Forecast business scenarios and plan solutions that can be applied in a real-life setting. Get hands-on with cloud, mobile and enterprise resource planning software. See where the analyst fits into different business divisions and management levels.
Your upfront cost: $0
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Students who successfully complete this subject will be able to:
- Demonstrate an understanding of the critical roles of business analytics in various organisational contexts
- Synthesise and develop appropriate business solution scenarios using appropriate analytics and visualisation techniques
- Analyse business problems and define the data requirements and business rules associated with the data-driven problem-solving approaches
- Demonstrate critical thinking and problem solving
- Communicate effectively as a professional and function as an effective leader or member of a diverse team.
- Use of and value of analytics in Business divisions, management functions, different management levels, performance management
- Business rules vs business data requirements
- Marketing analytics, financial analytics, sports analytics, geospatial analytics, security analytics, health analytics, SNA, etc.
- Multi-platform analytics (mobile, cloud, ERP)
- Data visualisation, dashboard design, storyboarding with analytics
- Data-driven decision making vs Intuitive decision making
- Introduction to advance analytics: Predictive, Prescriptive, CI, sentiment analytics, geospatial analytic
- Forecasting and predictive analytics, scenario planning
- Fast data, data lake, social media data, open data
You must have successfully completed the following subject(s) before starting this subject:
In addition to INF10003, students must also complete an additional 12 x subjects (150 credit points) before undertaking INF30030.
No additional requirements
Students will develop and enhance their conceptual and practical understanding in data analytics in a business context. Students will have an opportunity to immerse themselves in problem-solving activities requiring lateral and critical thinking by exploring structured and unstructured data, considering and applying the appropriate analytic techniques using a software programming environment for statistical computing (such as R) and visualisation tools and approaching organisational problems through data-driven decision-making processes.
Presentation, reports and journals Initial Project Plan Project Management Presentations Final report and Solution(s)
- Individual/Group (100%)
Current study term: 29 Aug 21 to 28 Nov 21
Check the learning management system (LMS) of your university for textbook details.