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Showing 25 course outlines from 794 matches
601
STATS 705
: Topics in Official Statistics2025 Semester Two (1255)
Official statistics, data access, data quality, demographic and health statistics, other social statistics, economic statistics, analysis and presentation, case studies in the use of official statistics.
No pre-requisites or restrictions
602
STATS 707
: Computational Introduction to Statistics2025 Semester One (1253)
An advanced introduction to statistics and data analysis, including testing, estimation, and linear regression.
Prerequisite: 15 points from STATS 101, 108 and 15 points from COMPSCI 101, MATHS 162
Restriction: ENGSCI 314, STATS 201, 207, 208, 210, 225
Restriction: ENGSCI 314, STATS 201, 207, 208, 210, 225
603
STATS 708
: Topics in Statistical Education2025 Semester One (1253)
Covers a wide range of research in statistics education at the school and tertiary level. There will be a consideration of, and an examination of, the issues involved in statistics education in the curriculum, teaching, learning, technology and assessment areas.
No pre-requisites or restrictions
604
STATS 709
: Predictive Modelling2025 Semester Two (1255)
Predictive modelling forecasts likely future outcomes based on historical and current data. Following an advanced introduction to statistics and data analysis, the course will discuss concepts for modern predictive modelling and machine learning.
Prerequisite: COMPSCI 130, MATHS 108, and 15 points from STATS 101, 108, or equivalent
Restriction: STATS 201, 207, 208, 210, 225, 707, 765
Restriction: STATS 201, 207, 208, 210, 225, 707, 765
605
STATS 710
: Probability Theory2025 Semester Two (1255)
Fundamental ideas in probability theory; sigma-fields, laws of large numbers, characteristic functions, the Central Limit Theorem, modes of convergence. Advanced topics may include Poisson random measures, random trees, Lévy processes, random spatial models. Students will undertake assigned individual research projects based on a journal article or advanced textbook, including a detailed explanation of the techniques of probability theory exemplified therein.
Prerequisite: B+ or higher in STATS 225 or 15 points from STATS 310, 320, 325
606
STATS 721
: Foundations of Stochastic Processes2025 Semester Two (1255)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Prerequisite: 15 points from STATS 125, 210, 225, 320 with at least a B+ and 15 points from MATHS 208, 250, 253
Restriction: STATS 325
Restriction: STATS 325
607
STATS 726
: Time Series2025 Semester Two (1255)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Prerequisite: STATS 210, and 15 points from STATS 326, 786
608
STATS 730
: Statistical Inference2025 Semester Two (1255)
Fundamental topics in estimation and statistical inference. Advanced topics in modelling including regression with dependent data, survival analysis, methods to handle missing data. Advanced topics in current statistical practice researched by students. Students will undertake and present individual research projects on assigned topics, consisting in a literature search and a computational application to a data analysis task.
Prerequisite: STATS 310 or 732
609
STATS 731
: Bayesian Inference2025 Semester Two (1255)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Prerequisite: STATS 331 and 15 points from STATS 210, 225
610
STATS 732
: Foundations of Statistical Inference2025 Semester One (1253)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
Restriction: STATS 310
611
STATS 762
: Regression for Data Science2025 Semester One (1253)
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Prerequisite: 15 points from STATS 210, 225, 707, and 15 points from ENGSCI 314, STATS 201, 207, 208
Restriction: STATS 330
Restriction: STATS 330
612
STATS 763
: Advanced Regression Methodology2025 Semester Two (1255)
Generalised linear models, generalised additive models, survival analysis. Smoothing and semiparametric regression. Marginal and conditional models for correlated data. Model selection for prediction and for control of confounding. Model criticism and testing. Computational methods for model fitting, including Bayesian approaches.
Prerequisite: STATS 210 or 225, and 15 points from STATS 330, 762 and 15 points at Stage II in Mathematics
613
STATS 765
: Statistical Learning for Data Science2025 Semester Two (1255)
Concepts of modern predictive modelling and machine learning such as loss functions, overfitting, generalisation, regularisation, sparsity. Techniques including regression, recursive partitioning, boosting, neural networks. Application to real data sets from a variety of sources, including data quality assessment, data preparation and reporting.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 207, 208 and 15 points from STATS 210, 225, 707
Corequisite: May be taken with STATS 707
Restriction: STATS 369
Restriction: STATS 369
614
STATS 767
: Foundations of Applied Multivariate Analysis2025 Semester One (1253)
Fundamentals of exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 207, 208, 707
Restriction: STATS 302
Restriction: STATS 302
615
STATS 768
: Longitudinal Data Analysis2025 Semester Two (1255)
Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 207, 208, 210, 707
616
STATS 769
: Advanced Data Science Practice2025 Semester Two (1255)
Databases, SQL, scripting, distributed computation, other data technologies.
Prerequisite: 15 points from STATS 220, 369, 380 and 15 points from ENGSCI 314, STATS 201, 207, 208, 707
617
STATS 770
: Introduction to Medical Statistics2025 Semester One (1253)
An introduction to ideas of importance in medical statistics, such as measures of risk, basic types of medical study, causation, ethical issues and censoring, together with a review of common methodologies.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 207, 208 and 15 points from STATS 210, 225, 707
618
STATS 773
: Design and Analysis of Clinical Trials2025 Semester One (1253)
The theory and practice of clinical trials, including: design issues, data management, common analysis methodologies, intention to treat, compliance, interim analyses and ethical considerations.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 207, 208, 707
619
STATS 776
: Estimating Animal Abundance2025 Semester One (1253)
Fundamentals of the statistical methods that underly capture-recapture, distance sampling and occupancy analysis, focusing on the critical role that p, the probability of detection, plays in estimating n, the number of animals, or psi, the probability of species presence. Extensions to these fundamental tools including spatially explicit, genetic, and hierarchical methods will be covered.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 207, 208, 707
620
STATS 779
: Professional Skills for Statisticians2025 Semester One (1253)
Statistical software, data management, data integrity, data transfer, file processing, symbolic manipulation, document design and presentation, oral presentation, professional ethics.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 707
621
STATS 782
: Statistical Computing2025 Semester Two (1255)
Professional skills, advanced statistical programming, numerical computation and graphics.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 707
622
STATS 782
: Statistical Computing2025 Semester One (1253)
Professional skills, advanced statistical programming, numerical computation and graphics.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 707
623
STATS 786
: Time Series Forecasting for Data Science2025 Semester One (1253)
Delivers a comprehensive understanding of widely used time series forecasting methods, illustrates how to build models to uncover the structure in time series and perform model diagnostics to assess the fit of models, and develops analytical and computer skills that are necessary for analysing time series data. Familiarity with coding in R is recommended.
Prerequisite: 15 points from STATS 201, 208
Restriction: STATS 326, 727
Restriction: STATS 326, 727
624
STATS 787
: Data Visualisation2025 Semester One (1253)
Effective visual presentations of data. Topics may include: how to present different types of data; human perception; graphics formats; statistical graphics in R; interactive graphics; visualising high-dimensional data; visualising large data.
Prerequisite: 15 points from STATS 220, 369, 380 and 15 points from ENGSCI 314, STATS 201, 207, 208, 707
625
SUSTAIN 100
: Sustainability and Us2025 Semester Two (1255)
What is sustainability? The course discusses what sustainability means, and its underpinning values, history and operation within complex physical systems. Students complete a group project to develop skills in collective decision making with a solution focus. The course explores two sustainability issues in depth.
No pre-requisites or restrictions