| Type: | Undergraduate Subject |
|---|---|
| Code: | DTSC13-306 |
| EFTSL: | 0.125 |
| Faculty: | Bond Business School |
| Semesters offered: |
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| Credit: | 10 |
| Study areas: |
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| Subject fees: |
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Description
This subject is designed for students who already have a basic understanding of machine learning and want to deepen their knowledge using more advanced techniques. The subject focuses on advanced machine learning methods that are relevant and effective in many real-life and business applications. Students will be provided the necessary tools to wrangle data, implement and train machine learning models, and evaluate the performance and feasibility of these models in the context of the environment where these models are going to be applied. Advanced visualisation tools will be used to create dynamic visual representations of data.
Subject details
Learning outcomes
- Create common models to extract patterns from data using sophisticated machine learning techniques and evaluate their effectiveness.
- Analyse the feasibility and usefulness of predictive models in the context for which they were created.
- Design solutions for common business problems using combinations of machine learning algorithms.
- Use computational infrastructure to implement solutions that involve big data for common business problems.
- Create static and dynamic common visualisations to convey clear information to management and other users.
Enrolment requirements
| Requisites: |
Nil |
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| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge. Assumed Prior Learning (or equivalent):Basic Python (equivalent to DTSC11-100) Basic concepts of data science (equivalent to DTSC12-200). |
| Restrictions: |
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Subject dates
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September 2024
Standard Offering Enrolment opens: 14/07/2024 Semester start: 09/09/2024 Subject start: 09/09/2024 Cancellation 1: 23/09/2024 Cancellation 2: 30/09/2024 Last enrolment: 22/09/2024 Withdraw - Financial: 05/10/2024 Withdraw - Academic: 26/10/2024 Teaching census: 04/10/2024
| Standard Offering | |
|---|---|
| Enrolment opens: | 14/07/2024 |
| Semester start: | 09/09/2024 |
| Subject start: | 09/09/2024 |
| Cancellation 1: | 23/09/2024 |
| Cancellation 2: | 30/09/2024 |
| Last enrolment: | 22/09/2024 |
| Withdraw - Financial: | 05/10/2024 |
| Withdraw - Academic: | 26/10/2024 |
| Teaching census: | 04/10/2024 |