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
| Type | Undergraduate |
| 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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Learning outcomes
1. Recognise and communicate the inputs, outputs, relationships, boundaries, and data transformations of digital systems. 2. Design, train and use neural networks, SVN and ensemble tree models for business data systems. 3. Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis. 4. Apply the language, thinking and tools of data retrieval and manipulation to real-world problems. 5. Apply the communication framework for translating data analysis into decision making outcomes. 6. Articulate ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.
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: ? |
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Subject outlines
Subject dates
| Standard Offering | |
|---|---|
| Enrolment opens | 20/03/2022 |
| Semester start | 16/05/2022 |
| Subject start | 16/05/2022 |
| Cancellation 1? | 30/05/2022 |
| Cancellation 2? | 06/06/2022 |
| Last enrolment | 29/05/2022 |
| Withdraw – Financial? | 11/06/2022 |
| Withdraw – Academic? | 02/07/2022 |
| Teaching census? | 10/06/2022 |