Description
Building on students’ existing knowledge of data science techniques, this subject investigates the range of deployment options to automatically extract insights from the vast amount of data available. This includes traditional server and database deployment, as well as a range of popular cloud solutions including open-source alternatives. The advantages and disadvantages of different approaches will be discussed. In addition to popular big data analytics deployment options such as Amazon Web Services (AWS), Microsoft Azure, Google Big Query, Apache Spark, H20.ai and NoSQL, students will also learn about the MapReduce and Hadoop framework. Importantly, security implications associated with big data analytic deployments will be discussed, including knowledge of principles for cybersecurity and an ability to implement basic best practices.
Subject details
| Type | Postgraduate |
| Code | DTSC71-300 |
| 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. Identify and apply frameworks for distributed storage and parallel processing using multiple (virtual) computers 2. Describe a variety of cloud-based deployment options for big data analytics, and an ability to implement simple, prototype deployments 3. Identify a variety of traditional database and server deployment options for big data analytics and implement simple, prototype deployments 4. Articulate the security risks, particularly cybercrime associated with a variety of deployment options for big data analytics 5. Identify the principles of cyber-safe deployment and implement basic safeguards to prototype deployments 6. Critically compare the advantages and disadvantages of different deployment options for big data analytics
Enrolment requirements
| Requisites: ? | Nil |
|---|---|
| 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 STAT71-112 Quantitative Methods. |
| Restrictions: ? | Nil |
Subject outlines
Subject dates
| Standard Offering | |
|---|---|
| Enrolment opens | 18/07/2021 |
| Semester start | 13/09/2021 |
| Subject start | 13/09/2021 |
| Cancellation 1? | 27/09/2021 |
| Cancellation 2? | 04/10/2021 |
| Last enrolment | 26/09/2021 |
| Withdraw – Financial? | 09/10/2021 |
| Withdraw – Academic? | 30/10/2021 |
| Teaching census? | 08/10/2021 |
| Standard Offering | |
|---|---|
| Enrolment opens | 14/11/2021 |
| Semester start | 17/01/2022 |
| Subject start | 17/01/2022 |
| Cancellation 1? | 31/01/2022 |
| Cancellation 2? | 07/02/2022 |
| Last enrolment | 30/01/2022 |
| Withdraw – Financial? | 12/02/2022 |
| Withdraw – Academic? | 05/03/2022 |
| Teaching census? | 11/02/2022 |