Predictive Analytics
Duration
- One Semester or equivalent
Contact hours
- 36 Hours
On-campus unit delivery combines face-to-face and digital learning.
2021 teaching periods
Hawthorn
| Higher Ed. Semester 2 | ||
|---|---|---|
Dates: Results: Last self enrolment: Census: |
Prerequisites
Enrolment in MBIS or Business Analytics SpecialisationAND
INF70008 Business Analytics and Visualisation
OR
Enrolment in MITPC
AND
INF60012 Cloud Enterprise Systems and Analytics
OR
Enrolment in MIT
AND
INF60007 Business Information Systems
Aims and objectives
This unit aims to develop and enhance students’ conceptual and practical understanding of Predictive Analytics as an advanced topic in business analytics. Using an appropriate predictive model on select datasets, an organisation would be able to identify the likelihood of future outcomes based on historical and current data. The business foresight based on this analytics is instrumental in strategic decision making processes of the organisation. 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 Predictive Analytics techniques.
Students who successfully complete this unit, will be able to:
1 Demonstrate the ability to systematically identify and review strategic opportunities through the application of Predictive Analytics on organisational datasets
2 Apply problem solving and decision-making techniques in order to evaluate the requirements for information and data/datasets enabling an effective use of Predictive Analytics tools and techniques
3 Critically analyse appropriate datasets using Predictive Analytics tools and techniques to generate business foresights
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
Face to Face Mode:
Class (12 x 1 hour) and Labs (12 x 1 hour)
Online: Directed Online Learning and Independent Learning
12 hours (12 x 1 hour)
Independent Learning
114 hours (12 x 9.5 hours)
Student workload:
This includes all:
• Scheduled teaching and learning events and activities (contact hours timetabled in a face-to-face teaching space) and scheduled online learning events (contact hours scheduled in an online teaching space), and
• Non-scheduled learning events and activities (including directed online learning activities, assessments, independent study, student group meetings, and research.
To be successful, students should:
• Attend and engage in all scheduled classes (face to face or online)
• Start assessment tasks well ahead of the due date, and submit assessments promptly
• Read / listen to all feedback carefully, and consider it for future assessment
• Engage with fellow students and teaching staff (don’t hesitate to ask questions)
Assessment
Project (Group) 30-50%
Report (Individual) 30-40%
Literature Review (Individual) 20-30%
Content
• The strategic importance of Predictive Analytics for a contemporary organisation
• The role of big data and analytics in strategic decision making and identifying business opportunities
• Predictive modelling: Trending and Forecasting
• Use cases of Predictive Analytics
• The concepts of Machine Learning, supervised learning, unsupervised learning, reinforcement learning
• The role of big data and analytics in strategic decision making and identifying business opportunities
• Predictive modelling: Trending and Forecasting
• Use cases of Predictive Analytics
• The concepts of Machine Learning, supervised learning, unsupervised learning, reinforcement learning
Study resources
- Reading materials.
Reading materials
Students are advised to check the unit outline in the relevant teaching period for appropriate textbooks and further
reading.