DTSC71-302: Statistical Learning and Regression Models

Description

This subject covers the theory and practice of modern statistical learning, regression and classification modelling. Techniques covered range from traditional model selection and generalised linear model structures to modern, computer-intensive methods including generalised additive models, splines and tree methods. Methods to handle continuous, ordinal and nominal response variables and assessment of fit via cross-validation and residual diagnostics are also considered.  All techniques will be investigated via practical application on real data using the statistical software package R.

Subject details

Type: Postgraduate Subject
Code: DTSC71-302
EFTSL: 0.125
Faculty: Bond Business School
Semesters offered:
  • May 2026 [Standard Offering]
  • September 2026 [Standard Offering]
Credit: 10
Study areas:
  • Actuarial Science and Data Analytics
Subject fees:
  • Commencing in 2025: $5,520.00
  • Commencing in 2026: $5,630.00
  • Commencing in 2027: $5,830.00
  • Commencing in 2025: $6,340.00
  • Commencing in 2026: $6,630.00
  • Commencing in 2027: $6,930.00

Learning outcomes

  1. Demonstrate detailed knowledge of the limitations of linear regression models and the ability to develop and interpret an appropriate regression model given the circumstance.
  2. Appropriately choose between applications of a variety of linear and generalised linear regression modelling structures, including regularisation, dimension reduction and sequential variable selection.
  3. Critically evaluate non-normal dependent variable regression and classification models, including the predictive and structural characteristics of binomial outcome models and associated methods to assess their discrimination and calibration within specific contexts.
  4. Develop prediction models utilising splines, polynomials, recursive partitioning and general additive methods and critically compare and contrast their results.
  5. Demonstrate ability to produce creative, contextual and well-documented analytic solutions addressing a potentially multifaceted issue or problem.
  6. Demonstrate ability to clearly produce verbal, visual and professional communications of the results of a statistical learning investigation; in particular, ensuring outcomes are carefully presented in appropriate language and level of detail depending on the audience being addressed.

Enrolment requirements

Requisites:

Nil
Assumed knowledge:

Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.

Assumed Prior Learning (or equivalent):

Possess demonstrable knowledge in the theory and application of simple and multiple linear regression models to subject levels, such as Linear Models and Applied Econometrics, as well as basic data science concepts and techniques to subject levels, such as Data Science.
Restrictions:

Subject outlines

Subject dates

  • Standard Offering
    Enrolment opens: 22/03/2026
    Semester start: 18/05/2026
    Subject start: 18/05/2026
    Last enrolment: 31/05/2026
    Teaching census: 12/06/2026
    Withdraw - Financial: 13/06/2026
    Withdraw - Academic: 04/07/2026
  • Standard Offering
    Enrolment opens: 19/07/2026
    Semester start: 14/09/2026
    Subject start: 14/09/2026
    Last enrolment: 27/09/2026
    Teaching census: 09/10/2026
    Withdraw - Financial: 10/10/2026
    Withdraw - Academic: 31/10/2026
Standard Offering
Enrolment opens: 22/03/2026
Semester start: 18/05/2026
Subject start: 18/05/2026
Last enrolment: 31/05/2026
Teaching census: 12/06/2026
Withdraw - Financial: 13/06/2026
Withdraw - Academic: 04/07/2026