Course coordinator
Dr Zara Taba
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
To be effective business analysts, graduates must understand the technology platforms that support business analytics. Numerical analysis software is the core platform on which business analytics relies. Using the numerical analysis software R, this course provides students with firsthand experience of coding and programming for business analytics. Thus, the course helps to equip students for careers as business analysts by providing them with knowledge of the platforms that support business analytics.
The revolution in "big data" is transforming both business practices and the curriculum in business schools.ﾠBusiness analytics applies technologies and software environments that have traditionally not been used extensively in business and therefore not heavily featured in business school curricula. A purpose of this course is to provide students with a more structured introduction to the technologies of business analytics, including the key software environments. R is an example of a Programming language which is a platform on which business analytics rests. This course places particular emphasis on an open-source platform (R); however, recognising that many businesses will use proprietary software environments to support their analytics initiatives is important to recognise (for example, software including MATLAB and SAS). Further, this course places emphasis on using this platform to create tools that can support business decision making such as graphical user interfaces and/or decision support systems.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
BISM2204 or BSAN2204
Incompatible
You can't enrol in this course if you've already completed the following:
BISM3210
Course contact
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff doﾠnotﾠhave access to the timetabling system to help with class allocation. Therefore, should you need help with your timetable and/or allocation of classes, please ensure you emailﾠbusiness.mytimetable@uq.edu.auﾠfrom your UQ student email account with the following details:
- Full name,
- Student ID, and
- the Course Code
Aims and outcomes
The broad aim of this course is to provide students with a structured introduction to the technologies of business analytics, with a particular emphasis on the software environments used for business analytics. Students should develop a specific understanding of how these software environments are used and the key concepts in using these software environments (including some emphasis on programming concepts for business analytics). Students should develop an appreciation of how these technologies can increase the productivity of business analysts and business analytics projects, and how businesses and analysts can deploy these technologies to support their business analytics efforts.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Recognise and explain the role of new and emerging technologies for business analytics.
LO2.
Explain the core features of the technology platforms of business analytics.
LO3.
Use and apply the technologies of business analytics with a particular emphasis on open-source computing environments.
LO4.
Compare and critically evaluate the different technologies and software environments used for business analytics.
LO5.
Demonstrate how the technologies of business analytics can support business decision making and improve business performance.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation, Project | Assessment 1 (A1) | 20% |
6/09/2024 5:00 pm |
| Paper/ Report/ Annotation, Project | Assessment 2 (A2) | 50% |
25/10/2024 5:00 pm |
| Essay/ Critique | Assessment 3 (A3) | 30% |
7/11/2024 - 14/11/2024 |
Assessment details
Assessment 1 (A1)
- Mode
- Product/ Artefact/ Multimedia
- Category
- Paper/ Report/ Annotation, Project
- Weight
- 20%
- Due date
6/09/2024 5:00 pm
- Learning outcomes
- L01, L02, L03
Task description
A project plan/proposal outlining the plan for your project in creating a software tool.
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task. Students must clearly reference any use of AI in each instance.
A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Via Blackboard
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assessment 2 (A2)
- Mode
- Product/ Artefact/ Multimedia
- Category
- Paper/ Report/ Annotation, Project
- Weight
- 50%
- Due date
25/10/2024 5:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
A project report on the developed software tool.
Artificial Intelligence (AI) provides emerging tools that may support students in completing this assessment task. Students may appropriately use AI in completing this assessment task. Students must clearly reference any use of AI in each instance.
A failure to reference generative AI use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Via Blackboard
Deferral or extension
You may be able to apply for an extension.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assessment 3 (A3)
- Mode
- Written
- Category
- Essay/ Critique
- Weight
- 30%
- Due date
7/11/2024 - 14/11/2024
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
You have a seven-day window to complete your assessment. You can access and submit your assessment at any time within the seven-day window.
AI Statement:
Machine Translation (MT) may support students in completing this assessment task. Students may appropriately use MT in completing this assessment task. Students must clearly reference any use of MT in each instance.
Use of generative Artificial Intelligence (AI) in this task is prohibited.
A failure to reference MT use and / or the use of generative AI may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 29 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Seminar |
Course Introduction Learning outcomes: L01 |
Week 2 |
Seminar |
Software for Business Analytics Learning outcomes: L01, L02 |
Practical |
Quick start in R Learning outcomes: L03 |
|
Week 3 |
Seminar |
Programming Concepts - Data Structures Learning outcomes: L02, L03 |
Practical |
The R Environment Learning outcomes: L03 |
|
Week 4 |
Seminar |
Programming Concepts - Input/Output Learning outcomes: L02, L03, L04, L05 |
Practical |
Basic expressions and visualisation Learning outcomes: L03 |
|
Week 5 |
Seminar |
Software Testing - Process and Practices Learning outcomes: L02, L03, L05 |
Practical |
Inside R Learning outcomes: L03 |
|
Week 6 |
Seminar |
Creating a Graphical User Interface and Website Learning outcomes: L02, L03, L05 |
Practical |
Shiny package in R Learning outcomes: L03 |
|
Week 7 |
Seminar |
Data management and linkage Learning outcomes: L02, L03, L05 |
Practical |
Meta-programming in R Learning outcomes: L03 |
|
Week 8 |
Seminar |
Creating an Information Repository Learning outcomes: L01, L02 |
Practical |
Object-Oriented Programming Learning outcomes: L03 |
|
Week 9 |
Seminar |
Cyber-Security (Privacy and Confidentiality) Learning outcomes: L02, L03, L05 |
Practical |
Working with databases Learning outcomes: L03 |
|
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 10 |
Seminar |
Software Documentation-Technical and End User Learning outcomes: L02, L03, L05 |
Practical |
Data Manipulation Learning outcomes: L03 |
|
Week 11 |
Practical |
Data Manipulation part 2 Learning outcomes: L02, L03, L05 |
Week 12 |
Seminar |
Project workshop and Consult Learning outcomes: L01, L02, L03, L04, L05 |
Week 13 |
Seminar |
Revision and Take-Home Assessment |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.