DATA 7203OL - Working with Big Data

Online - Online Teaching 2 - 2024

Use big data tools to explore large data sets. Discover practical algorithms used for solving problems when 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 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
    Course Description Use big data tools to explore large data sets. Discover practical algorithms used for solving problems when 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: Nordiana Shah

    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 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.

    4
  • 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:

    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
    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 6
    1, 2, 3, 4 5,6
    Assessment Detail
    See Assessment Summary
    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

    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 Study Smart Hub 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)

  • 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.