41004 AI/Analytics Capstone Project
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Subject handbook information prior to 2025 is available in the Archives.
Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): 31250 Introduction to Data Analytics AND 31272c Project Management and the Professional
The lower case 'c' after the subject code indicates that the subject is a corequisite. See definitions for details.
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Recommended studies:
knowledge of database technologies
Description
This subject brings together the full skill set learned by students in data analytics. Students undertake a data analytics project as part of a team, manage the investigation, document their progress, communicate their findings and reflect on their learning.
Projects are set out to solve real-world problems in academic research or industry and may include:
- data generation/collection
- data processing/cleaning
- data modelling/analysis
- interpretation and communication of results.
Throughout the project, students are supported by an academic mentor and assessment through written reports and an oral presentation is in line with expectations from academic/industry.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Conceptualise, design, plan and implement a data analytics project. (C.1) |
|---|---|
| 2. | Choose appropriate data analytics methods to achieve outcomes, apply them and evaluate their efficacy. (D.1) |
| 3. | Communicate the results of the data analytics investigation verbally and in written form at a level appropriate for a business audience. (E.1) |
| 4. | Contribute effectively in a data analytics team to achieve the desired outcomes. (E.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design and decision-making methodologies to develop components, systems and processes to meet specified requirements. (C.1)
- Technically Proficient: FEIT graduates apply abstraction, mathematics and discipline fundamentals, software, tools and techniques to evaluate, implement and operate systems. (D.1)
- Collaborative and Communicative: FEIT graduates work as an effective member or leader of diverse teams, communicating effectively and operating within cross-disciplinary and cross-cultural contexts in the workplace. (E.1)
Teaching and learning strategies
This subject is project based. In the first week, students inform themselves on available projects, clarify project expectations, choose a project and form groups using Canvas.
At the start of the session, students will attend a class on methodological issues and introduce their projects and groups.
Throughout the remainder of the session, students develop their projects through collaborative group work assisted by weekly group meetings with their project mentors/sponsors. These meetings are a valuable source of verbal feedback and give students the opportunity to clarify their understanding and to discuss their research to gain deeper considerations to include in their projects. To complement the project based learning and to provide formal feedback, students will be able to access reading materials and a discussion board on Canvas and submit written reports (see activates detailed in the assessment section) and deliver formal presentations of the project outcomes at the end of the session.
Content (topics)
In this subject students will apply Data Analytics Methodologies (CRISP-DM, SEMMA), Data Analytics Project Management (resource allocation, working in teams, planning, running meetings), use their skills for Data Interpretation and Communication of Results.
Assessment
Assessment task 1: Plan and proposal
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and E.1 |
|---|---|
| Type: | Report |
| Groupwork: | Group, group and individually assessed |
| Weight: | 20% |
| Length: | The task requires submission of a report of 10 pages in an 11 or 12 point font. |
| Criteria: | Assignments will be assessed based on the quality and plausibility of the proposal and plan and the completeness of team roles. |
Assessment task 2: Mid-project update and presentation
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
|---|---|
| Type: | Report |
| Groupwork: | Group, group and individually assessed |
| Weight: | 30% |
| Length: | A report of around 20 pages in an 11 or 12 point font and a 10 minute presentation to the client. |
Assessment task 3: Final project and presentation
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1, D.1 and E.1 |
|---|---|
| Type: | Project |
| Groupwork: | Group, group and individually assessed |
| Weight: | 50% |
| Length: | A report of around 50 pages in an 11 or 12 point font and a 10 minute presentation to the client executive panel. |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Required texts
The following books are strongly recommended.
1. Introduction to Data Mining, P.-N. Tan, M. Steinbach and V. Kumar, Addison-Wesley, 2005.
2. Data Mining: Concepts and Techniques, J. Han, M. Kamber, and J. Pei, Morgan Kaufmann, 2012
Recommended texts
You might find the following texts useful.
1. Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2006.
2. Witten, I. H. and Frank, E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, CA, 2000.
3. Graham Williams (2011). Data Mining with Rattle and R, Springer.