MIS373 - Artificial Intelligence for Business
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: Quan Vu |
| Prerequisite: | MIS272 and SIT112 |
| 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 1.5 hour on-campus (livestreamed) lecture (recordings provided) and 1 x 1.5 hour on-campus seminar each week |
| Educator-facilitated (scheduled) learning activities - online unit enrolment: | 1 x 1.5 hour class recorded lecture provided and 1 x 1.5 hour online seminar (recordings provided) each week |
Content
Businesses and governments have access to massive volumes of data and require advanced understanding and expertise to make sense of this information for strategic decision making and turning the information into key business assets. This unit will provide students with the knowledge and skills to develop advanced predictive models and make use of cutting-edge analytics tools to develop analytic-centric solutions in real business scenarios. Students will be given the opportunity to gain hands-on experience with the most widely used artificial intelligence software tools globally.
| ULO | These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: | Deakin Graduate Learning Outcomes |
|---|---|---|
| ULO1 | Design and develop artificial intelligence solutions to business problems. | GLO1: Discipline-specific knowledge and capabilities |
| ULO2 | Apply advanced analytics techniques to real-world business problems. | GLO1: Discipline-specific knowledge and capabilities |
| ULO3 | Critically evaluate feasibility and applicability of artificial intelligence techniques in business environments. | GLO1: Discipline-specific knowledge and capabilities |
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Assessment 1: (Individual) Case study: Data analysis with written report (Analytical) | Data analysis file plus 800 words | 30% | Week 5 |
| Assessment 2: (Individual) Case study: Data analysis with written report (Analytical) | Data analysis file plus 1000 words | 35% | Week 9 |
| Assessment 3: (Individual) Report (Business) | 2000 words | 35% | Week 11 |
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 at the link here: MIS373 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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