MA609 - Business Analytics and Data Intelligence

Credit Points: 15 credit points

Workload: 36 hours

Prerequisite: MA508 Business Statistics

Co-requisite: N/A

Aims & Objectives

This is a Year 2, Core Unit in the Master of Professional Accounting. For Course Learning Outcomes and further information relating to the Master of Professional Accounting program please visit our website:  http://www.mit.edu.au/study-with-us/programs/master-professional-accounting.

This unit develops student’s knowledge and skills for analysing data to improve business decision making. Specifically, students will use linear programming, graphical description of linear programming, sensitivity analysis, network model, data visualization and data mining techniques including clustering and classification techniques. These techniques also will allow students to identify relationships and trends in the data. In addition, students will gain knowledge and skills to understand and apply linear programming. Finally, since business analytics is about making better decisions, students also will learn how decision analysis can be used to develop an optimal decision. Topics to be discussed under decision analysis include payoff tables, decision trees and sensitivity analysis. The unit takes a practical approach to decision making.

This unit will cover the following topics:

  • Business and data Intelligence
  • Linear programming
  • Sensitivity analysis
  • Data visualisation
  • Distribution and network models 
  • Clustering and classification techniques
  • Decision analysis for optimal strategy

Learning Outcomes

Course Learning Outcomes

The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology’s website:  http://www.mit.edu.au/study-with-us/programs/master-professional-accounting.

Unit Learning Outcome

At the completion of this unit students should be able to:
a. Demonstrate advanced and integrated understanding of business and data Intelligence for organisational decision-making.
b. Analyse critically, reflect on and synthesise techniques of data visualisation and data mining.
c. Demonstrate advanced and integrated understanding of business analytical models. 
d. Critically analyse, synthesise and reflect on decision analysis techniques to develop optimal strategy.

Assessment

Assessment Task Due Date A B  Unit Learning Outcomes
1. Contribution and Participation Weeks 1 - 12 - 6% a-d
2. Formative Assessment Week 3 - 4% a
3. Assignment [Individual] Week 7 20% - a-b
4. Project [Group] Week 11 20% - a-d
5. Project Presentation [Individual] Week 11 - 10% b-c
6. Case Study Analysis [Individual] (3 hours) TBA - 40% a-d
TOTALS   40% 60% 100%

Task Type: Type A: unsupervised, Type B: supervised.

Contribution and Participation (6%)

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-rulespolicies-and-plans/policies-procedures-and-guidelines/Guidelines_on_Assessing_Class_Participation). Further details will be provided in the assessment specification on the type of assessment tasks and the marking rubrics.

Teaching Methods

NOTE: All School of Business units 3-hour workshops Flipped Classroom Mode. 

Textbook and Reference Materials

Note: Students are required to purchase the prescribed textbook and have it available each week in class.

Copies of the textbook are available in the MIT Library.

Prescribed Text

  • Anderson, D.R., Sweeney, D.J., Williams, T.A., Jeffrey, D.C., Cochran, J.J., Fry, M.J., Ohlman, J.W. (2019) An Introduction to Management Science, Quantitative Approaches to Decision Making, 14th ed. Cengage Learning.  

Other recommended references

  • Camm, J. D., Cochran, J. J., Fry, M. J., Ohlmann, J. W., Anderson, D. R., Sweeney, D. J., and Williams, T. A.  (2019). Business Analytics, 3rd Edition. South Melbourne: Cengage Learning Australia.
  • Selvanathan, A., et al. (2014) Business statistics, Abridged, Edition 6, Cengage Learning. 
  • Ragsdale, C.T., (2018) Spreadsheet modeling and decision analysis: A practical introduction to business analytics, 8th ed. South Melbourne: Cengage Learning Australia.
  • Winston, W. L. and Albright, S. C. (2016). Data Analysis and Decision Making, 6E. South Melbourne: Cengage Learning Australia.
  • Anderson, D. R. Sweeney, D. J., Williams, T. A., Camm, J. D., Cochran, J. J., ; Fry, M. J., and Ohlmann, J. W. (2016). Quantitative Methods for Business (13th Edition). Cengage Learning.

Journals and Business publications

  • Sun, Z., Sun, L., and Strang, K. (2018). Big Data Analytics Services for Enhancing Business Intelligence. Journal Of Computer Information Systems, 58(2), 162-169. doi:10.1080/08874417.2016.1220239
  • Heller, M. (2017). 10 hot data analytics trends and 5 going cold: Big data, machine learning, data science - the data analytics revolution is evolving rapidly. Cio, 9-15
  • Calof, J., Richards, G., and Santilli, P. (2017). Integration of business intelligence with corporate strategic management. Journal of Intelligence Studies in Business, 7(3), 62-73.

Check the unit Moodle page for additional recommended readings throughout the trimester.

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 weblinks.

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.