Course coordinator
Associate Professor Ida Asadi Someh
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Postgraduate Coursework
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
Business Analytics Foundations concerns the organisations' use of digital data assets for strategic value creation. It provides detailed and systematic insights into the process of value creation from data: formulating analytics projects, amassing and curating data, generating and communicating insights, automating analytical insights and taking competitive actions, and finally closing the loop by measuring data's strategic value.
This is the first course offered as part of the UQ's Master of Business Analytics program. It uses an interdisciplinary approach that merges business and IT to teach how data and analytics could be used for value creation for a variety of stakeholders. The course promotes three unique approaches for analytics development and use: (1) a product development mindset to develop analytics solutions that are adopted and pervasively used by different stakeholders, (2) a multidisciplinary team approach that merges skills from different domain areas, and (3) a responsible data use approach to make sure analytics solutions benefit rather than harming stakeholders.
The course is divided into six modules:
- Winning the Data Race
- Business Purpose
- Data
- Insight and Action
- Value
- Becoming Data-Savvy
This course offers a fully online student experience that engages students through interactive content built into a learning platform. The content engagement is further enhanced with live sessions, coding bootcamps, and debates on a social platform. Every module will have live case analysis sessions, for which students need to prepare their case analysis individually and engage in critical discussions with their peers. To build programming skills, the course offers coding bootcamps focused on data exploration, transformation and insight generation. All the live sessions will be recorded and provided to students to enable flexible learning.
The course also has a focus on using a team-based approach to performing analytical tasks that encourages sharing and peer learning.
Course requirements
Incompatible
You can't enrol in this course if you've already completed the following:
DATA2001 or 7001
Restrictions
Restricted to students in the MBusAn program
Course contact
Course staff
Lecturer
Associate Professor Ida Asadi Someh
Professor Shazia Sadiq
Dr Xin Yu
Tutor
Mr Jing Long
Mrs Dayaneth Vargas
Teaching assistant
Mr Hrishi Patel
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 business analytics concepts, techniques and development methodologies. Students will develop deep business knowledge on how analytics is changing the nature of work, together with hands-on programming and visualization skills to explore and transform data as well as generate actionable insights from it.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Evaluate a business analytics problem as a team using design thinking approaches
LO2.
Develop insights for stakeholders by applying business analytic tools and approaches
LO3.
Reflect on how Business Analytics frameworks enable problem solving and value creation
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Product/ Design |
Design Thinking Output
|
20% |
21/03/2025 5:00 pm |
| Project | Exploring and Transforming Data | 30% |
2/05/2025 5:00 pm |
| Project | Business Analytics Solution | 50% |
13/06/2025 5:00 pm |
Assessment details
Design Thinking Output
- Team or group-based
- Mode
- Product/ Artefact/ Multimedia
- Category
- Product/ Design
- Weight
- 20%
- Due date
21/03/2025 5:00 pm
- Other conditions
- Peer assessed.
- Learning outcomes
- L01
Task description
In this assessment, students will be using design-thinking tools to propose an analytics solution in an education context. Student teams are commissioned by the Brisbane-based analytics firm Aginic to develop this solution for the Edenglassie Department of Education: students are asked to investigate the role of data in transforming the education field and, accordingly, develop a business case and an initial prototype for an analytics solution that could enable teachers and corporate staff to make data-driven decisions that improve students’ learning experience.
This assessment is group-based. Students are supported by an online white board that enables asynchronous contributions from each team member.
Students will be required to complete a self and peer evaluation as part of this assignment. A series of review questions will be available on Blackboard and should be completed on submission of your teams manual. The evaluation process assists the teaching team to understand how each student contributed and the findings are confidential. The peer evaluation can affect each student's mark for this assignment. Further details will be provided in class and on Blackboard. This assessment builds student employability by providing an opportunity to access knowledge, think critically, problem solve and work in a team.
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
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.
Exploring and Transforming Data
- Mode
- Product/ Artefact/ Multimedia
- Category
- Project
- Weight
- 30%
- Due date
2/05/2025 5:00 pm
- Learning outcomes
- L02
Task description
In this assignment, students will enter the delivery phase of developing their analytics solution, by exploring and preparing/processing data available from the Department of Education. They will need to engage with the Data module and use their knowledge of data exploration and transformation to make the dataset fit-for-use in the process of building their analytics solution. The final dataset needs to meet stakeholder needs and support the prototype proposed in the first assignment.
Students are provided with several tables by Aginic pertaining to students’ assessment, attendance, and activities. They will need to explore the dataset and identify as many of the possible data quality issues as you can, then perform transformations to address the issues they have identified. Finally, they also need to justify why your transformed dataset is fit-for-use. This assessment builds your employability by developing your coding and data exploration skills.
AI Statement:
This assessment task evaluates students' abilities, skills and knowledge without the aid of 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
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 Solution
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Project
- Weight
- 50%
- Due date
13/06/2025 5:00 pm
- Learning outcomes
- L02, L03
Task description
In this assignment, students will deliver a business analytics solution – a digital dashboard. They need to build on the prototype they had proposed for Assessment 1 and use the curated data that was prepared for Assessment 2. The business-analytics solution must respond to the information needs specific to the categories of users represented and must enable users to perform a range of dimensional analyses of data (drilling down, rolling up, etc.).
For the delivery of the business analytics solution, students are asked to showcase how their solution can benefit the users too. Therefore, they have to generate insight via the data, engage in storytelling with analytical insights and propose actions, and close by reflecting on the solution’s value. This assessment builds your employability by developing your skills in designing digital platforms as a visual technique to communicate solutions to industry.
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
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.
