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Computational Statistics & Data Science (MAST90083)
Graduate courseworkPoints: 12.5On Campus (Parkville)
Overview
Availability | Semester 2 |
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Fees | Look up fees |
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: 8 November 2024