Course coordinator
Dr Bikesh Raj Upreti
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
Data Analytics for Business concerns the use of data for decision making in business. It focuses on how organisations can create business value from data by modelling, integrating, analysing and visualising data. It helps students develop fundamental data analytics knowledge, experience contemporary data analytic tools and learn about broader organisational and societal implications of data analytics such as data-driven transformation and ethics of big data.
Data analytics comprises skills, processes, and technologies to create timely and meaningful insights from organisational data. The insights improve managerial decision-making to create significant business value. The course explores the technical and managerial processes of using data for decision-making. It provides fundamental knowledge and skills necessary to model, integrate, analyse and visualise data. These analytical skills are highly sought after skills in various professions and industries. The course covers relevant theories as well as provides hands-on experience with business analytics tools.
Course requirements
Assumed background
This course aims to develop student's technical competencies for using the tools and techniques related to data integration, visualisation, and analytics. This is a higher-level course, and the work load and expectations are commensurate with this. The course requires a strong background in database and SQL skills, as reflected in the prerequisites. In preparation for attempting the practical components of this course, please ensure you follow all the materials in the reading list, lecture contents, and tutorials.
Before attempting this course, students are strongly recommended to complete the prerequisite courses(s) listed on the front of this course profile. No responsibility will be accepted by the School of Business, the Faculty of Business, Economics and Law or The University of Queensland for poor student performance occurring in courses where the appropriate prerequisite(s) has/have not been completed, for any reason whatsoever.
ﾠ
Prerequisites
You'll need to complete the following courses before enrolling in this one:
BISM7206 or MGTS7206
Incompatible
You can't enrol in this course if you've already completed the following:
BISM2202 or MGTS2202 or INFS7233
Restrictions
Quota: Minimum of 15 enrolments
Course contact
Course staff
Lecturer
Dr Bikesh Raj Upreti
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Please note: Teaching staff do not have 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 email business.mytimetable@uq.edu.au from your UQ student email account with the following details:
- Full Name
- Student ID
- Course Code
Aims and outcomes
This course aims to introduce students to data analytics concepts and techniques that are used to improve organisational decision-making. Students will develop knowledge and hands-on skills to manage, visualise and analyse data. Technical topics include dimensional modelling, Extract-Transform-Load (ETL), digital dashboards, prediction, classification and clustering algorithms. Managerial topics include data-driven transformations, data monetization and ethical use of data.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Demonstrate knowledge of Data Analytics for decision-making
LO2.
Apply the principles of data management to model and integrate organisational data
LO3.
Apply data analytics techniques individually to solve business problems
LO4.
Apply data visualisation principles and techniques to represent insights from data
LO5.
Demonstrate knowledge of managerial and societal issues related to data
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Essay/ Critique, Practical/ Demonstration, Project | Data Integration Using SSIS | 20% |
29/04/2025 2:00 pm |
| Paper/ Report/ Annotation | Business Analytics Case Study | 40% |
26/05/2025 2:00 pm |
| Examination |
Final Course Exam
|
40% |
End of Semester Exam Period 7/06/2025 - 21/06/2025 |
Assessment details
Data Integration Using SSIS
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Essay/ Critique, Practical/ Demonstration, Project
- Weight
- 20%
- Due date
29/04/2025 2:00 pm
- Learning outcomes
- L01, L02
Task description
You will be asked to design a Dimensional model and create a Data warehouse for the case company. You will also run a few analytical queries to generate business insights. You will write a short report (1000 words explaining, the rationale, process, and results of your analysis).
AI Statement:
This assessment task evaluates students' abilities, skills, and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Report and other outputs to be submitted electronically via TurnItIn and BlackBoard.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
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.
Business Analytics Case Study
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
26/05/2025 2:00 pm
- Learning outcomes
- L01, L02, L03, L05
Task description
You will complete an essay that requires you to undertake research in order to prepare a report (minimum 1500 words and strictly no more than 1600 words).
- You will be presented with a business analytics case with diverse stakeholder requests. You are tasked with developing recommendations for business analytics cases, where you will apply your knowledge and skill to develop an analytical model for business decision-making.
- A detailed rubric for the project will be accessible via Blackboard.
- Full details of the requirements for the written report will be provided with the release of the Project assessment document.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Report and other outputs to be submitted electronically via TurnItIn and 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.
Final Course Exam
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 40%
- Due date
End of Semester Exam Period
7/06/2025 - 21/06/2025
- Other conditions
- Time limited.
