COMP SCI 7306 - Mining Big Data
North Terrace Campus - Semester 1 - 2022
General Course Information
Course Code COMP SCI 7306 Course Mining Big Data Coordinating Unit School of Computer Science Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Contact Up to 2 hours per week Available for Study Abroad and Exchange Y Prerequisites COMP SCI 7201 Incompatible COMP SCI 7403 Restrictions Master of Computing and Innovation, Master of Data Science (Not Available to Extension Masters), Graduate Diploma in Computer Science and Graduate Certificate in Computer Science students only. Course Description The Web and Internet Commerce provide extremely large datasets from which important information can be extracted by data mining. This course will cover practical algorithms for solving key problems in mining of massive datasets. It focuses on parallel algorithmic techniques that are used for large datasets in the area of cloud computing. Furthermore, stream processing algorithms for data streams that arrive constantly, page ranking algorithms for web search, and online advertisement systems are studied in detail.
Course Coordinator: Professor Javen Qinfeng Shi
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesOn successful completion of this course students will be able to:
1 Assess what applications are data mining problems, and what are not. 2 Choose suitable algorithms for particular data mining problems. 3 Develop and/or apply algorithms for mining big data.
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s)
Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
Attribute 2: Creative and critical thinking, and problem solving
Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.
Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
Attribute 4: Professionalism and leadership readiness
Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.
Attribute 5: Intercultural and ethical competency
Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.
Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
Required ResourcesAll required resources for this course will be provided online via the MyUni platform
Recommended ResourcesTextbook and additional course materials: http://www.mmds.org/
Learning & Teaching Activities
Learning & Teaching ModesThe course will be delivered through the following activities:
Lectures will introduce and motivate the basic concepts of each topic. Significant discussions and two-way communication are also expected during the lectures. The assignments will reinforce concepts by their application to problem solving. This will be done via programming work and mathmatical derivation. All material covered in the lectures and assignments are assessable.
Workshops are designed to demosrate more practical aspects (like Hadoop) and tips for assignments.
WorkloadStudents are expected to spend 7-8 hours per week on this course.
There will be 1-2 hours contact time for learning and teaching activities and students will be working in groups and individually 5-6 hours to carry out the required learning and teaching activities for acquiring the expected knowledge, understanding, and skills in this course.
Learning Activities SummaryThis is a 3-unit course. Students are expected to spend about 8 hours per week on the course including a 2-hour lecture, 2-hour self study and up to 4 hours per week on completing assignments on average.
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment must maintain academic standards.
Assessment Task Weighting (%) Individual/ Group Formative/ Summative Due (week)* Hurdle criteria Learning outcomes CBOK Alignment** Assignment 1 15 Group Summative TBA 1. 2. 1, 2, 3.1, 3.2, 3.3, 4.1 Assignment 2 15 Group Summative TBA 2. 1, 2, 3.1, 3.2, 3.3, 4.1 Assignment 3 15 Group Summative TBA 3. 1, 2, 3.1, 3.2, 4.1 Assignment 4 15 Individual Summative TBA 3. 1, 2, 3.1, 3.2, 4.1 Exam 40 Individual Summative ^^ 3. 1, 3.2, 4.1 Total 100
This assessment breakdown complies with the University's Assessment for Coursework Programs Policy.
^^Hurdle Requirement: If your overall mark for the course is greater than 45F, and your mark for the final written exam is less than 40%, your overall mark for the course will be reduced to 45F.
**CBOK is the Core Body of Knowledge for ICT Professionals defined by the Australian Computer Society. The alignment in the table above corresponds with the following CBOK Areas:
1. Problem Solving1.1 Abstraction1.2 Design
2. Professional Knowledge2.1 Ethics2.2 Professional expectations2.3 Teamwork concepts & issues2.4 Interpersonal communications2.5 Societal issues2.6 Understanding of ICT profession
3. Technology resources3.1 Hardware & Software3.2 Data & information3.3 Networking
4. Technology Building4.1 Programming4.2 Human factors4.3 Systems development4.4 Systems acquisition
5. ICT Management5.1 IT governance & organisational5.2 IT project management5.3 Service management5.4 Security management
Due to COVID-19 situation modified arrangements have been made to assessments to facilitate remote learning and teaching. Assessment details provided here reflect recent updates.
Setting for carrying out assignments will not change. Assignments will be discussed online during workshop sessions.
Invigilation will be finalised over the coming weeks and communicated at a later stage.
No change to the weightening of the course components
Assessment DetailHurdle Requirement: If your overall mark for the course is greater than 45F, and your mark for the final written exam is less than 40%, your overall mark for the course will be reduced to 45F.
SubmissionSubmission details for all activities are available in MyUni but the majority of your submissions will be online and may be subjected to originality testing through Turnitin or other mechanisms. You will receive clear and timely notice of all submission details in advance of the submission date.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Final results for this course will be made available through Access Adelaide.
- Academic Support with Maths
- Academic Support with writing and speaking skills
- Student Life Counselling Support - Personal counselling for issues affecting study
- International Student Support
- AUU Student Care - Advocacy, confidential counselling, welfare support and advice
- Students with a Disability - Alternative academic arrangements
- Reasonable Adjustments to Teaching & Assessment for Students with a Disability Policy
- LinkedIn Learning
Policies & Guidelines
- Academic Credit Arrangement Policy
- Academic Honesty Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Elder Conservatorium of Music Noise Management Plan
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process