MBA522 - Business Intelligence
Credit Points: 20 credit points
Workload: 60 hours
Prerequisite: MBA512 Business Data Analysis
Co-requisite: N/A
Aims & Objectives
This is a core unit out of a total of 12 units in the Master of Business Analytics (MBA). This unit addresses the course learning outcomes and complements other courses in a related field by developing students’ specialised knowledge of business intelligence and its applications in the business context. For further course information refer to: https://www.mit.edu.au/study-with-us/programs/master-business-analytics. This unit is part of the AQF level 9 course.
This unit takes a managerial approach to Business Intelligence (BI), emphasising the applications and implementations behind the concepts. This approach allows students to understand how BI works in a way that will help them adopt these technologies in future managerial roles. Students will gain understanding of capabilities of BI, its cost and justification, and the innovative ways real corporations are using BI in their operations. Students will explore the descriptive, predictive, and prescriptive business analytics with an application focus in many industries. Students will have an opportunity to explore extensive yet vivid examples from large corporations, small businesses, and government and not-for-profit agencies make the difficult BI concepts more accessible and relevant.
This unit will cover the following topics:
- An overview of Business Intelligence (BI)
- Nature of data, statistical modelling, and visualization
- BI and data warehousing
- Data mining process, methods, and algorithms
- Text, web, and social media analytics
- Optimization and simulation using linear programming technique
- Big data tools and platforms
- Future Trends, Privacy and Managerial Considerations in Analytics
Learning Outcomes
4.1 Course Learning Outcomes
The Course learning outcomes applicable to this unit in respect of the course being studied are listed on the Melbourne Institute of Technology website: www.mit.edu.au
4.2 Unit Learning Outcomes
At the completion of this unit students should be able to:
- Introduce Business Intelligence (BI) as a broad category of applications and technologies for gathering, storing, analysing and providing access to data to help organisations to make better decisions.
- Appraise and apply the descriptive, predictive, and prescriptive business analytics with an application focus in many industries.
- Devise a linear program for optimisation and simulation.
- Evaluate the importance and implementation of big data tools and platforms.
- Explore the future trends, privacy and managerial considerations in analytics.
Weekly Topics
This unit will cover the content below:
Week | Topics |
---|---|
1 | Introduction to Business Intelligence (BI) |
2 | Nature of data, statistical modelling, and visualization |
3 | BI and data warehousing |
4 | Data mining process, methods, and algorithms |
5 | Text, web, and social media analytics |
6 | Optimization and simulation using linear programming technique |
7 | Big data tools and platforms |
8 | Location analytics |
9 | Internet of things |
10 | Cloud-based analytics |
11 | Privacy and ethical considerations in analytics |
12 | Review |
Assessment
Assessment Task | Due Date | Release Date | A | B | Learning Outcomes Assessed |
---|---|---|---|---|---|
Assignment 1 | Week 3 | Week 1 | 10% | b | |
In-class test | Week 6 | 10% | a-b | ||
Assignment 2 | Week 11 | Week 7 | 30% | c-d | |
Laboratory and Problem Based Learning participation & submission | Week 2-11 | Week 2-11 | 10% | a-e | |
Final Examination (3 hours) | 40% | a-e | |||
TOTALS | 40% | 50% |
Task Type: Type A: unsupervised, Type B: supervised.
Contribution and participation (in class) (10%)
Students are expected to attend each scheduled session, arrive on time and remain for the entire session. Adherence to this requirement will be reflected in the marks awarded for this assessment. Students are also strongly encouraged to actively participate in the class discussions and tutorial activities by answering questions, expressing their opinions, insights and their learnings from the course.
Presentations (if applicable)
For presentations conducted in class, students are required to wear business attire.
Textbook and Reference Materials
O’Reilly eBooks:
- Nokeri T.C., Data Science Revealed: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning (2021), Apress. (Chapters 1,5,8,10) Available at: https://learning.oreilly.com/library/view/data-science-revealed/9781484268704/
- Muro, A.D, Data Analytics Made Easy (2021), Packt publishing. (Chapters 1,4,6,7). Available at: https://learning.oreilly.com/library/view/data-analytics-made/9781801074155/
Textbook:
- Sharda Ramesh, Business Intelligence: A Managerial Approach, Global Edition, Pearson Education Limited, 4th Ed., 2017.
Recommended Readings:
- Ramesh Sharda, Dursun Delen and Efraim Turban, Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support, 11th ed., Pearson, 2020.
Graduate Attributes
MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:
Ability to communicate | Independent and Lifelong Learning | Ethics | Analytical and Problem Solving | Cultural and Global Awareness | Team work | Specialist knowledge of a field of study |
---|---|---|---|---|---|---|
Legend
Levels of attainment | Extent covered |
---|---|
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations. | |
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations. | |
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations. | |
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials. | |
The attribute is not considered, there is no theory or practice or activities associated with this attribute. |