# Search Course Outline

### Showing 25 course outlines from 3702 matches

2776

#### 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

2777

#### STATS 330

: Statistical Modelling2020 Semester One (1203)

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

2778

#### STATS 331

: Introduction to Bayesian Statistics2024 Semester Two (1245)

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

2779

#### 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

2780

#### 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

2781

#### STATS 331

: Introduction to Bayesian Statistics2021 Semester Two (1215)

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

2782

#### STATS 331

: Introduction to Bayesian Statistics2020 Semester Two (1205)

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

2783

#### STATS 369

: Data Science Practice2024 Semester Two (1245)

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

Restriction: STATS 765

2784

#### 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

2785

#### 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

2786

#### STATS 369

: Data Science Practice2021 Semester Two (1215)

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

Restriction: STATS 765

Restriction: STATS 765

2787

#### STATS 369

: Data Science Practice2020 Semester Two (1205)

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

Restriction: STATS 765

Restriction: STATS 765

2788

#### STATS 370

: Financial Mathematics2024 Semester Two (1245)

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

2789

#### 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

2790

#### 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

2791

#### STATS 370

: Financial Mathematics2020 Semester Two (1205)

Prerequisite: 15 points at Stage II in Statistics or BIOSCI 209; 15 points at Stage II in Mathematics

Restriction: STATS 722

Restriction: STATS 722

2792

#### STATS 380

: Statistical Computing2024 Semester Two (1245)

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

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

2793

#### STATS 380

: Statistical Computing2024 Semester One (1243)

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

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

2794

#### 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

2795

#### STATS 380

: Statistical Computing2023 Semester One (1233)

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

2796

#### STATS 380

: Statistical Computing2022 Semester Two (1225)

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

2797

#### STATS 380

: Statistical Computing2021 Semester Two (1215)

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

2798

#### STATS 380

: Statistical Computing2020 Semester Two (1205)

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

2799

#### STATS 383

: The Science and Craft of Data Management2024 Semester Two (1245)

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

2800

#### 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

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