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Showing 25 course outlines from 2938 matches

2276

STATS 760

: A Survey of Modern Applied Statistics
2020 Semester One (1203)
A survey of techniques from modern applied statistics. Topics covered will be linear, non-linear and generalised linear models, modern regression including CART and neural networks, mixed models, survival analysis, time series and spatial statistics.
Subject: Statistics
Prerequisite: STATS 310, 330
2277

STATS 762

: Regression for Data Science
2023 Semester One (1233)
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.
Subject: Statistics
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
2278

STATS 762

: Regression for Data Science
2022 Semester One (1223)
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.
Subject: Statistics
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
2279

STATS 762

: Regression for Data Science
2021 Semester One (1213)
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.
Subject: Statistics
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
2280

STATS 762

: Regression for Data Science
2020 Semester One (1203)
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.
Subject: Statistics
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
2281

STATS 763

: Advanced Regression Methodology
2023 Semester Two (1235)
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.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from STATS 330, 762 and 15 points at Stage II in Mathematics
2282

STATS 763

: Advanced Regression Methodology
2022 Semester One (1223)
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.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from STATS 330, 762 and 15 points at Stage II in Mathematics
2283

STATS 763

: Advanced Regression Methodology
2021 Semester One (1213)
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.
Subject: Statistics
Prerequisite: STATS 210 and 225, and 15 points from STATS 330, 762 and 15 points at Stage II in Mathematics
2284

STATS 763

: Advanced Regression Methodology
2020 Semester One (1203)
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.
Subject: Statistics
No pre-requisites or restrictions
2285

STATS 765

: Statistical Learning for Data Science
2023 Semester One (1233)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201 or 207 or 208 and 15 points from STATS 210 or 225, or STATS 707 Corequisite: May be taken with STATS 707
Restriction: STATS 369
2286

STATS 765

: Statistical Learning for Data Science
2022 Semester One (1223)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201 or 207 or 208 and 15 points from STATS 210 or 225, or STATS 707 Corequisite: May be taken with STATS 707
Restriction: STATS 369
2287

STATS 765

: Statistical Learning for Data Science
2021 Semester One (1213)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201 or 207 or 208 and 15 points from STATS 210 or 225, or STATS 707 Corequisite: May be taken with STATS 707
Restriction: STATS 369
2288

STATS 765

: Statistical Learning for Data Science
2020 Semester One (1203)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201 or 207 or 208 and 15 points from STATS 210 or 225, or STATS 707 Corequisite: May be taken with STATS 707
Restriction: STATS 369
2289

STATS 766

: Multivariate Analysis
2023 Semester Two (1235)
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).
Subject: Statistics
Prerequisite: STATS 310 or 732
2290

STATS 766

: Multivariate Analysis
2022 Semester Two (1225)
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).
Subject: Statistics
Prerequisite: STATS 302 or 767
2291

STATS 767

: Foundations of Applied Multivariate Analysis
2023 Semester One (1233)
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.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 707
Restriction: STATS 302
2292

STATS 767

: Foundations of Applied Multivariate Analysis
2022 Semester One (1223)
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.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 707
Restriction: STATS 302
2293

STATS 767

: Foundations of Applied Multivariate Analysis
2021 Semester One (1213)
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.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 707
Restriction: STATS 302
2294

STATS 767

: Foundations of Applied Multivariate Analysis
2020 Semester One (1203)
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.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208
Restriction: STATS 302
2295

STATS 768

: Longitudinal Data Analysis
2023 Semester Two (1235)
Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 210, 707
2296

STATS 768

: Longitudinal Data Analysis
2021 Semester Two (1215)
Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 210, 707
2297

STATS 768

: Longitudinal Data Analysis
2020 Semester Two (1205)
Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.
Subject: Statistics
No pre-requisites or restrictions
2298

STATS 769

: Advanced Data Science Practice
2023 Semester Two (1235)
Databases, SQL, scripting, distributed computation, other data technologies.
Subject: Statistics
Prerequisite: 15 points from STATS 220, 369, 380 and 15 points from BIOSCI 209, STATS 201, 207, 208, 707
2299

STATS 769

: Advanced Data Science Practice
2022 Semester Two (1225)
Databases, SQL, scripting, distributed computation, other data technologies.
Subject: Statistics
Prerequisite: 15 points from STATS 220, 369, 380 and 15 points from BIOSCI 209, STATS 201, 207, 208, 707
2300

STATS 769

: Advanced Data Science Practice
2021 Semester Two (1215)
Databases, SQL, scripting, distributed computation, other data technologies.
Subject: Statistics
Prerequisite: 15 points from STATS 220, 369, 380 and 15 points from BIOSCI 209, STATS 201, 207, 208, 707