Course coordinator
Dr Avijit Sengupta
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
Data Analytics and Information Management 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 used to create timely and meaningful insights from organisational data. The insights improve managerial decision-making and help to create significant business value.
This course explores the technical and managerial processes of using data for decision-making. Technical topics include dimensional modelling, Extract-Transform-Load (ETL), digital dashboards, regressions and decision trees. Managerial topics include data-driven transformations, data monetisation and appropriate use of data.
The course provides fundamental knowledge and skills necessary to model, integrate, analyse and visualise data. These analytical skills are highly sought after in industry. As such, this course covers relevant theories as well as providing hands-on experience with business analytics tools.
Course requirements
Assumed background
ﾠ
Prerequisites
You'll need to complete the following courses before enrolling in this one:
BISM2207
Incompatible
You can't enrol in this course if you've already completed the following:
MGTS2202 or BISM7233 or INFS7233
Course contact
Course staff
Lecturer
Dr Avijit Sengupta
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 introduceﾠstudents to Data Analytics concepts and techniques that are used to improve organisational decision-making. Students will also develop knowledge and hands-on skills to manage, visualise and analyse 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 as an individual 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, Paper/ Report/ Annotation, Practical/ Demonstration | Data Visualisation Using Microsoft PowerBI | 20% |
24/09/2025 2:00 pm |
| Computer Code, Paper/ Report/ Annotation | Business Analytics Case Study | 40% |
27/10/2025 2:00 pm |
| Examination |
Final Course Exam
|
40% |
End of Semester Exam Period 8/11/2025 - 22/11/2025 |
Assessment details
Data Visualisation Using Microsoft PowerBI
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Paper/ Report/ Annotation, Practical/ Demonstration
- Weight
- 20%
- Due date
24/09/2025 2:00 pm
- Learning outcomes
- L01, L03, L04
Task description
Using Microsoft PowerBI Students will be expected to:
- work with a given data set
- explore the given data set
- clean the data, rearrange it, transform it if required
- create multiple visualisations using Microsoft PowerBI to develop interesting insights
- document their insights generated form the visualisations and report them
- critically analyse those insights and recommend related actions or interventions
AI Statement:
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
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.
Business Analytics Case Study
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation
- Weight
- 40%
- Due date
27/10/2025 2:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
- You will complete a report that requires you to undertake research in order to prepare a 1400-1500 word report.
- You will be presented with a business analytics case with diverse stakeholder requests. You are tasked with developing recommendations for multiple business analytics stages. Full details of the requirements for the written report will be provided with the release of the Project assessment document.
AI Statement:
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance.
A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Report and other outputs are 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
8/11/2025 - 22/11/2025
- Other conditions
- Time limited, Secure.
- 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.
AI Statement
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
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 may be able to defer this exam.
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
Library resources are available 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 |
Week 2 |
Tutorial |
Tute 1: Introduction to Business Analytics. Learning outcomes: L01 |
Lecture |
Lecture 2: Relational Databases and Normalisation 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 Learning outcomes: L01, L02 |
Lecture |
Lecture 4: Advanced Dimensional Modelling Learning outcomes: L01, L02 |
|
Week 5 |
Tutorial |
Tute 4: Dashboards with Microsoft Power BI Learning outcomes: L01, L04 |
Lecture |
Lecture 5: Performance Dashboards and Information Delivery Learning outcomes: L03, L04 |
|
Week 6 |
Tutorial |
Tute 5: Building Report with Power BI Learning outcomes: L03, L04 |
Lecture |
Lecture 6: Data Integration and Metadata Learning outcomes: L01, L02 |
|
Week 7 |
Tutorial |
Tute 6: ETL with SQL Server Integration Services Learning outcomes: L02, L03, L04 |
Lecture |
Lecture 7: Data Mining Process and Predictive Analytics Learning outcomes: L01, L02, L04, L05 |
|
Week 8 |
Tutorial |
Tute 7: Slowly Changing Dimensions(SCDs)SSIS Learning outcomes: L01, L02 |
Lecture |
Lecture 8: Data Analytics - Classification and Clustering Learning outcomes: L03 |
|
Week 9 |
Tutorial |
Tute 8: Predicting with Linear Regression Learning outcomes: L01, L02 |
Lecture |
Lecture 9: Big Data Management Learning outcomes: L01, L02, L03, L04 |
|
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break. |
Week 10 |
Tutorial |
Tute 9: Classification with RapidMiner Kings Birthday Public Holiday - Monday 6 October 2025 - Check Blackboard for announcements about affected classes. Learning outcomes: L01, L02, L03, L04 |
Lecture |
Lecture 10: Advanced Analytics Learning outcomes: L01, L02 |
|
Week 11 |
Tutorial |
Tute 10: Clustering with RapidMiner Learning outcomes: L01, L02, L03 |
Lecture |
Lecture 11: Privacy, Ethics, and Acceptable Data Use. Learning outcomes: L01, L03 |
|
Week 12 |
Tutorial |
Tutorial 11: No Tutorial. This is for practice and preparing for the job market. More information followed! Learning outcomes: L01, L03 |
Lecture |
Lecture 12: Advanced Topics, Discussions, and Debates in Data Analytics. Learning outcomes: L05 |
|
Week 13 |
No student involvement (Breaks, information) |
No Tutorial. |
Lecture |
Lecture 13: Course Revision and Q&A Session. 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 for Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.