FIT2132 · Reasoning with data

Overview

This unit provides a foundational introduction to data science, covering core concepts, methods, and tools used to collect, analyse, model, and interpret data. You will develop practical skills in using programming languages such as Python and/or R for data science tasks, including data handling, visualization, and descriptive statistical analysis. The unit introduces principles of sampling, probability, expectation, and basic probability models, providing a basis for reasoning about uncertainty and variation in data. You will explore predictive modelling methods, including linear models, and examine how models can be fitted and evaluated using approaches such as maximum likelihood and minimum loss. The unit also introduces key ideas in statistical inference, including confidence intervals and hypothesis testing. In addition, you will consider ethical and privacy issues that arise when working with data. By the end of the unit, you will be able to apply foundational data science techniques, interpret statistical results, and communicate data-driven insights clearly and responsibly.

Offerings

Assessment

The Handbook publishes no assessment items for this unit yet.

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

Requisites

Learning outcomes

1. Apply privacy-aware and ethical working practices when handling data, recognising obligations around consent, sampling and responsible use across the data lifecycle;
2. Describe how data-driven decisions shape equity, sustainability and Indigenous perspectives, identifying opportunities to design data work that serves diverse communities;
3. Communicate data-driven findings and statistical interpretations clearly using appropriate written, visual and quantitative forms;
4. Select and apply Python and/or R for data handling, descriptive analysis and visualisation, incorporating responsible use of AI-enabled tools;
5. Apply foundational data science techniques to small datasets, including sampling, probability reasoning and basic statistical inference;
6. Produce data visualisations that support interpretation of analytical results.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled teaching activities.

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