Teaching and assessment for Biological and Biomedical Sciences (joint degree with National University of Singapore) BSc (Hons)

Faculty of Life Sciences

Date of entry

September 2026

Teaching

We use a variety of teaching methods, including:

  • lectures
  • lab practicals
  • workshops
  • tutorials
  • computer-based learning
  • media / video exercises
  • field excursions where appropriate

Assessment

We use a combination of in-course and end-of-course assessments. These will take the form of:

  • practical reports (based on lab work)
  • computer-based exercises
  • data processing exercises
  • essays
  • exams

Progression

Levels 3 and 4

You'll spend the first semester of Level 3 at the University of Dundee. Up to 15 students per year are then able to transfer to National University of Singapore for the remainder of Level 3.

To be eligible to transfer to National University of Singapore:

  • You need to achieve a minimum overall module grade average of A5 per semester during Levels 1 and 2.
  • If at the end of Level 2 there are more than 15 students enrolled on the programme, be within the top 15 based on your Level 2 results.

To progress to Level 4 of the course you must achieve a NUS GPA of 4.00 or above during semester 2 of Level 3.

If you don’t meet this progression criteria you will transfer to study another Dundee-based Life Sciences course.

Modules

Core Modules

These modules are an essential part of your course.

Module code: BS11005 Credits: 20 Semester: Semester 1

​​A strong foundation in the physical sciences is essential for biological and biomedical courses. 

​You will build your skills in biophysics, inorganic and physical chemistry, and numeracy. You will focus on how these apply to the life sciences. 

​You will develop these skills through practical classes, lectures and workshops.

Core Modules

These modules are an essential part of your course.

Module code: BS21001 Credits: 10 Semester: Semester 1

​​Statistics and experimental design are fundamental to every branch of the life sciences. 

​You will learn how to analyse biological and biomedical datasets. You will also learn to design robust experiments, and draw meaningful conclusions from data. 

​You will also cover topics like: 

  • ​Hypothesis testing 
  • ​ANOVA 
  • ​Regression 
  • ​Data visualisation​ 

Optional Modules

You need to choose one or more of these modules as part of your course.

Module code: BS21008 Credits: 20 Semester: Semester 1

​Organic chemistry is central to drug discovery and the life sciences.

This module builds on your existing chemistry knowledge to explore the principles of organic and heterocyclic chemistry in depth.

Through lectures, problem-solving workshops and two laboratory experiences in heterocyclic synthesis, you will develop the theoretical understanding and practical skills needed to engage with drug discovery and biological chemistry at a more advanced level.​

Optional Modules

You need to choose one or more of these modules as part of your course.

Module code: BS32036 Credits: 20 Semester: Semester 2

​This module is an in-depth study of molecular mechanisms that drive bacterial behaviour.

You will examine how bacteria grow and move, communicate, form biofilms and cause disease, while also developing skills in scientific writing, data analysis and oral presentation.​

Core Modules

These modules are an essential part of your course.

Module code: BS41010 Credits: 60 Semester: Semester 1

This capstone module is the major research experience of your Honours degree.

You will conduct an independent research project within the University's research environment under the guidance and supervision of a research academic and develop the skills necessary to formulate and conduct a research project, and to communicate it via a dissertation of up to 6,000 words, and at an Honours symposium alongside the wider research community.

Optional Modules

You need to choose one or more of these modules as part of your course.

Module code: BS42029 Credits: 20 Semester: Semester 2

​This module introduces you to machine learning in a biological context.

You will explore key concepts in machine learning for biological data analysis, study current approaches through seminars and guided self-study, and develop your ability to critically evaluate the use of AI tools in biology alongside their ethical implications.​

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