Business Analytics and Artificial Intelligence
UndergraduateSWI-INF300362023
Previously SWI-INF30030
Course information for 2023 intake
Learn how to use AI to unlock data for business success. You’ll build on your programming knowledge as you explore statistical computing and visualisation tools. Use machine learning to problem solve and develop business solutions.
Enrolments for this course are closed, but you may have other options to start studying now. Book a consultation to learn more.
- Study method
- 100% online
- Assessments
- 100% online
- Entry requirements
- Prior study needed
- Duration
- 13 weeks
- Loan available
- HECS-HELP and FEE-HELP available
Business Analytics and Artificial Intelligence
About this subject
After successfully completing this unit, you will be able to:
1. Demonstrate an understanding of the roles of business analytics and artificial intelligence in various organisational contexts
2. Synthesise and develop appropriate business solution scenarios using appropriate machine learning, analytics and visualisation techniques
3. Analyse business problems and define the data requirements and business rules associated with the data-driven and machine learning problem-solving approaches
4. Demonstrate critical thinking and problem solving through statistical and machine learning computing and visualization
5. Communicate effectively as a professional and function as an effective leader or member of a team
- The role and value of business analytics and artificial intelligence in business operations and strategic planning.
- The difference between business rules and business data requirements.
- Artificial intelligent applications as sources of business data
- How Artificial Intelligence can support and enhance marketing analytics, financial analytics, sports analytics, geospatial analytics, security analytics, health analytics, Social Network Analysis (SNA).
- The use of Artificial Intelligence in data analysis visualisation, dashboard design, storyboarding
- Predictive, prescriptive, sentiment analytics, geospatial analytics
- Fast data, data lake, social media data, open data
This unit aims to develop and enhance students' conceptual and practical understanding of data analytics and artificial intelligence in the contemporary 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 and artificial intelligence 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. Further, students will learn fundamental concepts, key techniques and popular tools in artificial intelligence and how it is applied in business and industry.
Essay, Problem Solving, Project
- Essay (10-20%)
- Problem Solving (20-30%)
- Project 1 (30-50%)
- Project 2 (20-30%)
For textbook details check your university's handbook, website or learning management system (LMS).
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Entry requirements
Others
200 credit points
Assumed Knowledge - Basic understanding of computer programming
Additional requirements
No additional requirements
Study load
- 0.125 EFTSL
- This is in the range of 10 to 12 hours of study each week.
Equivalent full time study load (EFTSL) is one way to calculate your study load. One (1.0) EFTSL is equivalent to a full-time study load for one year.
Find out more information on Commonwealth Loans to understand what this means to your eligibility for financial support.
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