Course coordinator
Dr Avijit Sengupta
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- 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 are highly sought after skills in various professions and industries. The course covers relevant theories as well as provides hand-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.
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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 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 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 |
|---|---|---|---|
| Examination |
In-Semester Exam
|
40% |
2/09/2024
During Lecture Time |
| Computer Code, Essay/ Critique, Practical/ Demonstration, Project | Data Visualisation Using Microsoft PowerBI | 20% |
4/10/2024 4:00 pm |
| Paper/ Report/ Annotation | Business Analytics Case Study | 40% |
25/10/2024 4:00 pm |
Assessment details
In-Semester Exam
- Online
- Mode
- Written
- Category
- Examination
- Weight
- 40%
- Due date
2/09/2024
During Lecture Time
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L04
Task description
The exam may consist of multiple choice questions, fill in the blanks, short descriptive questions, scenario-based problem solving questions related to mini-cases provided in this examination.
The exam may include:
- Databases and Dimensional modelling
- ETL
- Visualisation
For MCQ: each question will be marked objectively and can have more than one correct answer.
Fill in the Blanks, Short Descriptive Questions, Scenario-Based Problem Solving Questions Related to Mini-Cases, etc.
Further details about the In-Semester Exam will be discussed in class and posted to our course BB site if required.
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.
Exam details
| Planning time | no planning time minutes |
|---|---|
| Duration | 90 minutes |
| Calculator options | Any calculator permitted |
| Open/closed book | Open Book examination |
| Exam platform | Learn.UQ |
| Invigilation | Not invigilated |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Late submission
Exams submitted after the end of the submission time will incur a late penalty.
Data Visualisation Using Microsoft PowerBI
- Mode
- Product/ Artefact/ Multimedia, Written
- Category
- Computer Code, Essay/ Critique, Practical/ Demonstration, Project
- Weight
- 20%
- Due date
4/10/2024 4:00 pm
- Learning outcomes
- L01, 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:
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
Submit 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.
Business Analytics Case Study
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
25/10/2024 4:00 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 2000 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 appear on Turnitin 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 to be submitted electronically via TurnItIn
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
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 |
Course Overview and Business Analytics Framework This lecture will introduce an overall Business Analytics Framework including its various components. Learning outcomes: L01, L05 |
Week 2 |
Practical |
Practical 1: Introduction to Business Analytics Learning outcomes: L01 |
Lecture |
Relational Databases and Intro to Data Warehousing This lecture is on data modelling. It particularly reviews ER models and shows their limitations for decision making purposes. Learning outcomes: L01, L02 |
|
Week 3 |
Practical |
Practical 2: ER Modelling and SQL Learning outcomes: L01, L02 |
Lecture |
Dimensional Modelling This lecture introduces dimensional modelling as a fundamental decision making technique for business managers. Learning outcomes: L01, L02 |
|
Week 4 |
Practical |
Practical 3: Dimensional Modelling Royal Queensland Show Public Holiday - Wednesday 14 Aug 2024 - Check Blackboard for announcements about affected classes. Learning outcomes: L01, L02 |
Lecture |
Advanced Data Warehousing Topics The content of this week deals with deeper dimensional modelling concepts such as slowly changing dimensions, types of facts and fact tables and mini-dimensions. Royal Queensland Show Public Holiday - Wednesday 14 Aug 2024 - Check Blackboard for announcements about affected classes. Learning outcomes: L03 |
|
Week 5 |
Practical |
Practical 4: Dashboards with Microsoft Power BI Learning outcomes: L01, L04 |
Lecture |
Performance Dashboards and Information Delivery This lecture introduces digital dashboard operators and shows how information can be catered for various roles. It provides heuristics for effective visual design. Learning outcomes: L01, L04 |
|
Week 6 |
Practical |
Practical 5: Building Report with Power BI Learning outcomes: L03, L04 |
Lecture |
Data Integration and Metadata This lecture discusses the Extract, Transform, Load Process and how data transformations can be designed. It also highlights the importance of metadata. Learning outcomes: L01, L02 |
|
Week 7 |
Information technology session |
Doubt Clearing Related to Practical No tutorial this week due to Exam |
Lecture |
In-Semester Exam During Lecture (Online) Learning outcomes: L01, L02, L04 |
|
Week 8 |
Practical |
Practical 6: ETL with SQL Server Integration Services Learning outcomes: L01, L02 |
Lecture |
Data Mining Process and Predictive Analytics This lecture shows how data can be prepared and analysed with predictive algorithms to generate insights about future events or outcomes. Learning outcomes: L01, L03 |
|
Week 9 |
Lecture |
Data Analytics- Classification and Clustering This lecture focuses on classification and clustering algorithms such as logistic regression, decision trees and K-means clustering. Learning outcomes: L01, L02, L03, L04 |
Practical |
Practical 7: Slowly Changing Dimensions(SCDs)SSIS Learning outcomes: L03 |
|
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 10 |
Practical |
Practical 8: Predicting with Linear Regression Learning outcomes: L01, L02, L03, L04 |
Lecture |
Big Data Management 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 11 |
Practical |
Practical 9: Classification with RapidMiner King's Birthday Public Holiday - Monday 7 Oct 2024 - Check Blackboard for announcements about affected classes. Learning outcomes: L01, L02, L03 |
Lecture |
Advanced Analytics Lecture Can be a Guest Lecture. Learning outcomes: L01, L03, L05 |
|
Week 12 |
Practical |
Practical 10: Clustering with RapidMiner Learning outcomes: L01, L03 |
Lecture |
Privacy, Ethics and Acceptable Data Use 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, L05 |
|
Week 13 |
No student involvement (Breaks, information) |
No Tutorial |
Lecture |
Course Revision and Q&A Session The lecture content from Week 1-13 will be reviewed and a Q&A session will be held to answer questions on various course-related topics including assessments. 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.