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Actuarial Analytics and Data I (ACTL30008)
Undergraduate level 3Points: 12.5On Campus (Parkville)
For information about the University’s phased return to campus and in-person activity in Winter and Semester 2, please refer to the on-campus subjects page.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Please refer to the LMS for up-to-date subject information, including assessment and participation requirements, for subjects being offered in 2020.
Overview
Availability | Semester 1 |
---|---|
Fees | Look up fees |
This subject aims to provide students with basic training on modern data analytics methods, which include linear regression, classification, resampling methods, spline-based methods, generalised additive models and tree-based methods. This subject focuses on applying the above methods to modelling non-life insurance claims frequency and severity.
Intended learning outcomes
On completion of this subject, students should be able to:
- Recognise major types of non-life insurance data and their main characteristics.
- Demonstrate a depth of knowledge in linear regression methods, regression splines and smoothing splines.
- Demonstrate basic understanding in various statistical learning models that include classification methods, re-sampling methods, generalised additive models and tree-based methods.
- Use relevant computer software to apply various statistical learning models for insurance related applications.
- Apply basic linear model selection and regularisation techniques when conducting linear regression analyses and use bagging and boosting techniques to improve decision trees when applying tree-based methods.
- Interpret the results of data analytics conducted on real insurance data
- Compare benefits/drawbacks of competing models and methods, relevant to real problems.
Generic skills
- High level of development: written communication; logical problem solving; statistical reasoning; application of theory to practice; interpretation and analysis; synthesis of data and other information; evaluation of data and other information; use of computer software.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20005 | Statistics |
Semester 2 (On Campus - Parkville)
Summer Term (On Campus - Parkville)
|
12.5 |
Corequisites
None
Non-allowed subjects
Students who complete this subject cannot also gain credit for
Code | Name | Teaching period | Credit Points |
---|---|---|---|
COMP30027 | Machine Learning | Semester 1 (On Campus - Parkville) |
12.5 |
Recommended background knowledge
Refer to Prerequisites
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Due to the impact of COVID-19, assessment may differ from that published in the Handbook. Students are reminded to check the subject assessment requirements published in the subject outline on the LMS
Description | Timing | Percentage |
---|---|---|
Assignment 1 (Computer-Based) (Week 3-6)
| First half of the teaching period | 15% |
Assignment 2 (Computer-Based) (Week 9-12)
| Second half of the teaching period | 15% |
Final Exam
| During the examination period | 70% |
Last updated: 3 November 2022
Dates & times
- Semester 1
Coordinator Xueyuan Wu Mode of delivery On Campus (Parkville) Contact hours Two 1-hour lectures and one 1-hour computer lab per week Total time commitment 170 hours Teaching period 2 March 2020 to 7 June 2020 Last self-enrol date 13 March 2020 Census date 30 April 2020 Last date to withdraw without fail 5 June 2020 Assessment period ends 3 July 2020
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
An Introduction to Statistical Learning with Applications in R, by Gareth James et al. (Springer Science + Business Media New York 2013)
- Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
- Available to Study Abroad and/or Study Exchange Students
This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.
Last updated: 3 November 2022