SIG718 - Real World Analytics
| Year: | 2024 unit information |
|---|---|
| Enrolment modes: | Trimester 3: Great Learning |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Prerequisite: | Nil |
| Corequisite: | Must be enrolled in S773 Master of Data Science (Global) |
| Incompatible with: | SIT718 |
| Study commitment | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. This will include educator guided online learning activities within the unit site. |
| Scheduled learning activities - online | Online independent and collaborative learning including optional scheduled activities as detailed via the Great Learning platform. |
| Note: | This unit is part of the Master of Data Science (Global) program and is restricted to online international students who reside outside Australia. |
Content
This unit introduces students to two concepts at the heart of real world analytics: optimisation and multivariate data aggregation. Students will learn how decision-making problems in industry, business, and civic services can be solved using modern modelling and solution techniques. Students will learn how to make better decisions through mathematical methods in optimisation problems such as: production planning, time-tabling management, human resource rostering, sports program scheduling, robotics/vehicle routing, network design, and resource allocation. On the topic of aggregation, students will learn how to apply the concepts of multivariate functions in order to summarise datasets that involve several interrelated variables. They will be able to reasonably analyse datasets by interpreting the parameters associated with commonly used multivariate functions.