Program Overview

Introduction to ACST8095 Actuarial Data Analytics

ACST8095 is a university program that covers advanced tools and techniques in data analytics. The program focuses on practical application using real-life case studies, enabling students to contribute to all stages of developing solutions to analytical problems across multiple industries or domains.

General Information

• Unit Convenor: Maggie Lee
• Lecturer: Pavel Shevchenko
• Credit points: 10
• Prerequisites: Permission by special approval
• Corequisites: None
• Co-badged status: None
• Unit description: This unit covers advanced tools and techniques in data analytics, with a focus on practical application using real-life case studies.

Learning Outcomes

On successful completion of this unit, students will be able to:

• Explain the key iterative steps involved in building a model
• Describe the various stages in data understanding and preparation
• Compare predictive modelling techniques to select an appropriate method for a stated situation
• Use a range of perspectives to evaluate the appropriateness of a model
• Communicate modelling results to a range of business decision-making audiences

Assessment Tasks

• Professional Practice: Data Analytics Report (20%): A case study/analysis that demonstrates actuarial data analytics skills in commercial scenarios
• Skills Development: Reflection Portfolio (20%): A project that applies actuarial analytics concepts to complete a portfolio of project-based tasks
• Formal and Observed Learning: Exam (60%): A 3-hour exam held during the University Examination period

Delivery and Resources

• Classes: ACST8095 is offered via classes in the North Ryde campus, Sydney CBD campus, and via distance education
• Online lecture recordings: Standard recordings of campus lectures will be available
• Timetable: The timetable for classes can be accessed through eStudent Class Finder
• Teaching staff: Maggie Lee and Professor Pavel Shevchenko
• Assumed knowledge: Knowledge and skills in subjects from the Foundation Program (Part 1s) of the Actuaries Institute education program
• Lecture slides/Learning Guide: Lecture Slides and/or Learning Guides and associated readings will be provided for each section of work
• Technology Used and Required: Software to code (R and R studio) and word-processing software to produce reports

Unit Schedule

The unit schedule sets out the assessment and topics covered in each week of the session, including:

• Week 1: Business Environment
• Week 2: Communication
• Week 3: Data exploration
• Week 4: Data quality
• Week 5: Data manipulation and cleansing
• Week 6: Basic Concepts and Linear Regression
• Week 7: Linear Regression II
• Week 8: Model Selection
• Week 9: GLM (Poisson Regression), clustering
• Week 10: Regression Tree methods
• Week 11: Classification
• Week 12: Neural Networks and Generalised Additive Models
• Week 13: Mortality modelling using regression tree

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central, including:

• Academic Appeals Policy
• Academic Integrity Policy
• Academic Progression Policy
• Assessment Policy
• Fitness to Practice Procedure
• Assessment Procedure
• Complaints Resolution Procedure for Students and Members of the Public
• Special Consideration Policy

Academic Integrity

At Macquarie, academic integrity is at the core of learning, teaching, and research. The university offers a range of resources and services to help students reach their potential, including free online writing and maths support, academic skills development, and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students, including:

• Academic Success
• Library support
• IT Support
• Accessibility and disability support
• Mental health support
• Safety support
• Social support
• Student Advocacy
• Student Enquiries
• IT Help