Course overview
A practical introduction to finding relationships in data using statistical methods. The course introduces the principles of taming and tidying data, types of data, exploratory data analysis and visualisation, data transformation, as well as model fitting and interpretation. 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 in order to create a linear model.
- Interpret data within a linear model to make predictions from the model.
- 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.
Availability
Class details
Online
Class number 44012
Section 01OL
Size 500
Available 499
Notes:
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