31243 AI/Analytics Capstone Project B
Warning: The information on this page is indicative. The subject outline for a
particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2024 is available in the Archives.
Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): 41004 AI/Analytics Capstone Project
Description
Data mining and knowledge discovery is the kernel of contemporary computer analytics and intelligence. The process consists of several iterative steps, including data pre-processing and transformation, the actual data mining and pattern discovery steps, and putting discovered information and knowledge into action. This subject is focused on the practical implementation of this process to large data sets from different areas of human endeavour. It provides students with exposure to real-world analytics scenarios, and with expertise and experience in the application of the data mining techniques and in professional communication of analytics results. Students choose a real-world project of interest and, with the help of a staff mentor, research, plan and produce an outcome. They communicate the results of the project in a detailed report.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Demonstrate competency in conceptualising, designing, planning and implementing research studies in data analytics. (C.1) |
|---|---|
| 2. | Select and apply an appropriate research method for solving a real-world analytics problem. (C.1) |
| 3. | Explain and justify an application of problem solving, reported in the required format and style. (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)
- 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
The subject involves independent and self-motivated work by the student, assisted by the student's individual project supervisor and the subject co-ordinator. Student and supervisor will develop a learning contract by week 3 where they agree on the tasks and scope of the project and form a plan for doing the project. The learning contract shall be a statement containing: the title of the proposed project; the aim of the project; a list of tasks to be carried out by the student to fulfil the project aims; and a set of milestones and delivery dates.
Projects shall be of real-world significance and it is intended that the project report would be of sufficient quality to demonstrate data analytics competence to a prospective employer. Students will need to do some background research in the problem area and methods to be used under the guidance of the mentor to be able to solve the problem in the agreed project. The depth of research will be gauged by the mentor so as to match the level of work required in a 6 credit point undergraduate subject such as this.
Students will meet regularly with their mentor (weekly or fortnightly) where they will report and receive feedback on their progress, determine how they are tracking against the project plan and whether that plan needs to be modified.
Students may collaborate with other students in a meaningful way as need arises but this is not necessary. Mentors are guided by the student needs in the case and will suggest online readings and other research material to aid their project development.
There are no formal classes in this subject.
Content (topics)
The student will negotiate a suitable topic with their project supervisor who will guide the student's learning and studies.
Assessment
Assessment task 1: Research Project
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and E.1 |
|---|---|
| Type: | Project |
| Groupwork: | Individual |
| Weight: | 100% |
| Length: | This is up to the discretion of the supervisor, but reports would normally be between 40 and 100 pages long. |
| Criteria: | The mark is split into presentation of the work in the report and the work itself. The components are; Report presentation 30%, Report Content 70% Presentation - English (10%) This is purely the syntactic correctness and ease of understanding. Is the review written in clear and correct English? It includes such factors as Good English: What percentage of sentences is correct syntactically, and easy to read? Layout: table of contents, abstract, sections, labels on tables and figures, appendices, etc. Presentation - Context (10%) This evaluates the introduction and literature review presented in the report. The report is a stand-alone document so it should start by setting the scene with a review of the relevant literature and existing knowledge. Presentation - Description of model or experiment (10%) This measures how clearly the model or experiment is presented. It includes things such as how easy the report is to understand, logical development and presentation, and description of the results. Report Content (70%) - The criteria are how closely the work matches the tasks and scope developed in the learning contract and includes such things as the amount of work, elegance, correctness, testing or evaluation, and generality. For a computer system, this may cover such topics as the elegance or clarity of the code, the reliability and power of the system, and the ability of the model to generalise to situations other than the examples used to build the system. For an empirical study, this may cover such things as the design and execution of the study (method), the design of the experimental probes or surveys (material), the correctness of the analysis (by statistical methods) and the ability to generalise results outside the sample. |
Minimum requirements
Student and supervisor will develop a learning contract by week 3 where they agree on the tasks and scope of the project.
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Recommended texts
As relevant to the proposed project.
Other resources
Subject announcements, the topic discussion boards for the subject and other communication tools will be in Canvas: https://canvas.uts.edu.au/.