Course overview
The aim of this course is to introduce students to the foundational statistical methods and data analysis techniques essential for understanding and applying key concepts in data science and artificial intelligence. Through this course, students will learn to apply statistical models, visualise data, and interpret results within the context of introductory real-world applications in AI and data science.
Course learning outcomes
- Explain key statistical concepts, including data types, uncertainty, and the role of scientific inquiry in data science and AI
- Apply exploratory data analysis techniques to summarise and visualise datasets, identifying key patterns and relationships
- Perform basic probability calculations and statistical inferences, including constructing and interpreting confidence intervals and hypothesis tests
- Implement data transformation techniques to improve data quality, address non-normality, and enhance model performance
- Build, interpret, and evaluate linear regression models for prediction and decision-making, addressing common issues such as multicollinearity and heteroscedasticity
Degree list
The following degrees include this course