|Faculty:||Bond Business School|
Using an information systems approach, this subject outlines the design principles and techniques necessary to produce appropriate infrastructure specifications for different data analytic systems. These requirements can be specified in terms of people, procedures, data, software, and hardware. Successful designs will allow systems to automatically extract insights from vast amounts of available data. Topics include, but are not limited to, key modern issues such as job roles in data analytic ecosystems, the operation of orgainsations, security and data integrity principles, business processes, blockchains, NoSQL databases, cloud solutions, software options and fundamental tenets of computing. The knowledge of these, and understanding how the components interact together, allow students to design efficient systems that are robust to change and conform to best practice.
- Apply an information systems framework to determine the type of infrastructure requirements needed for complex data analytic systems.
- Demonstrate the ability to implement complex prototype deployments for big data analytics
- Explain key technical aspects of people, procedures, data, software and hardware as they pertain to data analytic information systems.
- Compare the advantages and disadvantages of complex infrastructure options for data analytic projects.
- Evaluate complex business data infrastructure issues using relevant concepts, models and theories.
- Express business information in a clear, concise writing style tailored to a general audience.
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.
Standard Offering Enrolment opens: 17/07/2022 Semester start: 12/09/2022 Subject start: 12/09/2022 Cancellation 1: 26/09/2022 Cancellation 2: 03/10/2022 Last enrolment: 25/09/2022 Withdraw - Financial: 08/10/2022 Withdraw - Academic: 29/10/2022 Teaching census: 07/10/2022
Standard Offering Enrolment opens: 13/11/2022 Semester start: 16/01/2023 Subject start: 16/01/2023 Cancellation 1: 30/01/2023 Cancellation 2: 06/02/2023 Last enrolment: 29/01/2023 Withdraw - Financial: 11/02/2023 Withdraw - Academic: 04/03/2023 Teaching census: 10/02/2023
|Withdraw - Financial:||08/10/2022|
|Withdraw - Academic:||29/10/2022|
|Withdraw - Financial:||11/02/2023|
|Withdraw - Academic:||04/03/2023|