Courses - Faculty of Science
Statistics
Stage I
Concepts in Statistics
A first exposure to statistics that builds data handling and literacy skills and develops conceptual thinking through active participation in problems using real data, computer simulations and group work. STATS 100 makes full use of appropriate technology and prepares students to use statistics in their own disciplines.
Restriction: May not be taken with, or after passing, any other Statistics course
Introduction to Statistics
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test.
Restriction: STATS 102, 107, 108, 191
Statistics for Commerce
The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce.
Restriction: STATS 101, 102, 107, 191
Probability and its Applications
Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk.
Corequisite: ENGSCI 111 or MATHS 108 or 110 or 120 or 130
Restriction: STATS 210
Communicating Statistics
Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news.
Stage II
Data Analysis
A practical course using the R language in the statistical analysis of data and the interpretation and communication of statistical findings. Includes exploratory data analysis, analysis of linear models including multiple regression and analysis of variance, generalised linear models including logistic regression and analysis of counts, time series analysis.
Prerequisite: 15 points from STATS 101-108, 191
Restriction: STATS 207, 208
Data Analysis for Commerce
A practical course using the popular R language in the statistical analysis of data and the interpretation and communication of statistical findings. Includes exploratory data analysis, analysis of linear models including multiple regression and analysis of variance, generalised linear models including logistic regression and analysis of counts, time series analysis.
Prerequisite: 15 points from STATS 101-108, 191
Restriction: STATS 201, 207
Statistical Theory
Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing.
Prerequisite: 15 points from ENGSCI 111, ENGGEN 150, STATS 125
Corequisite: 15 points from MATHS 208, 250, ENGSCI 211 or equivalent
Data Technologies
Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes.
Prerequisite: 15 points at Stage I in Computer Science or Statistics
Probability: Theory and Applications
Covers the fundamentals of probability through theory, methods, and applications. Topics should include the classical limit theorems of probability and statistics known as the laws of large numbers and central limit theorem, conditional expectation as a random variable, the use of generating function techniques, and key properties of some fundamental stochastic models such as random walks, branching processes and Poisson point processes.
Prerequisite: B+ or higher in ENGGEN 150 or ENGSCI 111 or STATS 125, or a B+ or higher in MATHS 120 and 130
Corequisite: 15 points from ENGSCI 211, MATHS 208, 250
Design and Structured Data
An introduction to research study design and the analysis of structured data. Blocking, randomisation, and replication in designed experiments. Clusters, stratification, and weighting in samples. Other examples of structured data.
Prerequisite: STATS 101 or 108
Restriction: STATS 340
Optimisation and Data-driven Decision Making
Explores methods for using data to assist in decision making in business and industrial applications. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models, simulation, analytics and visualisation will be considered.
Prerequisite: ENGSCI 211 or STATS 201 or 208, or a B+ or higher in either MATHS 108 or 120 or 130 or 162 or 199 or STATS 101 or 108, or a concurrent enrolment in either ENGSCI 211 or STATS 201 or 208
Restriction: ENGSCI 255
Stage III
Applied Multivariate Analysis
Covers the 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: ENGSCI 314 or STATS 201 or 208
Restriction: STATS 767
Introduction to Statistical Inference
Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models.
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250 or equivalent
Restriction: STATS 732
Advanced Topics in Probability
Characterisations of and relations between different kinds of random objects including random functions, random paths and random trees. Modes of convergence; the Law of Large Numbers and Central Limit Theorem.
Prerequisite: STATS 225
Restriction: STATS 710
Applied Stochastic Modelling
Construction, analysis and simulation of stochastic models, and optimisation problems associated with them. Poisson process, Markov chains, continuous-time Markov processes. Equilibrium distribution, reaching probabilities and times, transient behaviour. Use of R to simulate simple stochastic processes. Examples drawn from a range of applications including operations research, biology, and finance.
Prerequisite: 15 points from STATS 125, 210, 225 and 15 points from STATS 201, 208, 220, or ENGSCI 314
Stochastic Processes
Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks.
Prerequisite: B+ or higher in STATS 125 or B or higher in ENGSCI 314 or STATS 210 or 225 or 320, and 15 points from ENGSCI 211, MATHS 208, 250
Restriction: STATS 721
Applied Time Series Analysis
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Prerequisite: 15 points from ECON 211, ENGSCI 314, STATS 201, 208
Restriction: STATS 727
Statistical Modelling
Application of the generalised linear model and extensions to fit data arising from a range of sources including multiple regression models, logistic regression models, and log-linear models. The graphical exploration of data.
Prerequisite: ENGSCI 314 or STATS 201 or 208
Introduction to Bayesian Statistics
Introduces Bayesian data analysis using the WinBUGS software package and R. Topics include the Bayesian paradigm, hypothesis testing, point and interval estimates, graphical models, simulation and Bayesian inference, diagnosing MCMC, model checking and selection, ANOVA, regression, GLMs, hierarchical models and time series. Classical and Bayesian methods and interpretations are compared.
Prerequisite: 15 points from ENGSCI 263, STATS 201, 208 and 15 points from ENGSCI 111, ENGGEN 150, STATS 125
Data Science Practice
Modern predictive modelling techniques, with application to realistically large data sets. Case studies will be drawn from business, industrial, and government applications.
Prerequisite: STATS 220 and STATS 210 or 225 and 15 points from ECON 221, STATS 201, 208, or ENGSCI 233 and 263
Restriction: STATS 765
Financial Mathematics
Mean-variance portfolio theory; options, arbitrage and put-call relationships; introduction of binomial and Black-Scholes option pricing models; compound interest, annuities, capital redemption policies, valuation of securities, sinking funds; varying rates of interest, taxation; duration and immunisation; introduction to life annuities and life insurance mathematics.
