BIN250 Quantitative skills in BioScience

In the fields of plant, animal, biology, and aquaculture sciences, quantitative skills are essential for conducting research and advancing professional skills. However, many students in these disciplines may enter their programs with limited quantitative experience. This course aims to overcome these challenges by enhancing both quantitative competence and confidence, equipping students with crucial problem-solving abilities that are fundamental to their professional growth in Biosciences.

Building on foundational concepts, this advanced course offers a comprehensive approach to data collection in excel, management, R programming for data preparation and manipulation, statistical designs from biosciences and their analysis. It equips bachelor's and master's students with problem-solving abilities by incorporating digital tools and AI-based methods to assist with R programming. This course will also introduce students to the basics of cloud computing and data storage solutions for handling large-scale biological datasets. Emphasizing applied practical exercises and real-world case studies from our on-going research projects, students will develop the ability to effectively apply statistical methods and skills that are fundamental to impactful careers in biological sciences.

Knowledge:Upon completion of this course, students will be able to:

1. Understand the essential quantitative concepts and methods used in biology, plant, animal or aquaculture sciences.
2. Demonstrate knowledge of data management principles, including data collection, preparation, and analysis.
3. Understand the basics of cloud computing and its applications in storing and analyzing biological data.
4. Understand relevant statistical designs in BioSciences including the use of R and Python for analysis
5. Recognize and analyse the applications of statistical methods in real-world case studies, including from on-going research projects and scientific research.

Skills:Upon successful completion, students will be able to:

1. Apply quantitative methods confidently to scientific questions within their field of study.
2. Manage, clean, and analyse complex datasets using R with AI-assisted tools.
3. Produce graphical representations for understanding data and presenting of results to different audiences
4. Analyse data from experimental designs most relevant to plant, animal, biology, and aquaculture sciences with a focus on sustainable agriculture through real-world examples.

General Competence:Upon completing this course, students will:

1. Develop an interdisciplinary perspective, integrating quantitative analysis within biology, plant, animal, or aquaculture sciences to address complex scientific and real-world challenges.
2. Strengthen problem-solving abilities, enabling them to independently design, conduct, and interpret quantitative research in diverse biological contexts.
3. Enhance their confidence in using digital and AI-based tools, fostering adaptability to emerging technologies in data analysis.
4. Cultivate critical thinking and analytical skills essential for evaluating scientific data and drawing evidence-based conclusions.
5. Build effective communication skills, enabling them to present quantitative findings clearly and accurately to both technical and non-technical audiences.