IAB353 Business Intelligence using Enterprise Systems
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | IAB353 |
|---|---|
| Prerequisite(s): | (IFB104 or ITD104 or EGB103) and (IFB105 or IFB130 or ITD105) or IAB251 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,118 |
| Domestic tuition unit fee | $4,104 |
| International unit fee | $4,788 |
Unit Outline: Semester 2 2024, Gardens Point, Internal
| Unit code: | IAB353 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (IFB104 or ITD104 or EGB103) and (IFB105 or IFB130 or ITD105) or IAB251 |
| Coordinator: | Darshika Koggalahewa | darshika.koggalahewa@qut.edu.au |
Overview
This unit provides knowledge and skills for supporting Business Intelligence(BI) using enterprise systems. BI is a technology-driven process for analysing data and delivering actionable outcomes as part of planning and decision-making tasks undertaken by executives, managers, and workers. It involves data science and machine learning techniques and tools applied to key aspects of businesses including products, services, customers and resources. You will be exposed to the planning, modelling, reporting, and prediction structures underpinning business intelligence. To support this, you will learn, Data preparation, analysis and modelling, predictions, and visualization. In addition, you will be exposed to advanced data analytics capabilities including, real-time analytics and Internet of Things (IoT) analytics. This will be applied through a comprehensive framework that supports data Ops, data security, and governance. The unit provides a rich exposure to real-world BI platforms.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain the concepts and principles of business intelligence (BI) and its strategic importance in modern enterprises.
- Demonstrate advanced techniques and skills in data analytics, visualization, and prediction relevant for enterprise planning and operations.
- Apply Business Intelligence techniques using tools and technologies available through enterprise BI platforms to solve real-world business problems.
- Justify the data governance, ethical and legal considerations for enterprises using Business Intelligence.
Content
The first module introduces you to the fundamentals of Business Intelligence. You will learn about data warehousing and data modelling, data cleaning and integration, data visualization and predictive analytics. A real-world enterprise business intelligence platform and algorithms will be explored to apply BI concepts to enterprise business scenarios.
The second module introduces you to the advance concepts of Business Intelligence using cutting-edge industry technologies and algorithms. You’ll learn the fundamentals of, advance analytics, self-service BI, streaming, and IoT analytics. The focus will be to apply BI concepts to address complex, real-time business problems.
The third module introduces you to the operations, security, and data governance of Enterprise Systems. You’ll learn the fundamentals of, Data Ops, ethical and legal considerations, and ethical decision-making in BI for Enterprise Systems.
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You can expect to spend 10-15 hours per week involved in preparing for and attending scheduled classes, preparing, and completing assessment tasks as well as independent study and consolidation of your learning. The unit uses pre-recorded lectures, case studies, and practical exercises to develop your understanding of the theory and practice of Business Intelligence using Enterprise Systems.
The pre-recorded lectures and online activities will provide you with the knowledge and skills for using Business Intelligence in Enterprise Systems, modelling, analysing, visualizing, and predicting data and insights for organizations.
Tutorials will be conducted in face-to-face computer labs on-campus or online. They will be activity-based involving modelling, analysing, visualizing, and predicting data and insights for large-scale organizations. The tutorials build directly on the material presented in the pre-recorded lectures and will involve detailed instruction sheets for undertaking the required tasks. They are designed to support class instruction, group work, and class reflection.
QUT Canvas site will be used for lecture notes, tutorial materials, reading resources, and online class discussions.
This unit emphasises practical skills and artifact-driven learning. Students actively engage in hands-on exercises, supplemented by readings and discussions from the development community, to gain real-world experience and prepare for future challenges.
Feedback on Learning and Assessment
Students can obtain feedback on their progress throughout the unit through the following mechanisms:
- Class and group-based feedback on workshop exercises
- Written feedback on the formative phase of assessment tasks
- Written feedback on summative phases of assessment tasks including a rubric
- General verbal feedback will be provided to the entire class on assessment tasks
- You will receive feedback and results on each assessment task prior to the submission of the next assessment task
Assessment
Overview
The assessments in this unit have been designed so that you may develop knowledge and skills for applying business intelligence using enterprise systems. Students will develop the skills in planning, modelling, reporting, and predicting structures underpinning business intelligence.
The assessments will be structured through the different perspectives of applying BI in enterprise systems. It includes planning, modelling, designing, and developing a BI solution for a given case organization and working with IoT and real-time data analytics to make predictive insights for a given business problem.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: BI Solution Design and Implementation
For this assignment, students will design and implement a comprehensive BI solution for a given business scenario. The assignment will consist of the following components:
Business Scenario Analysis (5%):
BI Solution Design (15%):
BI Solution Implementation (20%):
Assessment: IoT Data Analytics and Predictive Insights
This assignment allows students to work with real-time IoT data, implementing stream analytics to process the data as it arrives. They will also utilize predictive analytics techniques to make informed predictions based on historical IoT data. By incorporating these practical aspects, students will gain hands-on experience in analysing IoT data, extracting valuable insights, and using predictive models to drive decision-making in real-world scenarios.
Assessment: Online Examination
Based on several real-world problems, and employing technical and business intelligence skills taught in this unit, you will take an online exam.
Timed Online Examination which covers the lectures from weeks 1-13
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
No extraordinary charges or costs are associated with the requirements for this unit. There is no required textbook for this unit. However, this unit may where appropriate, make use of the selected chapters from textbooks, journals, and magazines. Students are encouraged to obtain a copy of these materials from the library. Where possible, materials will be made available online through QUT Readings.
