This course covers the actuarial professional syllabus for data analysis including techniques for mortality, health, and insurance data used in actuarial analysis and decision-making. The course covers aspects of data analysis including exploratory data analysis, data checking and cleaning, and data visualization; classification and prediction with regression and generalized linear models; descriptive, inferential and predictive analysis and models; and statistical and machine learning including supervised and unsupervised learning. The course also covers ethical, regulatory and professional issues, and risks and risk management associated with using data and data analysis. A particular focus will be placed on communication of technical results for business applications.

The aim of this course is to provide students with understanding and ability to handle actuarial data in order to solve actuarial problems.

The aims of this course are to provide students with an understanding of the main techniques on predictive analytics / data analytics techniques of particular relevance to actuarial work, including

Students are assumed to have a good mathematics background and a solid understanding of the concepts of probability and statistics, and actuarial modelling.

Students need to be able to use a word processing package (such as Word) and a spreadsheet (such as Excel). They should also be able to use the statistical software package R which will be used to implement many of the models discussed in this course, and in particular in the lab classes.

This course consists of:

- Self-study course material available on the course Moodle website (e.g. textbook chapters, video lectures, lecture notes, exercises),

- Weekly lectures,

- Weekly labs, and

- Weekly consultation times.

This course covers the Regression theory and applications part of the subject 'CS1 – Actuarial Statistics 1' and the Machine learning part of 'CS2 – Risk Modelling and Survival Analysis Core Principles' of the Institute of Actuaries.

As a student at UNSW you are expected to display academic integrity in your work and interactions. Where a student breaches the UNSW Student Code with respect to academic integrity, the University may take disciplinary action under the Student Misconduct Procedure. To assure academic integrity, you may be required to demonstrate reasoning, research and the process of constructing work submitted for assessment.

To assist you in understanding what academic integrity means, and how to ensure that you do comply with the UNSW Student Code, it is strongly recommended that you complete the Working with Academic Integrity module before submitting your first assessment task. It is a free, online self-paced Moodle module that should take about one hour to complete.

In order to pass this course students must:

• Achieve a composite mark of at least 50 out of 100
• Engage actively in course learning activities and attempt all assessment requirements
• Meet any additional requirements specified in the assessment details
• Meet the specified attendance requirements of the course

The website for this course is on Moodle.

The course will use various digital resources, but they all will be linked from Moodle.

To access the Moodle online support site for students, follow the links from that website to UNSW Moodle Support/Support for Students. Additional technical support can be obtained from [email protected] (02 9385 1333).

All course contents will be available from the course website. It is essential that you visit the site regularly to see any notices posted there by the course coordinator, as it will be assumed that they are known to you within a reasonable time.

There are many books of relevance to the course topics. The following book will be the main text references for a substantial part of the course:

• James, G., Witten, D., Hastie, T., Tibshirani, R., An Introduction to Statistical Learning with Applications in R, Second Edition, Springer, 2021

Additional readings from the professional actuarial literature will also be used to provided additional context, details, and examples. This will be communicated in the course website.

The Actuaries Institute allows students to become University Subscribers free of charge. Full time undergraduates studying at an Institute accredited university who are members of a university student actuarial society are eligible. To sign up.

Feedback is regularly sought from students and continual improvements are made based on this feedback. At the end of this course, you will be asked to complete the myExperience survey, which provides a key source of student evaluative feedback. Your input into this quality enhancement process is extremely valuable in assisting us to meet the needs of our students and provide an effective and enriching learning experience. The results of all surveys are carefully considered and do lead to action towards enhancing educational quality.