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Data Science for Biologists (BIOL90041)
Graduate courseworkPoints: 12.5Not available in 2024
Overview
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This subject provides an overview of data science concepts and techniques as they pertain to the field of biology. Students will learn how to apply data science methods to real-world biological data, including techniques for data collection, curation, analysis, and visualization. The subject will provide practical skills in programming languages commonly used for data science, such as R or Python, and best practices for reproducible research, including documentation and data sharing. It will also provide with the students a practical experience of high-performance computing (HPC) and cloud computing. By the end of the subject, students will have developed a robust foundation across this set of skills, empowering them to work confidently with the kind of large-scale dataset that is becoming increasingly common across all fields of biology.
Intended learning outcomes
On completion of this subject, students should be able to:
- apply fundamental principles of data science to biological data sets;
- collect, organise, and curate biological data for analysis;
- assess the appropriateness of experimental designs and analysis techniques to interpret biological data;
- visualise and interpret the results of data analysis; and
- develop practical skills in programming languages commonly used for data science.
Generic skills
At the completion of this subject, students should gain skills in:
- collecting, curating, and interpreting quantitative data;
- developing the ability to exercise critical judgement;
- rigorous and independent thinking; and
- time management and self-management.
Last updated: 31 January 2024