MBA692 - Business Analytics Capstone Project

Credit Points: 20 credit points

Workload: 24 hours

Prerequisite: MBA691 Research Project for Analytic Professionals

Co-requisite: MDA611 Predictive Analytics

Aims & Objectives

This is a core unit out of a total of 12 units in the Master of Business Analytics (MBAnalytics). This unit addresses the MBAnalytics course learning outcomes and complements other units in a related field by developing students’ specialised knowledge in successfully completing a capstone project and applying critical skills in data analytics and ICT technologies. For further course information refer to: This unit is part of the AQF level 9 course.

Each project will be co-supervised by an industry supervisor and an MIT supervisor who has expertise in the field of the capstone project. This unit provides students with the experience of completing a research or industry-related capstone project in a team environment. Unlike lecture-oriented units, the capstone project is an opportunity to tackle complex problems using already acquired and developing technical and creative skills; this will often require the generation and evaluation of complex ideas at an abstract level before exploring concrete solutions. It is expected that the teams will normally continue the work from the project specification and project plan developed in MBA691 as well as the application of knowledge and skills gathered throughout the course in designing, developing, and testing a project solution. Students will meet with their project supervisors weekly. The teams will continue the detailed design, implementation, testing, and evaluation of a substantial project. Students learn to work in a group while maintaining personal autonomy and accountability for their contributions to the team.

Learning Outcomes

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:

Unit Learning Outcomes

At the completion of this unit, students will be able to:

  • Demonstrate the ability to apply cognitive, technical and creative skills to conceptualise, research, design, plan and execute a substantial capstone project
  • Adapt and apply the knowledge and skills acquired over the core units of the course in planning and executing a capstone project in an area related to data analytics.
  • Communicate the research background, design, implementation, results and conclusions to specialist and non‐specialist audience.
  • Through written reflective journals and project reports, demonstrate communication and technical research skills to justify and interpret problems, methodologies, conclusions and professional decisions.
  • Demonstrate the application of knowledge and skills with a high level of personal autonomy and accountability while being part of a team‐based working environment.


Assessment Task* Due Date Weight Learning Outcomes Assessed
Assignment 1: Group Detailed Design Report & Individual Report Week 4 10% a, b, e
Assignment 2: Group Implementation and Evaluation Report & Individual Report Week 11 50% a, b, e
Assignment 3: Individual report and Peer evaluation of contributions of team Week 12 30% c
Assignment 4: Group presentations Week 7, 11 10% d, e
TOTAL   100%  

* Within a group task, several subtasks will be respectively assigned to individual members. This, along with an individual’s contribution to the project, will be used to assess individual outcomes.

Contribution and participation (in class and meetings)
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 aligned with the course learning outcomes and the unit learning outcomes 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

There is no textbook. References and/or notes will be provided as required.

Adopted Reference Style: IEEE


  • IEEE Transactions on Neural Networks and Learning Systems
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Foundations and Trends in Machine Learning
  • Journal of Machine Learning Research
  • Artificial Intelligence
  • International Journal of Machine Learning and Cybernetics
  • International Journal of Artificial Intelligence
  • Intelligent Data Analysis
  • Applied Artificial Intelligence
  • Journal of Experimental and Theoretical Artificial Intelligence
  • Journal of Artificial Intelligence

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


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.