1. Handbook
  2. Subjects
  3. Computational Statistics & Data Science

Computational Statistics & Data Science (MAST90083)

Graduate courseworkPoints: 12.5On Campus (Parkville)

You’re viewing the 2019 Handbook:
Or view archived Handbooks


Year of offer2019
Subject levelGraduate coursework
Subject codeMAST90083
Semester 2
FeesSubject EFTSL, Level, Discipline & Census Date

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, generalised additive models, classification and regression trees, forward stagewise and gradient boosting models. Important statistical computing algorithms and techniques used in data science will be explained in detail. These include the bootstrap resampling and inference, cross-validation, the EM algorithm and Louis method, and Markov chain Monte Carlo methods including adaptive rejection and squeeze sampling, sequential importance sampling, slice sampling, Gibbs sampler and Metropolis-Hastings algorithm.

Intended learning outcomes

After completing this subject students should gain:

1. an understanding of theory and computing in modern statistics and data science, and how they are implemented in applications;

2. the skills of using nonparametric and Monte Carlo methods in statistics and data science; and

3. the ability to pursue further studies in this 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: 21 August 2019