ITO5197 · Statistical data modelling

Overview

This unit explores the statistical modelling foundations that underlie the analytic aspects of Data Science. Motivated by case studies and working through examples, this unit covers the mathematical and statistical basis with an emphasis on using the techniques in practice. It introduces data collection, sampling and quality. It considers analytic tasks such as statistical hypothesis testing and exploratory versus confirmatory analysis. It presents basic probability distributions, random number generation and simulation as well as estimation methods and effects such as maximum likelihood estimators, Monte Carlo estimators, Bayes theorem, bias versus variance and cross validation. Basic information theory and dependence models such as regression and log-linear models are also presented, as well as the role of general modelling such as inference and decision making, and predictive models.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Perform exploratory data analysis with descriptive statistics on given datasets;
2. Construct models for inferential statistical analysis;
3. Produce models for predictive statistical analysis;
4. Perform fundamental random sampling, simulation and hypothesis testing for required scenarios;
5. Implement a model for data analysis through programming and scripting;
6. Interpret results for a variety of models.

Workload

A minimum of 144 hours over the 6 week teaching period should be used to complete assignments, participating in discussions, private study and revision.

Student experience, not official rules. Nothing here changes what the Handbook says.