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DTSC13-300: Infrastructure for Data Analytics

Description

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 organisations, 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.

Subject details

Type: Undergraduate Subject
Code: DTSC13-300
EFTSL: 0.125
Faculty: Bond Business School
Semesters offered:
  • September 2023 [Standard Offering]
  • January 2024 [Standard Offering]
Credit: 10
Study areas:
  • Actuarial Science and Data Analytics
Subject fees:
  • Commencing in 2023: $4,050.00
  • Commencing in 2024: $4,260.00
  • Commencing in 2023: $5,400.00
  • Commencing in 2024: $5,730.00

Learning outcomes

  1. Apply an information systems framework to determine the type of infrastructure requirements needed for basic data analytic systems.
  2. Demonstrate the ability to implement basic prototype deployments for big data analytics
  3. Describe key technical aspects of people, procedures, data, software and hardware as they pertain to data analytic information systems.
  4. Compare the advantages and disadvantages of basic infrastructure options for data analytic projects.
  5. Evaluate basic business data infrastructure issues using relevant concepts, models and theories.
  6. Express business information in a clear, concise writing style tailored to a general audience.

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 STAT11-112 Quantitative Methods.

Restrictions:

Subject dates

  • Standard Offering
    Enrolment opens: 16/07/2023
    Semester start: 11/09/2023
    Subject start: 11/09/2023
    Cancellation 1: 25/09/2023
    Cancellation 2: 02/10/2023
    Last enrolment: 24/09/2023
    Withdraw - Financial: 07/10/2023
    Withdraw - Academic: 28/10/2023
    Teaching census: 06/10/2023
  • Standard Offering
    Enrolment opens: 12/11/2023
    Semester start: 15/01/2024
    Subject start: 15/01/2024
    Cancellation 1: 29/01/2024
    Cancellation 2: 05/02/2024
    Last enrolment: 28/01/2024
    Withdraw - Financial: 10/02/2024
    Withdraw - Academic: 02/03/2024
    Teaching census: 09/02/2024
Standard Offering
Enrolment opens: 16/07/2023
Semester start: 11/09/2023
Subject start: 11/09/2023
Cancellation 1: 25/09/2023
Cancellation 2: 02/10/2023
Last enrolment: 24/09/2023
Withdraw - Financial: 07/10/2023
Withdraw - Academic: 28/10/2023
Teaching census: 06/10/2023