Prerequisite: 15 points at Stage II in Mathematics and 15 points at Stage II in Statistics
Restriction: STATS 722
Statistical Computing
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220
The Science and Craft of Data Management
A structured introduction to the science and craft of data management, including: data representations and their advantages and disadvantages; workflow and data governance; combining and splitting data sets; data cleaning; the creation of non-trivial summary variables; and the handling of missing data. These will be illustrated by data sets of varying size and complexity, and students will implement data processing steps in at least two software systems.
Prerequisite: ENGSCI 314 or STATS 201 or 208, and COMPSCI 101 or ENGSCI 233 or STATS 220
Directed Study
Directed study on a topic from Data Science, Statistics or Probability approved by the Academic Head or nominee.
Postgraduate 700 Level Courses
Advanced SAS Programming
A continuation of STATS 301, with more in-depth coverage of programming in the SAS language. Topics covered will include advanced use of the SAS language, advanced data step programming, macros, input and output, connectivity to other software platforms, SAS SQL.
Prerequisite: STATS 301
Topics in Official Statistics
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.
Computational Introduction to Statistics
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
Topics in Statistical Education
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.
Predictive Modelling
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
Probability Theory - Level 9
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
Stochastic Processes
Stochastic models and their applications. Discrete and continuous-time jump Markov processes. A selection of topics from point processes, renewal theory, Markov decision processes, stochastic networks, inference for stochastic processes, simulation of stochastic processes, and computational methods using R.
Prerequisite: STATS 320 or 325
Foundations of Stochastic Processes
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
Foundations of Financial Mathematics
Fundamentals of financial mathematics. Topics include: mean-variance portfolio theory; options, arbitrage and put-call relationships; introduction of binomial and Black-Scholes option pricing models; compound interest, annuities, capital redemption policies, valuation of securities, sinking funds; varying rates of interest, taxation; duration and immunisation; introduction to life annuities and life insurance mathematics.
Prerequisite: 15 points at Stage II in Statistics or BIOSCI 209, and 15 points at Stage II in Mathematics
Restriction: STATS 370
Time Series
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Prerequisite: STATS 210, and 15 points from STATS 326, 786
Foundations of Applied Time Series Analysis
Fundamentals of applied time series analysis. Topics include: components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas are presented.
Prerequisite: 15 points from ECON 221, STATS 201, 207, 208, 707
Restriction: STATS 326
Statistical Inference - Level 9
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
Bayesian Inference
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
Foundations of Statistical Inference
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
Sample Surveys
The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non-sampling issues; Project management; Maintaining data quality. Concluding surveys: Analysis; Dissemination.
Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics
Sample Surveys and Experimental Design
Design, implementation and analysis of sample surveys and of experiments. This course covers the foundations of both areas.
Prerequisite: 15 points from STATS 201, 207, 208
Restriction: STATS 340
Statistical Methods in Marketing
Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.
Prerequisite: 15 points from STATS 201, 207, 208, 210, 707
Experimental Design
The design and analysis of data from experiments involving factorial and related designs and designs which have the property known as general balance (this includes most of the standard designs), and more general designs with blocking and replication. Response surface methodology. Sequential experimentation.
Prerequisite: 15 points from STATS 240, 330, 340, 762
Mixed Models
Linear mixed effect models for the analysis of data from small experiments, particularly those cases where the data are unbalanced. Methods include restricted maximum likelihood for the estimation of variance components.
Regression for Data Science
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
Advanced Regression Methodology
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
Statistical Learning for Data Science
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
Multivariate Analysis
A selection of topics from multivariate analysis, including: advanced methods of data display (e.g., Correspondence and Canonical Correspondence Analysis, Biplots, and PREFMAP) and an introduction to classification methods (e.g., various types of Discriminant Function Analysis).
Prerequisite: STATS 310 or 732
Foundations of Applied Multivariate Analysis
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
Longitudinal Data Analysis
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
Advanced Data Science Practice
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
Introduction to Medical Statistics
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
Design and Analysis of Clinical Trials
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
Dissertation in Statistics Education - Level 9
To complete this course students must enrol in STATS 774 A and B, or STATS 774
Estimating Animal Abundance
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
Professional Skills for Statisticians
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
Statistical Consulting
Students will learn about the practicalities of statistical consulting. Students will carry out a statistical consulting project, including the writing of a report, under the supervision of a member of the academic staff.
Prerequisite: STATS 330 or 762
Research Project - Level 9
Restriction: STATS 789
To complete this course students must enrol in STATS 781 A and B, or STATS 781
Statistical Computing
Professional skills, advanced statistical programming, numerical computation and graphics.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 707
Simulation and Monte Carlo Methods
A practical introduction to modern simulation and Monte Carlo techniques and their use to simulate real situations and to solve difficult statistical inferential problems whose mathematical analysis is intractable.
Statistical Data Mining
Data cleaning, missing values, data warehouses, security, fraud detection, meta-analysis, and statistical techniques for data mining such as regression and decision trees, modern and semiparametric regression, neural networks, statistical approaches to the classification problem.
Prerequisite: 15 points from STATS 210, 225, and 15 points from STATS 330, 762
Foundations of Statistical Data Management
SAS statistical software with an emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 207, 208, 707
Restriction: STATS 301
Time Series Forecasting for Data Science
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
Data Visualisation
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
Research Project - Level 9
Restriction: STATS 796
To complete this course students must enrol in STATS 790 A and B, or STATS 790
Dissertation in Statistics Education - Level 9
To complete this course students must enrol in STATS 792 A and B, or STATS 792
Dissertation - Level 9
To complete this course students must enrol in STATS 793 A and B, or STATS 793
MSc Thesis in Statistics - Level 9
To complete this course students must enrol in STATS 796 A and B