Data Analytics in Insurance 2 (ACTL90019)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 2
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This subject aims to further develop students’ knowledge of modern analytical tools and techniques, including GLM, shrinkage techniques (e.g., LASSO and ridge regression), tree-based methods (e.g., random forests and GBM) and neural networks. It also teaches students to connect data analytics work to the actuarial control cycle and real-world business environments. Effective communication of findings to a range of business decision making audiences is also stressed.

Intended learning outcomes


On successful completion of this subject, students should be able to:

  • Explain where and how their data analytics work can add value to the business environment and strategy.
  • Source, interpret, evaluate and prepare data for modelling.
  • Use judgment to select appropriate predictive analytic techniques for a given business problem.
  • Apply predictive analytic techniques to solve estimation and classification problems.
  • Evaluate and compare performance of different models.
  • Communicate findings to a range of audiences.

Generic skills

  • Written communication
  • Problem solving
  • Statistical reasoning
  • Application of theory to practice
  • Predictive analytics
  • Interpretation and analysis

Last updated: 4 March 2025

Eligibility and requirements

Prerequisites

One of

Code Name Teaching period Credit Points
ACTL90023 Data Analytics in Insurance 1
Semester 1 (On Campus - Parkville)
 12.5
ACTL90008 Statistical Techniques in Insurance
Semester 2 (On Campus - Parkville)
 12.5
ACTL30004 Actuarial Statistics
Semester 2 (On Campus - Parkville)
 12.5

MC-COMACTS Master of Commerce (Actuarial Science)

    Corequisites

    None

    Non-allowed subjects

    None

    Recommended background knowledge

    Statistical modelling; actuarial modelling

    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: 4 March 2025

    Assessment

    DescriptionTimingPercentage

    Individual Written Assignment 1

    • 1000 words (equivalent)
    		From Week 7 to Week 820%

    Individual Written Assignment 2

    • 1000 words (equivalent)
    		From Week 11 to Week 1220%

    End of Semester Examination

    • 3 hours
    Hurdle requirement: Successful completion of this subject requires a pass (50/100) in the final exam.    		During the examination period60%

    Last updated: 4 March 2025

    Dates & times

    • Semester 2
      CoordinatorGenevieve Hayes
      Mode of deliveryOn Campus (Parkville)
      Contact hours2 hours of lectures and a 1 hour tutorial per week
      Total time commitment170 hours
      Teaching period22 July 2024 to 20 October 2024
      Last self-enrol date 2 August 2024
      Census date 2 September 2024
      Last date to withdraw without fail20 September 2024
      Assessment period ends15 November 2024

      Semester 2 contact information

      Genevieve Hayes: genevieve.hayes@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: 4 March 2025

    Further information

    • Texts

      Prescribed texts

      Main textbook: Raschka, S. and Mirjalili, V. (2019). Python Machine Learning (3rd ed). Packt.
      Supplementary textbook (optional): Geron, A. (2022). Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow (3rd ed). O’Reilly.

    • 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.

      Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants

      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: 4 March 2025