Computational Statistics & Data Science (MAST90083)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 2
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Computing techniques and data mining methods are indispensable in modern statistical research and data science applications, where "Big Data" problems are often involved. This subject will introduce a number of recently developed methods and applications in computational statistics and data science that are scalable to large datasets and high-performance computing. The data mining methods to be introduced include general model diagnostic and assessment techniques, kernel and local polynomial nonparametric regression, basis expansion and nonparametric spline regression, and generalised additive models. Important statistical computing algorithms and techniques used in data science will be explained in detail. These include unsupervised learning of meaningful components, bootstrap resampling and inference, cross-validation, the EM algorithm and variational approximation, and Markov chain Monte Carlo methods including adaptive rejection and squeeze sampling, sequential importance sampling, slice sampling, Gibbs samplers and the Metropolis--Hastings algorithm.

Intended learning outcomes

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

  • Explain the role of theory and computing in modern statistics and data science, and how they are implemented in applications;
  • Apply nonparametric and Monte Carlo methods in statistics and data science; and
  • Develop key knowledge to pursue further studies in this discipline and related areas, or to be work ready as an applied statistician or a data scientist.

Generic skills

In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:

  • problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
  • analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
  • collaborative skills: the ability to work in a team;
  • time-management skills: the ability to meet regular deadlines while balancing competing commitments

Last updated: 4 March 2025

Eligibility and requirements

Prerequisites

Code Name Teaching period Credit Points
MAST30025 Linear Statistical Models
Semester 1 (On Campus - Parkville)
 12.5

OR

Note: the following subject/s can also be taken concurrently (at the same time):

One of

Code Name Teaching period Credit Points
MAST30027 Modern Applied Statistics
Semester 2 (On Campus - Parkville)
 12.5
MAST90104 A First Course In Statistical Learning
Semester 2 (On Campus - Parkville)
 25

OR

Admission into the Statistical Data Science specialisation (formal) in the MC-DATASC Master of Data Science

OR

Admission into the Computational and Statistical Data Science specialisation (formal) in the MC-DATASC Master of Data Science

OR

Admission into one of:

• Master of Data Science (MC-DATASC)- Statistics Background Stream (pre-2025)

• Master of Data Science (MC-DATASC) - Data Science Background Stream (pre-2025)

Corequisites

None

Non-allowed subjects

None

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

Two written assignments, up to a total of 1000 words each (each written assignment is worth 20%, approx. 20 hours time commitment) due mid and late semester (40%)

  • 2000 words
During the teaching period40%

Written examination

  • 3 hours
Hurdle requirement: Students must receive a mark of 50% or more for the written exam to pass the subject.		During the examination period60%

Last updated: 4 March 2025

Dates & times

  • Semester 2
    Principal coordinatorKarim Seghouane
    Mode of deliveryOn Campus (Parkville)
    Contact hoursContact Hours: 36 hours comprising 2 one-hour lectures per week and 1 one-hour practice class per week.
    Total time commitment170 hours
    Teaching period28 July 2025 to 26 October 2025
    Last self-enrol date 8 August 2025
    Census date 1 September 2025
    Last date to withdraw without fail26 September 2025
    Assessment period ends21 November 2025

    Semester 2 contact information

    abd-krim.seghouane@unimelb.edu.au

Time commitment details

Estimated Total Time Commitment - 170 hours

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

Last updated: 4 March 2025