Risk Assessment Statement
There are no out-of-the-ordinary risks associated with studying this unit
Unit Outline: Semester 2 2024, Online
| Unit code: | IAB353 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (IFB104 or ITD104 or EGB103) and (IFB105 or IFB130 or ITD105) or IAB251 |
Overview
This unit provides knowledge and skills for supporting Business Intelligence(BI) using enterprise systems. BI is a technology-driven process for analysing data and delivering actionable outcomes as part of planning and decision-making tasks undertaken by executives, managers, and workers. It involves data science and machine learning techniques and tools applied to key aspects of businesses including products, services, customers and resources. You will be exposed to the planning, modelling, reporting, and prediction structures underpinning business intelligence. To support this, you will learn, Data preparation, analysis and modelling, predictions, and visualization. In addition, you will be exposed to advanced data analytics capabilities including, real-time analytics and Internet of Things (IoT) analytics. This will be applied through a comprehensive framework that supports data Ops, data security, and governance. The unit provides a rich exposure to real-world BI platforms.
Learning Outcomes
On successful completion of this unit you will be able to:
- Explain the concepts and principles of business intelligence (BI) and its strategic importance in modern enterprises.
- Demonstrate advanced techniques and skills in data analytics, visualization, and prediction relevant for enterprise planning and operations.
- Apply Business Intelligence techniques using tools and technologies available through enterprise BI platforms to solve real-world business problems.
- Justify the data governance, ethical and legal considerations for enterprises using Business Intelligence.
Content
The first module introduces you to the fundamentals of Business Intelligence. You will learn about data warehousing and data modelling, data cleaning and integration, data visualization and predictive analytics. A real-world enterprise business intelligence platform and algorithms will be explored to apply BI concepts to enterprise business scenarios.
The second module introduces you to the advance concepts of Business Intelligence using cutting-edge industry technologies and algorithms. You’ll learn the fundamentals of, advance analytics, self-service BI, streaming, and IoT analytics. The focus will be to apply BI concepts to address complex, real-time business problems.
The third module introduces you to the operations, security, and data governance of Enterprise Systems. You’ll learn the fundamentals of, Data Ops, ethical and legal considerations, and ethical decision-making in BI for Enterprise Systems.
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You can expect to spend 10-15 hours per week involved in preparing for and attending scheduled classes, preparing, and completing assessment tasks as well as independent study and consolidation of your learning. The unit uses pre-recorded lectures, case studies, and practical exercises to develop your understanding of the theory and practice of Business Intelligence using Enterprise Systems.
The pre-recorded lectures and online activities will provide you with the knowledge and skills for using Business Intelligence in Enterprise Systems, modelling, analysing, visualizing, and predicting data and insights for organizations.
Tutorials will be conducted in face-to-face computer labs on-campus or online. They will be activity-based involving modelling, analysing, visualizing, and predicting data and insights for large-scale organizations. The tutorials build directly on the material presented in the pre-recorded lectures and will involve detailed instruction sheets for undertaking the required tasks. They are designed to support class instruction, group work, and class reflection.
QUT Canvas site will be used for lecture notes, tutorial materials, reading resources, and online class discussions.
This unit emphasises practical skills and artifact-driven learning. Students actively engage in hands-on exercises, supplemented by readings and discussions from the development community, to gain real-world experience and prepare for future challenges.
Feedback on Learning and Assessment
Students can obtain feedback on their progress throughout the unit through the following mechanisms:
- Class and group-based feedback on workshop exercises
- Written feedback on the formative phase of assessment tasks
- Written feedback on summative phases of assessment tasks including a rubric
- General verbal feedback will be provided to the entire class on assessment tasks
- You will receive feedback and results on each assessment task prior to the submission of the next assessment task
Assessment
Overview
The assessments in this unit have been designed so that you may develop knowledge and skills for applying business intelligence using enterprise systems. Students will develop the skills in planning, modelling, reporting, and predicting structures underpinning business intelligence.
The assessments will be structured through the different perspectives of applying BI in enterprise systems. It includes planning, modelling, designing, and developing a BI solution for a given case organization and working with IoT and real-time data analytics to make predictive insights for a given business problem.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: BI Solution Design and Implementation
For this assignment, students will design and implement a comprehensive BI solution for a given business scenario. The assignment will consist of the following components:
Business Scenario Analysis (5%):
BI Solution Design (15%):
BI Solution Implementation (20%):
Assessment: IoT Data Analytics and Predictive Insights
This assignment allows students to work with real-time IoT data, implementing stream analytics to process the data as it arrives. They will also utilize predictive analytics techniques to make informed predictions based on historical IoT data. By incorporating these practical aspects, students will gain hands-on experience in analysing IoT data, extracting valuable insights, and using predictive models to drive decision-making in real-world scenarios.
Assessment: Online Examination
Based on several real-world problems, and employing technical and business intelligence skills taught in this unit, you will take an online exam.
Timed Online Examination which covers the lectures from weeks 1-13
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
No extraordinary charges or costs are associated with the requirements for this unit. There is no required textbook for this unit. However, this unit may where appropriate, make use of the selected chapters from textbooks, journals, and magazines. Students are encouraged to obtain a copy of these materials from the library. Where possible, materials will be made available online through QUT Readings.
Risk Assessment Statement
There are no out-of-the-ordinary risks associated with studying this unit