BAN211 - Business Analytics
Credit points: 15 credit points
Workload: 36 hours
Prerequisite: BB108 Business Statistics
Co-requisite: N/A
Aims & objectives
This is a second-year Core Unit in the Bachelor of Business, Major in Business Analytics, and offered as a recommended Elective Unit from another specialisation stream in the Bachelor of Business, subject to meeting pre- and co-requisites. For Course Learning Outcomes and further information relating to Bachelor of Business programs, please visit our website: http://www.mit.edu.au/study-with-us/programs/bachelor-business.
This unit introduces the concepts, fundamentals, and tools of business analytics. Business analytics is a powerful skill for business, data, and IT graduates in today’s job market. Business analytics includes high-level statistical analysis and other quantitative techniques to derive meaning from data to make informed decisions. Students will develop the analytical skills that are in high demand in businesses today and gain analytical knowledge to extract useful hidden information and patterns from data for making better business decisions. Students will also find practical guidance for developing analytic thinking, operationalising Big Data in global environments, and preparing for future analytical innovations.
Unit topics include:
- Data and Business Analytics in Organisational Decision Making
- Organizational, Legal, Cultural and Ethical Considerations of Data and Analytics
- Preparing to Work with Data and Big Data
- Collecting, Sorting, Prioritizing, and Storing Big Data
- Analysis Fundamentals
- Communicating Analytical Results
- Marketing, and HR/People Analytics
- Operational Analytics
- Financial Analytics
- The Future of Big Data and Analytics
Learning outcomes
-
Course learning outcomes
- The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology's website: www.mit.edu.au
-
Unit learning outcomes
- Explain the importance of data, ethical considerations and security of data driven approach to solve business problems.
- Apply statistical and quantitative methods to analyse business performance.
- Interpret the foundations of business analytics in the data generation process.
- Design raw data into relevant information for management decisions.
- Implement contemporary data analytical tools (such as MS Excel, R, Power BI etc.) to predict future outcomes and prescribe appropriate management action.
Assessment
Assessment Task | Due Date | A | B | Learning Outcomes Assessed |
---|---|---|---|---|
1. Assessment Task 1: Opinion paper | Week 3 | - | 10% | a |
2. Assessment Task 2: Consultancy Report (individual) | Week 8 | 30% | - | a-d |
3. Assessment Task 3: Contribution and Participation | Week 12 | - | 10% | a-e |
4. Assessment Task 4 (Group) – Project Report (3000 words) (30%) and Presentation (20%) | Week 12 | 30% | 20% | a-e |
TOTALS | 60% | 40% |
Task Type: Type A: unsupervised, Type B: supervised.
Contribution and participation (10%)
This unit has class participation as an assessment. The assessment task and marking rubric will follow the Guidelines on Assessing Class Participation (https://www.mit.edu.au/about-us/governance/institute-rules- policies-and-plans/policies-procedures-and-guidelines/Guidelines_on_Assessing_Class_Participation).
Further details on the type of assessment tasks and the marking rubrics will be provided in the assessment specification
Textbook and reference materials
Note: Students are required to purchase the prescribed textbook and have it available each week in class.
Prescribed textbook
- Gordon M. E. (2023), Business Analytics: combining Data, Analysis and Judgement to Inform Decisions, SAGE Publishing 2023.
Recommended texts
- Min, H (2016), Global Business Analytics Models: Concepts and Applications in Predictive, Healthcare, Supply Chain, and Finance Analytics, 1st edition, Pearson FT Press (March 5th 2016).
- Quinto, Butch. (2018). Next-Generation Big Data: A Practical Guide to Apache Kudu, Impala, and Spark . Berkeley, CA: Apress L. P. https://www.oreilly.com/library/view/next-generation-big-data/9781484231470/?sso_link=yes&sso_link_from=FederationUniversity
- Camm, J. D., Cochran, J. J., Fry, M. J., & Ohlmann, J. W. (2020). Business analytics. Cengage AU.
Journal articles
- Chen, et al. (2023), Reconciling business analytics with graphically initialized subspace clustering for optimal nonlinear pricing. European Journal of Operational Research. Feb 2024, Vol. 312 Issue 3, p1086-1107.
- Acito, F., & Khatri, V. (2014). Business analytics: Why now and what next?. Business Horizons, 57(5), 565-570.
- Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision support systems, 64, 130-141.
- Seddon, P. B., Constantinidis, D., Tamm, T., & Dod, H. (2017). How does business analytics contribute to business value? Information Systems Journal, 27(3), 237-269.
The Referencing style for this unit is APA:
See the MIT Library Referencing webpage: https://library.mit.edu.au/referencing/APA and the Unit Moodle page for additional referencing support material and web links.
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. |