet the case study requirements. |
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| Product: | Artefact - Technical and Scientific, and Written Piece | ||||||||||||||||||
| Authorship Statement: | |||||||||||||||||||
| Format: | 1 software application (code) satisfying the requirements of the case study and 1 report with 1500 words on design decisions justifying the chosen data structures and algorithms. |
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| Criteria: |
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| Generic Skills: | Communication, Problem solving |
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| All - Assessment Task 3:Final Exam | |||||||||||||
| Goal: | The final exam will develop your ability to independently apply your skills and knowledge to solve familiar problem-based questions with confidence within a set time limit. |
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| Product: | Examination - Centrally Scheduled | ||||||||||||
| Authorship Statement: | |||||||||||||
| Format: | This examination consists of a set of questions on the use of data structures and algorithms. The questions are based on tutorial activities and lecture materials. |
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| Criteria: |
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| Generic Skills: | Problem solving, Applying technologies |
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A 12-unit course will have total of 150 learning hours which will include directed study hours (including online if required), self-directed learning and completion of assessable tasks. Student workload is calculated at 12.5 learning hours per one unit.
Please note: Course information, including specific information of recommended readings, learning activities, resources, weekly readings, etc. are available on the course Canvas site– Please log in as soon as possible.
You need regular access to the resource(s) below. Many texts are available as ebooks through the Library at no additional cost.
| Required? | Author | Year | Title | Edition | Publisher |
| Required | Michael T. Goodrich,Roberto Tamassia,Michael H. Goldwasser | 2014 | Data Structures and Algorithms in Java | 6th Edition | John Wiley & Sons |
Not applicable
Academic integrity is the ethical standard of university participation. It ensures that students graduate as a result of proving they are competent in their discipline. This is integral in maintaining the value of academic qualifications. Each industry has expectations and standards of the skills and knowledge within that discipline and these are reflected in assessment.
Academic integrity means that you do not engage in any activity that is considered to be academic fraud; including plagiarism, collusion or outsourcing any part of any assessment item to any other person. You are expected to be honest and ethical by completing all work yourself and indicating in your work which ideas and information were developed by you and which were taken from others. You cannot provide your assessment work to others. You are also expected to provide evidence of wide and critical reading, usually by using appropriate academic references.
In order to minimise incidents of academic fraud, this course may require that some of its assessment tasks, when submitted to Canvas, are electronically checked through Turnitin. This software allows for text comparisons to be made between your submitted assessment item and all other work to which Turnitin has access.
Eligibility for Supplementary Assessment
Your eligibility for supplementary assessment in a course is dependent of the following conditions applying:
(a) The final mark is in the percentage range 47% to 49.4%; and
(b) The course is graded using the Standard Grading scale
Late submissions may be penalised up to and including the following maximum percentage of the assessment task’s identified value, with weekdays and weekends included in the calculation of days late:
(a) One day: deduct 5%;
(b) Two days: deduct 10%;
(c) Three days: deduct 20%;
(d) Four days: deduct 40%;
(e) Five days: deduct 60%;
(f) Six days: deduct 80%;
(g) Seven days: A result of zero is awarded for the assessment task.
The following penalties will apply for a late submission for an online examination:
Less than 15 minutes: No penalty
From 15 minutes to 30 minutes: 20% penalty
More than 30 minutes: 100% penalty
For more information on Academic Learning & Teaching categories including:
For more information, visit https://www.usc.edu.au/explore/policies-and-procedures#academic-learning-and-teaching
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