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Introduction to ACST8095 Actuarial Data Analytics

ACST8095 Actuarial Data Analytics is a university program offered by Macquarie University. The program is designed to provide students with advanced tools and techniques in data analytics, focusing on practical application using real-life case studies.

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. Students will learn how to apply and develop these skills in a range of business environments.

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

General Assessment Information

• Late Assessment Submission Penalty: 5% penalty per day for written assessments, up to 7 days
• Special Consideration: Students must submit an application for special consideration for late submissions of time-sensitive tasks

Assessment Tasks

• Professional Practice: Data Analytics Report (20%): A written report (max 2000 words) demonstrating actuarial data analytics skills in commercial scenarios
• Skills Development: Reflection Portfolio (20%): A portfolio of project-based tasks (max 1000 words) showcasing practical application of skills and knowledge
• Formal and Observed Learning: Exam (60%): A 3-hour exam held during the University Examination period

Delivery and Resources

• Classes: Offered via classes in the North Ryde campus, Sydney CBD campus, and via distance education
• Online Lecture Recordings: Available through the University's lecture recording facility (ECHO360 or Zoom)
• Timetable: Accessible through eStudent Class Finder
• Teaching Staff: Maggie Lee (Unit Convenor) and Pavel Shevchenko (Lecturer)
• Assumed Knowledge: Knowledge and skills acquired in subjects from the Foundation Program (Part 1s) of the Actuaries Institute education program
• Lecture Slides/Learning Guide: Available for each section of work
• Technology Used and Required: Software to code (R and R Studio) and word-processing software to produce reports
• Teaching Website: Course material available on the online learning management system (iLearn)

Unit Schedule

The unit schedule outlines the 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

Student Support

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

• Academic Success: Resources to develop English language proficiency, academic writing, and communication skills
• Library: Online and face-to-face support to help students find and use relevant information resources
• Student Services and Support: IT support, accessibility and disability support, mental health support, safety support, and social support
• Student Enquiries: Service Connect Portal or contact Service Connect
• IT Help: Support for University computer systems and technology