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Actuarial Analytics and Data I (ACTL30008)
Undergraduate level 3Points: 12.5On Campus (Parkville)
To learn more, visit 2023 Course and subject delivery.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 1
Xueyuan Wu: xueyuanw@unimelb.edu.au
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 support vector machines. 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 support vector machines.
- 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.
- 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: 31 January 2024
Eligibility and requirements
Prerequisites
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST20005 | Statistics |
Semester 2 (On Campus - Parkville)
Summer Term (Dual-Delivery - Parkville)
|
12.5 |
Corequisites
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: 31 January 2024
Assessment
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% |
End-of-semester examination
| During the examination period | 70% |
Last updated: 31 January 2024
Dates & times
- Semester 1
Principal 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 27 February 2023 to 28 May 2023 Last self-enrol date 10 March 2023 Census date 31 March 2023 Last date to withdraw without fail 5 May 2023 Assessment period ends 23 June 2023 Semester 1 contact information
Xueyuan Wu: xueyuanw@unimelb.edu.au
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 31 January 2024
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: 31 January 2024