Data Analytics for Decision Making

General Information

This subject develops the student’s facility for evidence-based decision making, by introducing students to the use and application of data. As the business world has increasing access to data, and in the availability of big data sets which allow greater understanding of customers and other business related data, effective use of the data will enable decisions to become more informed. This course will consider the role of data in an evolving business system, discuss and review common sources of data and processes for developing superior data sets, and will introduce the quantitative methods that are needed for understanding what the data tells us re the decision we need to make. It develops an understanding of modern computational methods to solve quantitative problems in business decision making, using a case-based approach to using data.

  • Academic unit: Bond Business School
    Subject code: GMBA71-202
    Subject title: Data Analytics for Decision Making
    Subject level: Postgraduate
    Semester/Year: September 2019
    Credit points: 10.000
  • Timetable: https://bond.edu.au/timetable
    Delivery mode: Online
    Workload items:
    • Seminar: x12 (Total hours: 24) - Webinar
    • Personal Study Hours: x12 (Total hours: 72) - Study time and reviewing materials
    Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible.
  • Prescribed resources:

    Books

    • E. Antony Selvanathan,Saroja Selvanathan,Gerald Keller (2016). Business Statistics Abridged. 7th, Cengage AU 896
    After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.
    iLearn@Bond & Email: iLearn@Bond 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
Academic unit: Bond Business School
Subject code: GMBA71-202
Subject title: Data Analytics for Decision Making
Subject level: Postgraduate
Semester/Year: September 2019
Credit points: 10.000

Enrolment requirements

Requisites:

Nil
Assumed knowledge:

Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.

Restrictions:

Nil

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. Understand the role of data in evidence based decision making
  2. Examine the systems by which data is or can be made available
  3. Possess an understanding of measurement issues and processes for understanding relationships based on statistical theory
  4. Apply modern quantitative tools (Microsoft Excel) to data analysis in a business context
  5. Analyse and interpret data to provide meaningful information to assist in decision making

Generative Artificial Intelligence in Assessment

The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.

  • Type Task % Timing* Outcomes assessed
    *Online Quiz Minor Projects/Quizzes 20% Ongoing 1,2,3,4,5
    Case Analysis Project 30% Ongoing 1,2,3,4,5
    Computer-Aided Examination (Open) Final Examination/Project 50% Non-Standard Examination Period 1,2,3,4,5
    • * 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.

Type Task % Timing* Outcomes assessed
*Online Quiz Minor Projects/Quizzes 20% Ongoing 1,2,3,4,5
Case Analysis Project 30% Ongoing 1,2,3,4,5
Computer-Aided Examination (Open) Final Examination/Project 50% Non-Standard Examination Period 1,2,3,4,5
  • * 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.

Study Information

Submission procedures

Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.

Policy on late submission and extensions

Unexplained late submissions will not be considered for marks. Penalties will apply for late submissions. The specific late penalties for the exams appear below. Policy for Final Exams: Penalty of 25% per 15 minutes late (rounded up), such that exams submitted more than 45 minutes late receive an ungraded zero.

Academic Integrity

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.

Accessibility and Inclusion 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

Approved on: Jul 10, 2019. Edition: 1.6
Last updated: Nov 16, 2022
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