Course coordinator
Dr Avijit Sengupta
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
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
This course aims to develop student's technical competencies for using the tools and techniques related to data integration, visualization, 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:
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 |
|---|---|---|---|
| Examination |
In-Semester Exam
|
40% |
6/09/2024
During Class |
| Paper/ Report/ Annotation | Business Analytics Case Study | 60% |
25/10/2024 3:00 pm |
Assessment details
In-Semester Exam
- Online
- Mode
- Written
- Category
- Examination
- Weight
- 40%
- Due date
6/09/2024
During Class
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L04
Task description
- The exam will be available for 90 minutes.
- 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.
- Dimensional modelling, ETL and Visualisation will be assessed.
- Submit via Blackboard
- 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.
Business Analytics Case Study
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 60%
- Due date
25/10/2024 3:00 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 2000-2500 word report.
- You will be presented a business analytics case with diverse stakeholder requests. You are tasked with developing recommendations for multiple business analytics stages: identification of sources and data collection, explaining data extraction and transformation concerns, offer guidelines for metadata management, apply data warehousing concepts and recommend data mart designs, as well as outline considerations for the preparation and design of reports.
- 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 Learning outcomes: L01 |
Week 2 |
Tutorial |
Tutorial 1: Introduction to Business Analytics Learning outcomes: L01 |
Lecture |
Relational Databases and Normalisation Learning outcomes: L01, L02 |
|
Week 3 |
Tutorial |
Tutorial 2: ER Modelling and SQL Learning outcomes: L01, L02 |
Lecture |
Dimensional Modelling Learning outcomes: L01, L02 |
|
Week 4 |
Tutorial |
Tutorial 3: Dimensional Modelling Learning outcomes: L01, L02 |
Lecture |
Advanced Dimensional Modeling Learning outcomes: L01, L02 |
|
Week 5 |
Tutorial |
Tutorial 4: Dashboards with Microsoft Power BI Learning outcomes: L01, L04 |
Lecture |
Performance Dashboards and Information Delivery Learning outcomes: L03, L04 |
|
Week 6 |
Tutorial |
Tutorial 5: Building Report with Power BI Learning outcomes: L03, L04 |
Lecture |
Data Integration and Metadata Learning outcomes: L01, L02 |
|
Week 7 |
Information technology session |
Doubt Clearing for Tutorial Contents No Tutorials |
Lecture |
In-Semester Exam During Class Learning outcomes: L01, L02, L04, L05 |
|
Week 8 |
Tutorial |
Tutorial 6: ETL with SQL Server Integration Services Learning outcomes: L01, L02 |
Lecture |
Data Mining Process and Predictive Analytics Learning outcomes: L03 |
|
Week 9 |
Tutorial |
Tutorial 7: Slowly Changing Dimensions(SCDs)SSIS Learning outcomes: L01, L02 |
Lecture |
Data Analytics - Classification and Clustering Learning outcomes: L01, L02, L03, L04 |
|
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 10 |
Tutorial |
Tutorial 8: Predicting with Linear Regression Learning outcomes: L01, L02, L03, L04 |
Lecture |
Big Data Management Learning outcomes: L01, L02 |
|
Week 11 |
Tutorial |
Tutorial 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 Learning outcomes: L01, L03 |
|
Week 12 |
Tutorial |
Tutorial 10: Clustering with RapidMiner Learning outcomes: L01, L03 |
Lecture |
Privacy, Ethics and Acceptable Data Use Learning outcomes: L05 |
|
Week 13 |
No student involvement (Breaks, information) |
No Tutorial |
Lecture |
Course Revision and Assignment Q&A 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.