Course Title: Data-Driven Decision Making: Machine Learning for Business Professionals
Part A: Course Overview
Course Title: Data-Driven Decision Making: Machine Learning for Business Professionals
Credit Points: 12.00
Course Coordinator: Huan Vo-Tran
Course Coordinator Phone: 9925 1699
Course Coordinator Email: huan.vo-tran@rmit.edu.au
Course Coordinator Location: Building 80, Level 8, Room 048
Course Coordinator Availability: By appointment
Pre-requisite Courses and Assumed Knowledge and Capabilities
None
Course Description
This course introduces you to the capabilities, limitations, and biases of machine learning and its applications in addressing complex business challenges through data-driven decision-making. You will learn to create heuristic designs, analyse case studies, and develop presentations to communicate your findings effectively. Throughout the course, you will explore heuristics and tools for selecting and applying machine learning algorithms to various business scenarios, and present insights to executive stakeholders to aid in automating some decision-making processes, allowing teams to focus on higher-order decisions.
Objectives/Learning Outcomes/Capability Development
.
On successful completion of this course you will be able to:
CLO1: Select and justify a heuristic to support the selection of the appropriate tool to solve a range of data-driven business problems.
CLO2: Analyse and recommend appropriate analytical approaches (e.g., Machine learning techniques, Artificial Intelligence models, Deep Learning) based on a range of problem-specific parameters (e.g., problem recognition, testing hypotheses, reproducibility, and applicability of models) to solve business challenges through data analytics.
CLO3: Evaluate and identify relevant data analysis options for managing and incorporating big data into decision-making within an organisation.
CLO4: Critically reflect to verify the approach, reproducibility, and accuracy of inputs and outputs of data science algorithms are ethical, valid, and sustainable.
CLO5: Interpret the outputs of machine learning and effectively communicate this to decision-makers in a range of business contexts.
CLO6: Utilise machine learning applications and/or analyse the outputs to provide additional clarity to support strategic decision-making for an executive group.
Overview of Learning Activities
This course uses highly structured learning activities to guide your learning process and prepare you for your assessments. a range of individual and group activities to facilitate learning. These include completing the required and recommended readings and attending in-class and online activities in which seminars and group discussions take place. The activities are a combination of individual, peer-supported, and facilitator-guided activities, and where possible project-led, with opportunities for feedback throughout.
Authentic and industry-relevant learning is critical to this course, and you will be encouraged to critically compare and contrast what is happening in your context and in the business analytics industry, and to use your insights.
Social learning is another important component, and you are expected to participate in class and group activities, share drafts of work and resources, and give and receive peer feedback. You will be expected to work efficiently and effectively with others to achieve outcomes greater than those that you might have achieved alone.
Above all, the learning activities are designed to maximise the likelihood that you will not only understand the course learning resources but also apply that learning to improving your own practice, for example, by producing real-world business artefacts and engaging in scenarios and case studies.
Overview of Learning Resources
Various learning resources are available online through myRMIT/Canvas. In addition to topic notes, assessment details and a study schedule, you may also be provided with links to relevant online information, readings, audio and video clips and communication tools to facilitate collaboration with your peers and to share information.
RMIT Library provides extensive resources, services and study spaces. All RMIT students have access to scholarly resources including course related material, books, e-books, journals and databases.
Computers and printers are available at every Library. You can access the Internet and Library e-resources. You can also access the RMIT University wireless network in the Library.
Contact: Ask the Library for assistance and information on Library resources and services: http://www.rmit.edu.au/library. Study support is available for assistance with assignment preparation, academic writing, information literacy, referencing, maths and study skills. Additional resources and/or sources to assist your learning will be identified by your course coordinator and will be made available to you as required during the teaching period.
Overview of Assessment
The assessment alignment list below shows the assessment tasks against the learning outcomes they develop.
Assessment Task 1: 30%
Linked CLOs: 1, 2
Assessment Task 2: 30%
Linked CLOs: 2, 4, 5
Assessment Task 3: 40%
Linked CLOs: 2, 3, 5, 6
Feedback will be provided throughout the semester in class and/or in online forums through individual and group feedback on practical exercises and by individual consultation.