The course is conducted in English。

In the era of big data, proficiency in several fundamental computational skills is required to conduct high-quality analysis and reproducible research in multiple disciplines. Within the field of biology and agriculture, large-scale datasets are easily accessible due to the advancement in technology. The amount of data will continue to increase at a dramatic rate over the following decades. Students will be required to have the ability to process and analyse large amounts of data efficiently in the “-omic” and even "post-omic" era. This course will introduce a few fundamental and transferable computational skills for students who work with biological data. These skills include but are not limited to command line interface, working with computer servers, software version control (Git and GitHub) for collaboration, software testing for reproducible analysis, working with the relational database (MySQL), data cleaning and manipulation. Although many of these skill sets are transferable to fields outside of biology, this course will focus on their application to biological data.

At the completion of this course, students will be able to:

• Work with the command line interface, navigate the filesystem, perform file manipulation, execute commands to solve simple tasks and connect to remote servers.
• Utilise software version control systems (Git and GitHub) for reproducible work, collaborate with others, and perform software testing and validation.
• Understand the principles and concepts of the relational database (MySQL), and be able to perform daily tasks, such as storing and retrieving data.
• Work collaboratively in groups to solve computational challenges.

• This course will be conducted in English. All lectures, course materials, and assignments will be presented and conducted in English.
• Cheating and plagiarism in assignments, exams or any other assessments are serious academic misconduct. All instances will be handled according to the university policy.
• Students are required to have basic statistics knowledge. Any Statistics101 course that covers descriptive statistics, simple linear regression and ANOVA will be sufficient.
• Students are required to have exposure to at least one programming language. It is recommended that students are familiar with the following basic concepts: declare variables, basic arithmetic operation, basic data type (R: vector, list, data.frame. Python: list, dictionary), declare functions.

2-6 hours