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DTSC13-303: Data Analytics Case Studies January 2020 [Standard]

General information

Data analysis is the skill of the future. This subject requires students to explore the concepts, theories and frameworks of big data research from their entire program in a series of case studies. Through reflection and interaction, students will learn the research process and analyze how the research question presented in case studies is relevant to both industry and the academic community. Students will examine the relevant literature, identify the big data techniques utilised, interpret the results and evaluate their implications.

Details

Academic unit:Bond Business School
Subject code:DTSC13-303
Subject title:Big Data Analytics Case Studies
Subject level:Undergraduate
Semester/Year:January 2020
Credit points:10

Delivery & attendance

Timetable: https://bond.edu.au/timetable
Delivery mode:

Standard

Workload items:
  • Lecture: x12 (Total hours: 24) - Weekly Lecture
  • Computer Lab: x12 (Total hours: 24) - Computer Lab
  • Personal Study Hours: x12 (Total hours: 72) - Study time and reviewing materials
Attendance and learning activities: Attendance at all sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible.

Resources

Prescribed resources: No Prescribed resources. After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.
[email protected] & Email:[email protected] is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.

To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au

Enrolment requirements

Requisites: ?

Pre-requisites: ?

  • INFT13-326 Statistical Learning and Regression Models
  • INFT12-223 Machine Learning in Business

Co-requisites: ?

There are no co-requisites.

Restrictions: ? This subject is not available to
  • Study Abroad Students

This subject is not available as a general elective. To be eligible for enrolment, the subject must be specified in the students’ program structure.

Anti-requisites: ?

  • INFT13-327 Advanced Big Data Projects

Assurance of learning

Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.

At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.

Program Learning Outcomes (PLOs)

Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.

Find your program

Subject Learning Outcomes (SLOs)

On successful completion of this subject the learner will be able to:
  1. Apply knowledge from previous subjects in the discipline to a real big data project.
  2. Critically analyse and synthesise the relevant literature on an approved big data research topic.
  3. Analyse the design of data collection and analyses from a given research question.
  4. Articulate the rationale, theory, methods, findings and implications of a research project in a professional written report.
  5. Deliver a clear, concise well-organised research presentation appropriate to specialist and non-specialist audiences using suitable visual aids.

Assessment

Assessment details

TypeTask%Timing*Outcomes assessed
Case Report written report: Case study analysis. Students choose an individual case study for thorough analysis, examining the research process, theory and implications of the research. 50% Week 3 3, 4.
Case Presentation presentation: Case study. Students will create and deliver a research presentation appropriate to a specialist and non-specialist audience. 15% Week 12 4, 5.
Reflective Journal reflective journal: Students log weekly journal reflections linking previous knowledge to big data research processes identified in literature and case studies 35% Week 13 1, 2.
  • * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
  • C = Students must reach a level of competency to successfully complete this assessment.

Assessment criteria

High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking.
Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas.
Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above.
Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment.
Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines.

Quality assurance

For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Study information

Submission procedures

Students must check the [email protected] subject site for detailed assessment information and submission procedures.

Policy on late submission and extensions

A late penalty will be applied to all overdue assessment tasks unless an extension is granted by the subject coordinator. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.

Policy on plagiarism

The University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.

Bond University utilises Originality Reporting software to inform academic integrity.

Feedback on assessment

Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.

Disability support

If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.

Additional subject information

As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Subject curriculum

A detailed curriculum has not been published for this subject.
Approved on: Sep 10, 2019. Edition: 1.0
Last updated: Sep 25, 2019.