# Search Course Outline

### Showing 25 course outlines from 2938 matches

2201

#### STATS 330

: Statistical Modelling2021 Semester Two (1215)

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: 15 points from STATS 201, 207, 208, BIOSCI 209

2202

#### STATS 330

: Statistical Modelling2021 Semester One (1213)

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: 15 points from STATS 201, 207, 208, BIOSCI 209

2203

#### STATS 330

: Statistical Modelling2020 Semester Two (1205)

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: 15 points from STATS 201, 207, 208, BIOSCI 209

2204

#### STATS 330

: Statistical Modelling2020 Semester One (1203)

Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209

2205

#### STATS 331

: Introduction to Bayesian Statistics2023 Semester Two (1235)

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: ENGSCI 314 or STATS 201 or 208

2206

#### STATS 331

: Introduction to Bayesian Statistics2022 Semester Two (1225)

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 STATS 201, 207, 208, BIOSCI 209

2207

#### STATS 331

: Introduction to Bayesian Statistics2021 Semester Two (1215)

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 STATS 201, 207, 208, BIOSCI 209

2208

#### STATS 331

: Introduction to Bayesian Statistics2020 Semester Two (1205)

Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209

2209

#### STATS 369

: Data Science Practice2023 Semester Two (1235)

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 314

Restriction: STATS 765

Restriction: STATS 765

2210

#### STATS 369

: Data Science Practice2022 Semester Two (1225)

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 BIOSCI 209, ECON 221, STATS 201, 207, 208

Restriction: STATS 765

Restriction: STATS 765

2211

#### STATS 369

: Data Science Practice2021 Semester Two (1215)

Modern predictive modelling techniques, with application to realistically large data sets. Case studies will be drawn from business, industrial, and government applications.

Prerequisite: 15 points from BIOSCI 209, ECON 211, STATS 201, 207, 208, and STATS 210 or 225

Restriction: STATS 765

Restriction: STATS 765

2212

#### STATS 369

: Data Science Practice2020 Semester Two (1205)

Prerequisite: STATS 220, 201 or 208, 210 or 225

Restriction: STATS 765

Restriction: STATS 765

2213

#### STATS 370

: Financial Mathematics2023 Semester Two (1235)

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

Restriction: STATS 722

2214

#### STATS 370

: Financial Mathematics2022 Semester Two (1225)

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; 15 points at Stage II in Mathematics

Restriction: STATS 722

Restriction: STATS 722

2215

#### STATS 370

: Financial Mathematics2020 Semester Two (1205)

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; 15 points at Stage II in Mathematics

Restriction: STATS 722

Restriction: STATS 722

2216

#### STATS 380

: Statistical Computing2023 Semester Two (1235)

Statistical programming using the R computing environment. Data structures, numerical computing and graphics.

Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220

2217

#### STATS 380

: Statistical Computing2023 Semester One (1233)

Statistical programming using the R computing environment. Data structures, numerical computing and graphics.

Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220

2218

#### STATS 380

: Statistical Computing2022 Semester Two (1225)

Statistical programming using the R computing environment. Data structures, numerical computing and graphics.

Prerequisite: 15 points from STATS 201, 207, 208, 220, BIOSCI 209

2219

#### STATS 380

: Statistical Computing2021 Semester Two (1215)

Prerequisite: 15 points from STATS 201, 207, 208, 220, BIOSCI 209

2220

#### STATS 380

: Statistical Computing2020 Semester Two (1205)

Prerequisite: 15 points from STATS 201, 207, 208, 220, BIOSCI 209

2221

#### STATS 383

: The Science and Craft of Data Management2023 Semester Two (1235)

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

2222

#### STATS 383

: The Science and Craft of Data Management2022 Semester Two (1225)

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: STATS 201, 207, or 208 or BIOSCI 209; and STATS 220 or COMPSCI 101

2223

#### STATS 399

: Capstone: Statistics in Action2023 Semester Two (1235)

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

2224

#### STATS 399

: Capstone: Statistics in Action2022 Semester Two (1225)

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

2225

#### STATS 399

: Capstone: Statistics in Action2021 Semester Two (1215)

Provides opportunities to integrate statistical knowledge and collaborate with others through completion of a group-based project.

Prerequisite: 30 points at Stage III in Statistics

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