University of Adelaide
1 semester
Data Skills for Scientists equips undergraduate students with foundational statistical knowledge and practical R programming skills for real-world data analysis. Ideal for science students seeking to develop evidence-based decision-making and scientific communication abilities.
This course is open to undergraduate students with no specific prerequisites. No assumed knowledge is required. Students should have access to a computer with R statistical software installed. Basic computer literacy is recommended to support learning of the R programming language.
The course covers foundational statistical concepts including study design, distinguishing between experimental and observational studies, and identifying sampling biases. Students learn to apply appropriate statistical techniques for data analysis, perform probability calculations, and produce statistical visualizations using R. The curriculum emphasizes data interpretation and communication of results to both technical and general audiences using reproducible reporting tools. Contemporary applications of statistical modelling techniques are integrated throughout, drawing from real-world scientific research and industry scenarios.
The University of Adelaide is a leading Australian research institution with strong expertise in mathematical sciences and data analysis. The course is delivered by experienced instructors from the School of Mathematical Science who combine academic rigor with practical industry applications. The university's commitment to contemporary, accessible education ensures students gain skills directly applicable to modern scientific practice.
Graduates of this course develop skills applicable across numerous scientific and research-focused careers. Potential career paths include data analyst roles in research institutions, pharmaceutical companies, agricultural organizations, and government agencies. The statistical and R programming skills are valuable for roles in biomedical research, environmental science, food science, veterinary science, and laboratory medicine. Many graduates progress to advanced research degrees or specialized data science positions.