- Learning outcomes
- L01, L03, L04, L05
Task description
End of Course exam with a mix of multiple choice questions, conceptual short questions, and/or scenario-based analysis questions.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | No calculators permitted |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You cannot defer or apply for an extension for this assessment.
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. |
Additional course grading information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
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 |
Lecture |
Lecture 1: Course Overview and Business Analytics Framework. Learning outcomes: L01, L05 |
Week 2 |
Tutorial |
Tute 1: Introduction to Business Analytics. Learning outcomes: L01 |
Lecture |
Lecture 2: Relational Databases and Intro to Data Warehousing. Learning outcomes: L01, L02 |
|
Week 3 |
Tutorial |
Tute 2: ER Modelling and SQL. Learning outcomes: L01, L02 |
Lecture |
Lecture 3: Dimensional Modelling. Learning outcomes: L01, L02 |
|
Week 4 |
Tutorial |
Tute 3: Dimensional Modelling and ETL Introduction. Learning outcomes: L01, L02 |
Lecture |
Lecture 4: Advanced Data Warehousing Topics. Learning outcomes: L03 |
|
Week 5 |
Tutorial |
Tute 4: ETL: Data Integration I. Learning outcomes: L01, L04 |
Lecture |
Lecture 5: Data Integration and Metadata. Learning outcomes: L01, L04 |
|
Week 6 |
Tutorial |
Tute 5: ETL Data Integration II. Learning outcomes: L03, L04 |
Lecture |
Lecture 6: Performance Dashboards and Information Delivery. Learning outcomes: L01, L02 |
|
Week 7 |
Tutorial |
Tute 6: Microsoft Power BI: I. Learning outcomes: L01, L02, L04 |
Lecture |
Lecture 7: Data Analytics - Data Exploration and Fundamental Concepts. Learning outcomes: L01, L02, L04 |
|
Week 8 |
Tutorial |
Tute 7: Microsoft Power BI: II. Friday 18th April - Public Holiday - Classes falling on a public holiday may be rescheduled or the recording of the lecture will be provided. Learning outcomes: L01, L02 |
Lecture |
Lecture 8: Data Analytics - Model Building and Algorithms. Friday 18th April - Public Holiday - Classes falling on a public holiday may be rescheduled or the recording of the lecture will be provided. Learning outcomes: L01, L03 |
|
Mid-sem break |
No student involvement (Breaks, information) |
In-Semester Break. |
Week 9 |
Tutorial |
Tute 8: Rapid Minner: Supervised Models. Learning outcomes: L03 |
Lecture |
Lecture 9: Data Analytics - Model Building and Best Practices. Learning outcomes: L03 |
|
Week 10 |
Tutorial |
Tute 9: Rapid Minder: Supervised and Unsupervised Models. Labour Day Public Holiday - Monday 5 May 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L01, L02, L03, L04 |
Lecture |
Lecture 10: Big Data Management and Advanced Analytics. Labour Day Public Holiday - Monday 5 May 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L03 |
|
Week 11 |
Tutorial |
Tute 10: Unsupervised Models and Intro to Data Challenge. Learning outcomes: L01, L02, L03, L04 |
Lecture |
Lecture 11: Privacy, Ethics, and Acceptable Data Use. This lecture is about big data including its definitions, its transformational impact and how it could be modelled and managed. NoSQL will be introduced as a frequently used approach to manage big data. Learning outcomes: L01, L03, L05 |
|
Week 12 |
Tutorial |
Tutorial 11: Data Challenge (Bring Your Data). Learning outcomes: L01, L02, L03 |
Lecture |
Lecture 12: Advanced Topics, Discussions, and Debates in Data Analytics. Can be a Guest Lecture. Learning outcomes: L01, L03, L05 |
|
Week 13 |
Tutorial |
No Tutorial. Learning outcomes: L01, L03 |
Lecture |
Lecture 13: Course Revision and Q&A Session. This lecture discusses unintended and potentially unethical consequences of big data analytics. Scenarios of unethical data use are showed and debated in the class. Learning outcomes: L01, L02, L03, L04, L05 |
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.
School guidelines
Your school has additional guidelines you'll need to follow for this course:
- Business School site
- Exams and assessment advice
- Program and course advice
Course guidelines
- The assignment task has been designed to be challenging, authentic, and complex. Successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. Students are required to demonstrate detailed comprehension of their written submissions without using any Generative AI tools. For all assessment tasks, the use of AI technologies to generate text or solutions to the task is strictly prohibited and will constitute student misconduct under the Student Code of Conduct.
- Before every lecture, reading materials or articles related to the lectures will be provided. As a preparation for the lectures, students are advised to consult these materials. Lecture slides will be provided only after each lecture.