Course overview
Data is all around us, but often in forms that are messy, incomplete, or difficult to interpret. This course develops foundational skills in data wrangling and analysis, focusing on transforming real-world data into tidy, structured formats suitable for exploration and predictive modelling. Students will learn to work with diverse data types and formats, apply data transformation techniques, and build and evaluate statistical and machine learning models. The course builds both technical proficiency and critical thinking to support data-informed decision making.
- Introduction to Taming Messy Data in R
- Transformation and Continuous Predictive Modelling
- Classification and Discrete Predictive Modelling
Course learning outcomes
- Describe the principles of data taming and approaches used to tidy data
- Compare and critically evaluate the performance of predictive models using appropriate validation techniques and model metrics.
- Select from data analysis and visualisation techniques to create a predictive model and make predictions from it
- Execute techniques to transform, reduce and summarise data in order to visualise it
- Communicate professionally on the application of predictive models through the use of real-world case studies