260776 Foundation of Business Analytics
Warning: The information on this page is indicative. The subject outline for a
particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2024 is available in the Archives.
Credit points: 3 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): 260777c Data Processing Using SAS
The lower case 'c' after the subject code indicates that the subject is a corequisite. See definitions for details.
These requisites may not apply to students in certain courses. See access conditions.
Anti-requisite(s): 220700 Data Driven Decision Making AND 26776 Foundations of Business Analytics
Description
Business analytics is concerned with the use of data, quantitative analysis, predictive models and fact-based management to create value for organisations. With the tremendous growth in the amount of data being generated by organisations, government and society, it is crucial that the leaders of tomorrow understand the proper tools to be able to make evidence-based decisions. In Foundations of Business Analysis, students are provided with the foundations of business analytics, as well as an introduction to techniques and methods required to undertake practical problems. Students can ultimately gain the necessary knowledge to initiate and conduct small scale business analytics projects and professionally communicate their findings to a diverse set of stakeholders.
Subject learning objectives (SLOs)
| 1. | demonstrate methods to locate, prepare and analyse data in order to be able to make predictions |
|---|---|
| 2. | communicate the results of a business analytics project |
| 3. | relate ethical principles to the collection, storage and use of data by business and government |
Contribution to the development of graduate attributes
The subject introduces students to business analytics and provides a framework for data-driven decision making. To do so requires students to critically analyse the facts they are presented with so as to provide a solution to the identified problem(s). In doing so, it is important that students are cognizant of multiple points of view. The learning activities enable students to develop and apply analytical and presentation skills in complex and diverse contexts.
This subject is aligned with the following Graduate Attribute(s):
- Communication and collaboration
- Social responsibility and cultural awareness
- Professional and technical competence
More specifically, this subject contributes to the development of the following program learning objective(s):
- Interact with colleagues and stakeholders to work effectively towards agreed outcomes (2.2)
Teaching and learning strategies
Orientation activities
Preparation for the Session - students are expected to undertake activities prior to the first week. These activities (approximately two hours in duration) include online readings, videos (database searching) and interaction with peers and are important in helping students prepare for the subject’s Assessment Tasks. This also provides students with an opportunity to meet and interact with peers.
Students will learn through independent learning activities, group work, peer review, and participation in collaborative online sessions through the learning management system.
Independent learning activities
Relevant readings, videos and activities will be made available online relevant to the topic of the week. Students are expected to come to the collaborative online sessions prepared. This will enhance the students’ ability to progress successfully throughout the subject and complete assessment items effectively. The online material aims to enhance students’ understanding of the topic or delve deeper into a more specific area. Information and links to all these learning activities can be accessed via Canvas as well as the subject outline.
Online collaborative sessions
The online collaborative sessions will provide opportunities for group activities and discussion, self-assessment, peer review and formative feedback from the subject facilitator. Online collaborative sessions will be conducted at a time that enables the majority of students to contribute.
Feedback
Feedback will be frequent and takes several forms including self-assessment and peer review. Formative feedback throughout the subject aims to increase student performance at summative assessments.
Content (topics)
- Introduction to business analytics
- Data exploration
- Ethical considerations in relation to data
- Predictive modelling
- Communication and presentation
Assessment
Assessment task 1: Business Analytics - Context and Ideation (Individual)
| Objective(s): | This addresses subject learning objective(s): 1 and 3 |
|---|---|
| Weight: | 40% |
| Criteria: |
|
Assessment task 2: Business Analytics - Prediction and Communication (Individual)
| Objective(s): | This addresses subject learning objective(s): 1 and 2 |
|---|---|
| Weight: | 60% |
| Criteria: |
|
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
There is no required textbook. Resources will be made available through the learning management system as required.
References
Albright, S. Christian and Winston, Wayne L. (2019). Business analytics: data analysis and decision making, Cengage.
Camm, Jeffrey, Cochran, James J., Fry, Michael J., Ohlmann, Jeffrey W., Anderson, David R. and Sweeney, Dennis J. (2019). Business Analytics, Cengage.
Knaflic, Cole N. (2015). Storytelling with data: A data visualisation guide for business professionals, Wiley.
Sahay, Amar (2018). Business Analytics, Volume 1: A Data-Driven Decision Making Approach for Business, Business Expert Press.