MDA692 - Data Analytics Capstone Project

Credit Points: 20 credit points

Workload: 60 hours

Prerequisite: MDA691 Project Management and Research Methods

Co-requisite: N/A

Aims & Objectives

This is a core unit out of a total of 12 units in the Master of Data Analytics (MDA). This unit addresses the MDA 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. For further course information refer to: This unit is part of the AQF level 9 (MDA) course.

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 MDA691 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 supervisor 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

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: 

4.2 Unit Learning Outcomes
 At the completion of this unit, students will be able to:
a. Demonstrate the ability to apply cognitive, technical and creative skills to conceptualise, research, design, plan and execute a substantial capstone project.
b. 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.
c. Communicate the research background, design, implementation, results and conclusions to specialist and non-specialist audiences.
d. Through written reflective journals and project reports, demonstrate communication and technical research skills to justify and interpret problems, methodologies, conclusions and professional decisions.
e. 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.

Weekly Topics

There are no lectures in this unit. This is the second of the two core project units. It is expected that student teams will continue from the project requirements developed in the first project unit. In this unit, they will complete the project design, its implementation, testing and evaluation. Each week, students will meet with their project supervisor to report progress on their project and obtain guidance on the project.

Each person should maintain a journal where they write weekly reflections on their participation and experience of the project activities. The journal should be submitted individually to their supervisor every week.

The project may be industry-based or may be an industry-relevant project offered in-house. For in-house projects, industry exposure will be provided through mentors with industry experience who will simulate an industry-client environment for the project. Project topics require the written approval of the Project Review Panel. Approval will be given only if the panel is assured that the project, if completed successfully, will meet the learning outcomes of the unit. An industry-based project may have an industry supervisor from whom the unit coordinator will seek input during the marking of the assessment tasks prescribed in Section 9.


Assessment Task Due date Unit Learning Outcomes Weighting
1. Assignment 1 Group report: Project Detailed Design and Individual contributions report Week 3 a,b,e 15%
2. Assignment 2 Group report: Project Implementation and evaluation report Individual contributions report [Progress demonstration to supervisor is to be done every week] Week 11 a,b,e 55%
3. Assignment 3 Individual report: Peer evaluation of contributions of team members and reflective journal on professional practice/experience Week 12 d,e 10%
4. Assignment 4 Group presentations Week 12 c 10%
5. Attendance and Participation [client meeting and supervision meeting] Weeks 2-11 e 10%
Total     100%

*Within a group task, a number of sub-tasks will be assigned to individuals by the project supervisor. This, along with peer evaluation of contributions, supervisor’s evaluation of individual contributions and individual reflective journals, 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. Students are also strongly encouraged to actively participate in the group discussions and 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

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

Adopted Referencing Style: IEEE. For IEEE Style referencing guidance go to:


  • 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.