| Type: | Undergraduate Subject |
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| Code: | DTSC13-301 |
| 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
Unprecedented volumes of data are being created on an almost daily basis and the amount of data we generate is expected to double every two years. This ‘Big Data’ has the power to change the way we work, live, and think. This subject is designed to provide students with the knowledge and skills to analyse Big Data in a variety of business contexts. Specifically, mathematical and practical applications of Artificial Neural Networks, Support Vector Machines, Natural Language Processing and Ensemble Decision Tree techniques are explored. Valuable skills in the use of these techniques are reinforced with practical application.
Subject details
Learning outcomes
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis.
- Design and train neural networks, including convolutional and recurrent structures and other modern extensions.
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Apply the language, thinking and tools of data retrieval and manipulation to real-world problems.
- Apply the communication framework for translating data analysis into decision making outcomes.
- Articulate machine learning ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.
- Demonstrate an appropriate awareness of global issues impacting decision-making paradigms and model building exercises.
- Apply appropriate professional standards and best practices to make ethical, responsible decisions decision-making paradigms and model building exercises.
Enrolment requirements
| Requisites: |
Pre-requisites:Co-requisites:There are no co-requisites |
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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):Possess demonstrable knowledge in elementary probability theory, statistics, elementary calculus and linear algebra to the level of a unit such as STAT11-112 Quantitative Methods. |
| Restrictions: |
This subject is not available to
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Subject outlines
Subject dates
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May 2024
Standard Offering Enrolment opens: 17/03/2024 Semester start: 13/05/2024 Subject start: 13/05/2024 Cancellation 1: 27/05/2024 Cancellation 2: 03/06/2024 Last enrolment: 26/05/2024 Withdraw - Financial: 08/06/2024 Withdraw - Academic: 29/06/2024 Teaching census: 07/06/2024
| Standard Offering | |
|---|---|
| Enrolment opens: | 17/03/2024 |
| Semester start: | 13/05/2024 |
| Subject start: | 13/05/2024 |
| Cancellation 1: | 27/05/2024 |
| Cancellation 2: | 03/06/2024 |
| Last enrolment: | 26/05/2024 |
| Withdraw - Financial: | 08/06/2024 |
| Withdraw - Academic: | 29/06/2024 |
| Teaching census: | 07/06/2024 |