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 principle 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.