COMP SCI 7319OL - Big Data Analysis & Industry Project

Online - Online Teaching 4 - 2024

In this course, students will complete a medium-scale, industry-inspired, data science project. This project will involve evaluating, selecting and applying relevant data science techniques, principles and theory to a data science problem. Working with a real-world, industry-motivated dataset, students will develop their data science skills and knowledge and demonstrate autonomy, initiative and accountability. Students will need to reflect on the nature of their data and identify any social and ethical concerns and identify appropriate ethical frameworks for data management. As part of the course students will deliver a written and oral presentation of their project design, plan, methodologies, and outcomes.

  • General Course Information
    Course Details
    Course Code COMP SCI 7319OL
    Course Big Data Analysis & Industry Project
    Coordinating Unit Computer Science
    Term Online Teaching 4
    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 Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    Deep discipline knowledge an 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.

    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.

    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.

    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.

    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.

    Aboriginal and cultural competency 
    Graduates have an understanding of, and respect for, Australian Aboriginal values, culture and knowledge.

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

    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.
    University Graduate Attributes

    No information currently available.

  • Learning & Teaching Activities
    Learning & Teaching Modes
    This is a project-based course. The course has six modules as following:
    1. Fundamentals of Big Data
    2. Big Data techniques
    3. Generic Data Modelling
    4. Image Data Modelling
    5. Time series Data Modelling
    6. Advanced machine learning tools

    There are two project-based assessments:
    Assessment 1 is about big data analysis
    Assessment 1 is about image and text data analysis
    Workload

    No information currently available.

    Learning Activities Summary

    No information currently available.

  • 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
    There are two project-based assessments:
    Assessment 1 is a project about big data analysis.
    Assessment 2 is a project on image and text data analysis.

    Assessment Detail

    No information currently available.

    Submission

    No information currently available.

    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.