General Information
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.
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Details
Academic unit: Bond Business School Subject code: GMBA71-202 Subject title: Data Analytics for Decision Making Subject level: Postgraduate Semester/Year: May 2020 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Online Workload items: - Seminar: x12 (Total hours: 24) - Webinar
- Personal Study Hours: x12 (Total hours: 72) - Recommended study time & 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. -
Resources
Prescribed resources: Books
- E. Antony Selvanathan,Saroja Selvanathan,Gerald Keller (2016). Business Statistics Abridged. 7th, Cengage AU 896
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: | May 2020 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Online |
| Workload items: |
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| 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
|
|---|---|
| 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 |
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.
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| 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.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Describe the role of data in evidence-based decision making
- Examine the systems by which data is or can be made available
- Understand data measurement issues and apply processes for investigating relationships, based on statistical theory
- Apply modern quantitative tools (Microsoft Excel) to data analysis in a business context
- 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.
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Assessment details
Type Task % Timing* Outcomes assessed *Online Quiz Quizzes 20% Ongoing 1,2,3,4,5 Case Analysis Analyse a dataset using course concepts 30% Ongoing 1,2,3,4,5 Computer-Aided Examination (Open) Final Examination 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.
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Assessment criteria
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 | Quizzes | 20% | Ongoing | 1,2,3,4,5 |
| Case Analysis | Analyse a dataset using course concepts | 30% | Ongoing | 1,2,3,4,5 |
| Computer-Aided Examination (Open) | Final Examination | 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.
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
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Introduction to Data Analytics and Big Data
What is data analytics and big data, the role of data analytics, descriptive statistics (graphical and numerical) and the role of ethics. Introduces the course by discussing ways to visualize data and summarize it quantitatively.
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Probability and Discrete Probability Distributions
Usefulness of probability concepts in business, basics of probability, discrete random variables, expectation and variance. Probability is an underlying concept in data analytics, with an understanding of it being crucial to understand more advanced concepts, such as predictive modelling.
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Continuous Probability Distributions
Continuous random variables, with a focus on the normal distribution. Continues discussion of probability from the previous topic by focusing on variables that do not have a finite number of values.
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Sampling and Sampling Distributions
Types of data, sampling techniques and distribution of the sampling mean. A data analysis is only as reliable as the data on which it is performed. This topic examines different methods for collecting data, along with the advantages and disadvantages of each.
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Confidence Intervals and Hypothesis Testing
Confidence intervals for the mean, hypothesis testing for the mean. This topic introduces statistical methods for making inferences about data that has been collected.
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Linear Regression
Simple and Multiple Linear Regression - introduction to the theory and application. Begins discussion of predictive modelling by discussing how to predict continuous variables.
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Regression with Categorical Dependent Variables
Linear Probability Model and Logistic Regression. Predicting categorical data is an important task in data science. This topic outlines one of the most common techniques used to do this.
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Classification
Big Data classification techniques. Expands on the previous topic's discussion of logistic regression by outlining other well-known techniques for predicting categorical data.
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Clustering
Big Data clustering techniques. Discusses unsupervised learning and ways to identify hidden patterns and groupings in data.
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Project Evaluation
How to evaluate big data projects as managers. Summarises the course and provides insight into managerial considerations when building/running data science teams.