SIT718 - Real World Analytics
Unit details
| Year | 2026 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Online Trimester 2: Burwood (Melbourne), Online Trimester 3: Burwood (Melbourne), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Unit Chair: | Trimester 1: Son Tran Trimester 2: Ye Zhu Trimester 3: Wei-Yu Chiu |
| Prerequisite: | For students in S406, S408, S434, S479: Must have completed a minimum of 16 credit points of SIT units. |
| Corequisite: | Nil |
| Incompatible with: | Nil |
| Educator-facilitated (scheduled) learning activities - on-campus unit enrolment: | 1 x 2 hour online lecture per week, 1 x 2 hour practical experience (workshop) per week. Weekly meetings. |
| Educator-facilitated (scheduled) learning activities - online unit enrolment: | Online independent and collaborative learning including 1 x 2 hour online lecture per week (recordings provided), 1 x 2 hour online practical experience (workshop) per week, weekly meetings. |
| Typical study commitment: | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. |
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.
Learning outcomes
Each unit in your course is a building block towards Deakin's Graduate Learning Outcomes - not all units develop and assess every Graduate Learning Outcome (GLO).
| ULO | These are the Unit Learning Outcomes (ULOs) for this unit. At the completion of this unit, successful students can: | Alignment to Deakin Graduate Learning Outcomes (GLOs) |
|---|---|---|
| ULO1 | Apply knowledge of multivariate functions data transformations and data distributions to summarise data sets. | GLO1: Discipline-specific knowledge and capabilities |
| ULO2 | Analyse datasets by interpreting summary statistics, model and function parameters. | GLO1: Discipline-specific knowledge and capabilities |
| ULO3 | Apply game theory, and linear programming skills and models, to make optimal decisions. | GLO1: Discipline-specific knowledge and capabilities |
| ULO4 | Develop software codes to solve computational problems for real world analytics. | GLO1: Discipline-specific knowledge and capabilities |
| ULO5 | Demonstrate professional ethics and responsibility for working with real world data. | GLO8: Global citizenship |
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Assessment 1 | Five online quizzes | 20% (5 x 4%) | Weeks 5, 7, 8, 10 and 12 |
| Assessment 2 | Two problem solving tasks | 50% (20%, 30%) | Weeks 7 and 10 |
| End-of-Unit Assessment | Timed online test | 30% | End-of- Unit Assessment period |
The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.
Learning resource
The texts and reading list for SIT718 can be found via the University Library.
Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.
Bring Your Own Device (BYOD)
To fully engage with Deakin's learning experiences, students must be able to access and use internet-connected devices as outlined in computing requirements at Deakin.
To support student success at Deakin, we have a bring-your-own-device (BYOD) learning environment that acknowledges that students and educators bring with them the digital tools they regularly use to complete academic tasks. These tools stay with you beyond the classroom, helping you to keep learning, explore ideas more deeply, and connect with knowledge in ways that matter to you.
Students requiring a loan device should visit our Loan Laptop webpage or students requiring longer-term assistance should visit our Student Financial Assistance webpage.
Unit fee information
Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study and their study discipline, and your study load.
Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.
For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current Students website.