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 |
Not Timetabled |
Topic 1.1: The Data Race Framework Self-Directed Learning: This topic builds on a practical framework called Data Race to explain how businesses can create value from their digital data assets. Learning outcomes: L01 |
Seminar |
Live welcome session and course introduction Learning outcomes: L01 |
|
Week 2 |
Not Timetabled |
Topic 2.1: Business Purpose Self-Directed Learning: This topic focuses on how companies can formulate analytics business cases, using a user-centric approach. Learning outcomes: L01 |
Seminar |
Live Case Study Analysis This case analysis session focuses on the journey of IDEO, a design thinking firm, and how their approaches can be used to develop user-centric analytics solutions. Learning outcomes: L01 |
|
Week 3 |
Not Timetabled |
Topic 2.1: Business Purpose (continued) Self-Directed Learning: This topic focuses on how companies can formulate analytics business cases, using a user-centric approach. Learning outcomes: L01 |
Seminar |
Assignment-1 Q&A with Mantel The session gives students opportunity to ask assignment-1 data related questions to Aginic employees. Learning outcomes: L01 |
|
Week 4 |
Not Timetabled |
Topic 3.1: Understanding Data Self-Directed Learning: This topic takes a deep dive into understanding data, its evolution, data sources and types, data's quality issues and meta data. Learning outcomes: L02 |
Seminar |
Live Case Study Analysis This session provides a discussion on the cases of Netflix and Walmart and how they explore their data assets to generate value for users. Learning outcomes: L02 |
|
Week 5 |
Not Timetabled |
Topic 3.2: Exploring and Transforming Data Self-Directed Learning: This topic uses a hands-on programming approach to demonstrate how data can be explored and transformed to make it fit for use. Learning outcomes: L02 |
Workshop |
Coding Bootcamp prep Learning outcomes: L01 |
|
Seminar |
Generative AI for coding In this session, students will be introduced to generative AI and how it can be used for coding using prompt engineering. Learning outcomes: L02 |
|
Week 6 |
Workshop |
Coding Bootcamp 1 Learning outcomes: L02 |
Tutorial |
Coding Practice 1a This session is to help you practice your coding skills and troubleshoot any errors. Learning outcomes: L02 |
|
Tutorial |
Coding Practice 1b This session is to help you practice your coding skills and troubleshoot any errors. Learning outcomes: L02 |
|
Week 7 |
Not Timetabled |
Topic 3.3: Transforming Data Self-Directed Learning: Module 3.3 is the final topic for the second step in the Data Race: Data. The focus of this topic is Transforming Data . Learning outcomes: L02 |
Workshop |
Coding Bootcamp 2 Learning outcomes: L02 |
|
Tutorial |
Coding Practice 2a Learning outcomes: L02 |
|
Tutorial |
Coding Practice 2b Learning outcomes: L02 |
|
Week 8 |
Not Timetabled |
Topic 4.1: Performance Management Self-Directed Learning: This topic describes how data and analytics can help companies manage their business performance. Learning outcomes: L02 |
Workshop |
Coding Bootcamp 3 This session introduce students to Python programming and its associated coding environments. Learning outcomes: L02 |
|
Tutorial |
Coding Practice 3a This session focus on data exploration and transformation using Python. Learning outcomes: L02 |
|
Tutorial |
Coding Practice 3b This session is to help you practice your coding skills and troubleshoot any errors. Learning outcomes: L02 |
|
Mid-sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 9 |
Not Timetabled |
Topic 4.2: Introduction to Machine Learning Self-Directed Learning: This topic explains the process of machines learning, together with a number of machine learning algorithms and how they are used in business. Learning outcomes: L02 |
Tutorial |
Assessment 2 Q&A Session 1a This session will help you practice your coding skills and prepare for Assignment 2 submission. Learning outcomes: L02 |
|
Tutorial |
Assessment 2 Q&A Session 1b Learning outcomes: L02 |
|
Week 10 |
Not Timetabled |
Topic 4.3: Visual Storytelling Self-Directed Learning: This topic focuses on how analytics professionals can build narratives using data-driven insights that influence their audience. Learning outcomes: L02 |
Workshop |
Dashboarding workshop 1a This session provides a hands-on experience to build digital dashboards with a popular tool. Learning outcomes: L02 |
|
Workshop |
Dashboarding workshop 1b This session provides a hands-on experience to build digital dashboards with a popular tool. Learning outcomes: L02 |
|
Week 11 |
Seminar |
Live Case Analysis (Performance Management and Dimensional Modelling) Learning outcomes: L02 |
Not Timetabled |
Topic 4.4: Data-driven Action Self-Directed Learning: This topic shows how data-driven insights can inspire competitive action or automation of business processes. Learning outcomes: L02 |
|
Workshop |
Dashboarding workshop 2a Learning outcomes: L02 |
|
Workshop |
Dashboarding workshop 2b Learning outcomes: L02 |
|
Week 12 |
Not Timetabled |
Topic 5.1: Value Self-Directed Learning: This module explains how analytics value can be defined and measured and how its negetive consequences can be avoided. Learning outcomes: L01, L02, L03 |
Workshop |
Dashboarding workshop 3a This session provides a hands-on experience to build digital dashboards with a popular tool. Learning outcomes: L02, L03 |
|
Workshop |
Dashboarding workshop 3b Learning outcomes: L02, L03 |
|
Week 13 |
Not Timetabled |
Topic 6.1: Becoming Data Savvy Self-Directed Learning: In this topic, you will learn about the future of analytics and reflect on your journey on becoming a data savvy business professional. Learning outcomes: L02, L03 |
Tutorial |
Assignment 3 Q&A Session 1a This is a Q&A session for Assignment 3. Learning outcomes: L02, L03 |
|
Tutorial |
Assignment 3 Q&A Session 1b Learning outcomes: L02, L03 |
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.