Course overview
This course aims to equip students with the knowledge and skills to fit predictive models to real-life datasets. Building up the linear models and R coding learned in Statistical Practice, students will learn the general framework of predictive modelling both regression and classification, and how to implement the models in R, interpret the models and communicate to a general audience.
- Workflows
- Models
- Models In The Real World
Course learning outcomes
- Explain important factors and considerations for each key step of the data science workflow
- Discriminate between predictive models with reference to the research question associated with a given dataset
- Demonstrate an understanding the statistical underpinning of the chosen method
- Safely implement data science methods
- Interpret the results of data science methods
- Communicate the output from the predictive models to a general audience
Degree list
The following degrees include this course