DATA 7203OL - Working with Big Data
Online - Online Teaching 2 - 2024
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General Course Information
Course Details
Course Code DATA 7203OL Course Working with Big Data Coordinating Unit Mathematical Sciences Term Online Teaching 2 Level Postgraduate Coursework Location/s Online Units 3 Contact Up to 4 hours per week Available for Study Abroad and Exchange N Prerequisites Carousel 2 Courses: COMP SCI 7211OL, DATA 7301OL, DATA 7302OL & MATHS 7027OL Restrictions Master of Data Science (Applied) OL only Assessment Assignments Course Staff
Course Coordinator: Nordiana Shah
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
On successful completion of this course, students will be able to:
1 Explain algorithms for big data sets and methodologies in the context of data mining. 2 Interpret algorithms for particular classes of big data problems. 3 Develop and integrate algorithms as a part of software development for mining big data. 4 Presentation and critical review of research outcomes. 5 Utilise contemporary technologies and practices to effectively handle big datasets. 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.
1-5 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.
2,3 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.
4 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.
1,2 Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
1-5 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.
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Learning Resources
Required Resources
Jure Leskovec, Anand Rajaraman, Jeff Ullman: Mining of Massive Datasets, http://www.mmds.org/ -
Learning & Teaching Activities
Learning & Teaching Modes
Online supported by tutorials/discussions.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Students are expected to spend 10-12 hours per week on this course.
There will be 1-2 hours contact time for learning and teaching activities and students will be working individually and in groups 8-9 hours to carry out the required learning and teaching activities for acquiring the expected knowledge, understanding, and skills in this course.Learning Activities Summary
Schedule Module 1: Introduction to Mining Big Data, Parallel Processing using MapReduce. Module 2: Similar Items and Data Streams. Module 3: PageRank and Frequent Item sets. Module 4: Clustering and Advertising. Module 5: Recommendation Systems and Social Networks. Module 6: Dimensionality Reduction and Machine Learning and Big Data. -
Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Assessment Task Weighting Due Date Learning Outcome Related weeks Assessment 1 - MapReduce, Similar Items and Data Streams 20%
Sunday, Week 2
1, 3, 5 1,2 Assessment 2 - PageRank, Frequent Itemsets, and Clustering 30% Sunday, Week 4 1, 2, 5 3,4 Assessment 3 - Research Article 50%
(25% video,
25% report)Video - Sunday Week 5;
Report - Sunday Week 61, 2, 3, 4 5,6 Assessment Detail
See Assessment SummarySubmission
Submission 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.Course Grading
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.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
Counselling for Fully Online Postgraduate Students
Fully online students can access counselling services here:
Phone: 1800 512 155 (24/7)
SMS service: 0439 449 876 (24/7)
Email: info@assureprograms.com.au
Go to the to learn more, or speak to your Student Success Advisor (SSA) on 1300 296 648 (Monday to Thursday, 8.30am–5pm ACST/ACDT, Friday, 8.30am–4.30pm ACST/ACDT)
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
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