SIT718 - Real World Analytics
Unit details
| Year: | 2024 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Unit Chair: | Trimester 1: Ye Zhu Trimester 2: Son Tran Trimester 3: Wei-Yu Chiu |
| Prerequisite: | Nil |
| Corequisite: | Nil |
| Incompatible with: | Nil |
| Typical 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. |
| 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. |
Note:^GIFT City (India) offering is available to students enrolled at the GIFT City (India) campus only | |
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.
| ULO | These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: | Deakin Graduate Learning Outcomes |
|---|---|---|
| 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 the unit can be found on the University Library via the link below: SIT718 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.
Unit Fee Information
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