Undergraduate Course: Mathematical, Data & Research Skills for Life Sciences (FNDN07018)
Course Outline
| School | Centre for Open Learning |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 7 (Year 1 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This foundation course will prepare you for undergraduate studies in life and medical sciences at the University of Edinburgh.
It consolidates and builds on your previous mathematics education, to prepare you for undergraduate study in a wide variety of Biological, Biomedical, Environmental Sciences, and related degrees.
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| Course description |
The landscape of life and medical sciences has undergone profound transformation with the progress of technology and the ability to generate and store large amounts of data. Future biological, biomedical and environmental scientists need good mathematical & numeracy skills to carry out laboratory work, such as unit conversion, preparation of solutions and serial dilutions, and need strong data analysis skills to successfully navigate and make use of rich quantitative data.
Through lectures, tutorials, and computer-based workshops, this course will develop a strong foundation in numeracy, mathematics, statistics and scientific computing, tailored to the needs of all students progressing to undergraduate degrees in biological, biomedical, environmental sciences and related disciplines.
The landscape of life and medical sciences has undergone profound transformation with the progress of technology and the ability to generate and store large amounts of data. Future biological, biomedical and environmental scientists need good mathematical & numeracy skills to carry out laboratory work, such as unit conversion, preparation of solutions and serial dilutions, and need strong data analysis skills to successfully navigate and make use of rich quantitative data.
Through lectures, tutorials, and computer-based workshops, this course will develop a strong foundation in numeracy, mathematics, statistics and scientific computing, tailored to the needs of all students progressing to undergraduate degrees in biological, biomedical, environmental sciences and related disciplines.
This course is designed to bridge the gap between existing knowledge and undergraduate-level mathematics, statistics and data science that biological and biomedical science students will encounter in their undergraduate programme.
The course balances theoretical understanding with practical application, emphasising problem-solving and critical thinking skills, essential for scientific research. Through a combination of lectures, tutorials, and computer lab workshops you will consolidate your mathematical knowledge, develop your statistical and data science skills, and develop the ability to apply mathematical and statistical concepts to real-world scientific problems.
Practical skills developed include using specialised software, analytical tools, statistical packages and programming languages for data analysis and problem-solving in life and medical sciences.
You will engage in a varied learning experience combining lectures, tutorials and computer lab workshops. Lectures introduce key concepts and theories, while tutorials provide opportunities for problem-solving and discussion in small groups.
The United Nations' Sustainable Development Goals are embedded throughout, providing opportunities to develop critical insights and discuss strategies which could lead to positive global transformations.
Computer lab workshops offer hands-on experience with mathematical software and programming tools used in scientific research.
An online platform provides access to course materials and additional resources. Regular formative assessments, including online quizzes and problem sets, give you instant automated feedback, allowing you to track your progress and reflect on your learning.
The course culminates in a final written test and a data analysis project. The project involves analysing a real-world dataset relevant to the life and medical sciences, requiring you to apply the mathematical, computational and research skills that you have developed.
If you require any additional support, you will find this available through our MathsHub, which runs throughout the duration of the course. MathsHub is a regular, in-person session during which you can participate in supported study and/or book an appointment to speak with a mathematics teacher about a specific issue. This bespoke, individualised support ensures that, if needed, you will receive comprehensive assistance in all aspects of the course.
To support your academic success, you will have access to specialised assistance from the Scientific Academic Languages and Literacies team, ensuring you are well-equipped to excel in all learning.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2026/27, Not available to visiting students (SS1)
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Quota: 80 |
| Course Start |
Flexible |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 16,
Seminar/Tutorial Hours 32,
Formative Assessment Hours 24,
Summative Assessment Hours 2,
Other Study Hours 32,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
90 )
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| Additional Information (Learning and Teaching) |
Other Study Hours include MathsHub (optional)
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Continuous Assessment: 30%
[LOs 1, 2, 3, 4]
Data Analysis Project: 30%
[LOs 1, 2, 3, 4]
Written Assessment: 40%
[LOs 1, 2, 3, 4]
To pass the course, students must achieve a minimum of 40% overall, meeting all Learning Outcomes.
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| Feedback |
Throughout the course, teaching staff will support you to identify the gaps in your skills and learning, and your strengths. You will be encouraged to engage with feedback through personal reflection and discussion with peers.
You will receive ongoing feedback through:
Weekly tutorial sessions with immediate feedback on problem-solving approaches.
Regular online quizzes with automated feedback and opportunities to try similar questions multiple times.
Detailed written feedback on problem sets and the data analysis project.
One-on-one feedback with teachers during tutorials, workshops and MathsHub sessions.
Peer feedback during group work.
Comprehensive feedback on the final written assessment, highlighting areas of strength and areas for further development.
This comprehensive feedback system ensures that you have multiple opportunities to gauge your progress and continuously improve your performance throughout the course.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Carry out basic mathematical calculations necessary for laboratory work.
- Use statistics to analyse biomedical and biological datasets.
- Interpret and critically evaluate data.
- Apply data representation strategies.
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Reading List
There is no single core textbook. All essential learning materials will be provided through the University of Edinburgh's online platform.
The following books are recommended useful resources, and they can be found in the University library:
Harris, M., Taylor, G. and Taylor, J. (2013). Catch up Maths & stats for the Life and Medical Sciences. Banbury: Scion Publishing.
Stephenson, F. H. (2010) Calculations for molecular biology and biotechnology : a guide to mathematics in the laboratory. 2nd edition. London: Academic.
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Additional Information
| Graduate Attributes and Skills |
Mindset:
You will be encouraged to develop a reflective approach on your knowledge and skills and critically identify ways in which to improve and grow.
You are encouraged to adopt an inquiring mindset and develop an appreciation of why mathematics and data analysis are important in scientific and medical research.
You will build confidence in applying mathematical & statistical concepts to real-world scientific problems.
You will be introduced to non-Eurocentric data and research, to support your understanding of the role of mathematics and data science in global scientific and medical research.
Skills:
You are encouraged to use creative problem-solving skills to address complex scientific questions.
You will build personal and intellectual autonomy in approaching mathematical and computational challenges.
You will develop teamwork skills through collaborative problem-solving and group projects.
You will develop strong communication skills in presenting mathematical and scientific ideas clearly and concisely.
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| Keywords | Not entered |
Contacts
| Course organiser | Mrs Jayne Quoiani
Tel:
Email: Jayne.Quoiani@ed.ac.uk |
Course secretary | Mr James Cooper
Tel: (0131 6)50 4400
Email: jcooper6@ed.ac.uk |
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