COMP SCI 3306 - Mining Big Data

North Terrace Campus - Semester 1 - 2022

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.

  • General Course Information
    Course Details
    Course Code COMP SCI 3306
    Course Mining Big Data
    Coordinating Unit School of Computer Science
    Term Semester 1
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 2 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites COMP SCI 2201
    Incompatible COMP SCI 4403, COMP SCI 4803
    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 Staff

    Course Coordinator: Professor Javen Qinfeng Shi

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    On 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.

    1-3

    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.

    1-3

    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.

    1-3

    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.

    1-3

    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.

    1-3

    Attribute 6: Australian Aboriginal and Torres Strait Islander cultural competency

    Graduates have an understanding of, and respect for, Australian Aboriginal and Torres Strait Islander values, culture and knowledge.

    1-3

    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.

    1-3

    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.

    3
  • Learning Resources
    Required Resources
    All required resources for this course will be provided online via the MyUni platform.
    Recommended Resources
    Textbook and additional course materials: http://www.mmds.org/
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course will be delivered through the following activities:

    - Lectures
    - Assignments
    - Workshops

    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.
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Students 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 Summary
    This 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

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    3 Assignments each contributing 10% to the final mark plus final exam contributing 70% to the final mark.

    Assessment Task Weighting (%) Individual/ Group Formative/ Summative
    Due (week)*
    Hurdle criteria Learning outcomes CBOK Alignment**
    Assignment 1 10 individual / Group Summative Week 4 1. 2.
    Assignment 2 10 individual / Group Summative Week 7 2.
    Assignment 3 10 individual / Group summative Week 11 3.
    Exam 70 3.
    Total 100
    * The specific due date for each assessment task will be available on MyUni.

    This assessment breakdown complies with the University's Assessment for Coursework Programs Policy.



    **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 Solving
    1.1 Abstraction
    1.2 Design

    2. Professional Knowledge
    2.1 Ethics
    2.2 Professional expectations
    2.3 Teamwork concepts & issues
    2.4 Interpersonal communications
    2.5 Societal issues
    2.6 Understanding of ICT profession

    3. Technology resources
    3.1 Hardware & Software
    3.2 Data & information
    3.3 Networking

    4. Technology Building
    4.1 Programming
    4.2 Human factors
    4.3 Systems development
    4.4 Systems acquisition

    5. ICT Management
    5.1 IT governance & organisational
    5.2 IT project management
    5.3 Service management
    5.4 Security management

    Due to the current 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 Detail

    No information currently available.

    Submission
    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 Access Adelaide.

  • 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.

  • Student Support
  • Policies & Guidelines
  • 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’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.