Intermediate Statistics

This course builds on the introductory course by introducing more advanced statistical methods used in research. Topics include hypothesis testing, regression, one way and two-way analysis of variance (ANOVA), non-parametric methods and introduction to multivariate statistics. The course focuses on applying statistical thinking to real research problems. The course emphasises selecting appropriate statistical methods, interpreting results, and understanding statistical assumptions, while providing an introduction to the use of statistical software and data science tools to support data analysis and research.

Who Should Attend

• Honors and Master’s students
• PhD students
• Early career researchers
• Academic and professional staff involved in research
• Anyone seeking a solid foundation in statistical analysis before learning statistical software or data science techniques

Learning Outcomes

Upon completion of this course, participants will be able to:

• Understand fundamental statistical concepts and terminology.
• Summarise and visualise data appropriately.
• Select suitable basic statistical methods for common research questions.
• Interpret statistical outputs and communicate findings correctly.
• Develop the statistical knowledge required for further training run by Data ScienceCRP in R, SPSS, Python, and AI-based analytical tools.

Overall Training Pathway

These two courses will establish the statistical foundation required before participants undertake software-focused or advanced analytical trainings provided by Data Science CRP. They are intended to support future professional development in:

• Data Science
• R Programming
• SPSS
• Python
• Artificial Intelligence (AI)
• Machine Learning
• Advanced data analytics