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Data Analytics in Insurance 1 (ACTL90023)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 1
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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

Intended learning outcomes

  • 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, resampling methods, generalised additive models and support vector machines.
  • Use computer software R 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

Prerequisites

Code Name Teaching period Credit Points
MAST20005 Statistics
Summer Term (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5

OR

Admission into the MC-ACTSCEN Master of Actuarial Science (Enhanced)

Corequisites

None

Non-allowed subjects

Code Name Teaching period Credit Points
COMP30027 Machine Learning
Semester 1 (On Campus - Parkville)
 12.5

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

DescriptionTimingPercentage

Indiviudal Assignment 1

  • 1500 words
From Week 3 to Week 615%

Individual Assignment 2

  • 1500 words
From Week 9 to Week 1215%

End-of-semester examination

  • 2 hours
Hurdle requirement: Examination must by passed		During the examination period70%

Last updated: 31 January 2024

Dates & times

  • Semester 1
    Principal coordinatorXueyuan Wu
    Mode of deliveryOn Campus (Parkville)
    Contact hours
    Total time commitment170 hours
    Teaching period26 February 2024 to 26 May 2024
    Last self-enrol date 8 March 2024
    Census date 3 April 2024
    Last date to withdraw without fail 3 May 2024
    Assessment period ends21 June 2024

    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