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Courses - Faculty of Science


Statistics

Stage I

STATS 100
15 Points

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

STATS 101
15 Points

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

STATS 108
15 Points

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

STATS 125
15 Points

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

STATS 150
15 Points

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

STATS 201
15 Points

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

STATS 208
15 Points

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

STATS 210
15 Points

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

STATS 220
15 Points

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

STATS 225
15 Points

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

STATS 240
15 Points

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

STATS 255
15 Points

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

STATS 302
15 Points

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

STATS 310
15 Points

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

STATS 313
15 Points

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

STATS 320
15 Points

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

STATS 325
15 Points

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

STATS 326
15 Points

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

STATS 330
15 Points

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

STATS 331
15 Points

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

STATS 369
15 Points

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

STATS 370
15 Points

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

STATS 380
15 Points

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

STATS 383
15 Points

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

STATS 392
15 Points

Directed Study

Directed study on a topic from Data Science, Statistics or Probability approved by the Academic Head or nominee.

STATS 399
15 Points

Capstone: Statistics in Action

Provides opportunities to integrate knowledge in statistics and data science, and collaborate with others through a succession of group projects and activities.

Prerequisite: 30 points at Stage III in Statistics

Postgraduate 700 Level Courses

STATS 701
15 Points

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

STATS 702
15 Points

Special Topic in Statistics 2

STATS 703
15 Points

Special Topic in Statistics 1

STATS 705
15 Points

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.

STATS 707
15 Points

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

STATS 708
15 Points

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.

STATS 709
30 Points

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

STATS 710
15 Points

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

STATS 720
15 Points

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

STATS 721
15 Points

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

STATS 722
15 Points

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

STATS 725
15 Points

Special Topic

STATS 726
15 Points

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

STATS 727
15 Points

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

STATS 730
15 Points

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

STATS 731
15 Points

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

STATS 732
15 Points

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

STATS 740
15 Points

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

STATS 741
15 Points

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

STATS 747
15 Points

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

STATS 750
15 Points

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

STATS 761
15 Points

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.

STATS 762
15 Points

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

STATS 763
15 Points

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

STATS 765
15 Points

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

STATS 766
15 Points

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

STATS 767
15 Points

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

STATS 768
15 Points

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

STATS 769
15 Points

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

STATS 770
15 Points

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

STATS 771
15 Points

Special Topic

STATS 773
15 Points

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

STATS 774
60 Points

STATS 774A
30 Points

STATS 774B
30 Points

Dissertation in Statistics Education - Level 9

To complete this course students must enrol in STATS 774 A and B, or STATS 774

STATS 776
15 Points

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

STATS 779
15 Points

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

STATS 780
15 Points

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

STATS 781
30 Points

STATS 781A
15 Points

STATS 781B
15 Points

Research Project - Level 9

Restriction: STATS 789

To complete this course students must enrol in STATS 781 A and B, or STATS 781

STATS 782
15 Points

Statistical Computing

Professional skills, advanced statistical programming, numerical computation and graphics.

Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 707

STATS 783
15 Points

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.

STATS 784
15 Points

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

STATS 785
15 Points

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

STATS 786
15 Points

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

STATS 787
15 Points

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

STATS 790
30 Points

STATS 790A
15 Points

STATS 790B
15 Points

Research Project - Level 9

Restriction: STATS 796

To complete this course students must enrol in STATS 790 A and B, or STATS 790

STATS 792
45 Points

STATS 792A
22.5 Points

STATS 792B
22.5 Points

Dissertation in Statistics Education - Level 9

To complete this course students must enrol in STATS 792 A and B, or STATS 792

STATS 793
45 Points

STATS 793A
22.5 Points

STATS 793B
22.5 Points

Dissertation - Level 9

To complete this course students must enrol in STATS 793 A and B, or STATS 793

STATS 796A
60 Points

STATS 796B
60 Points

MSc Thesis in Statistics - Level 9

To complete this course students must enrol in STATS 796 A and B

STATS 798A
45 Points

STATS 798B
45 Points

Masters Thesis in Statistics - Level 9

Prerequisite: 15 points from STATS 310, 732 and 15 points from STATS 330, 762, or approval of Head of Department

Restriction: STATS 790, 796

To complete this course students must enrol in STATS 798 A and B

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