Course overview
This course is a practical introduction to the practice of wrangling, finding relationships in, and making predictions from, messy datasets using statistical methods. The course introduces the principles of tidy data, types of data and data formats, exploratory data analysis, data transformation, as well as model fitting and prediction using statistical machine learning tools. A focus will be to introduce R programming for data science applications, particularly through real-world case studies.
Course learning outcomes
- Describe the principles of data taming and approaches used to tidy data.
- Identify the different types of data and data variables.
- Select from data analysis and visualisation techniques to create a linear model and make predictions from it.
- Execute techniques to transform, reduce and summarise data in order to visualise it.
- Articulate the ideas that data scientists consider when looking at data.
- Communicate professionally on the application of linear models through the use of real-world case studies.
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The Student Contribution amount displayed below is for students commencing a new program from 2021 onwards. If you are continuing in a program you commenced prior to 1 January 2021, or are commencing an Honours degree relating to an undergraduate degree you commenced prior to 1 January 2021, you may be charged a different Student Contribution amount from the amount displayed below. Please check the Student Contribution bands for continuing students here. If you are an international student, or a domestic student studying in a full fee paying place, and are continuing study that you commenced in 2025 or earlier, your fees will be available here before enrolments open for 2026.