Computational Statistics

This course is an introduction to nonparametric statistical methods based on empirical distribution functions, kernel smoothing, bootstrap, and resampling. We will study these methods by looking at their theoretical properties and their performance in practical data analysis

15 points

15 points from 200 level MATH or EMTH, STAT210-299 or DATA203

Bayesian Inference

This course explores the Bayesian approach to statistics by considering the theory, methods for computing Bayesian solutions, and examples of applications.

15 points

30 points from 200 level MATH, EMTH, STAT202-299, DATA203 and PHYS285

Time Series Methods

Analysis of sequentially collected data including data modelling and forecasting techniques.

15 points

15 points from MATH102, EMTH118 or MATH199; and another 30 points from 200 level STAT or ECON213

ECON323, FINC323

Data Mining

Parametric and non-parametric statistical methodologies and algorithms for data mining.

15 points

15 points from MATH102, EMTH118 or MATH199; and another 30 points from 200 level STAT, COSC, DATA, MATH or EMTH

Generalised Linear and Multivariate Models

STAT319 is a course in Generalised Linear Models (GLM), suited to anyone with an interest in analysing data. In this course we introduce the components of GLM and other advanced data analysis techniques. We use the free-ware package R. R is becoming the preferred computer package for many statisticians. In this course we will show you how to use the package, enter, manipulate and analyse data in R.

15 points

30 points from STAT202-299