Undergraduate Course: Mathematical, Data & Research Skills for Physical Sciences (FNDN07017)
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 physical sciences at the University of Edinburgh. It covers essential mathematical concepts, data analysis techniques, and research skills. The course consolidates and builds on previous mathematics education, tailored to the needs of chemistry, physics and related disciplines. |
| Course description |
The landscape of physical sciences continues to evolve, demanding increasingly sophisticated mathematical skills from future chemists and physical scientists. This course is designed to bridge the gap between existing knowledge and university-level mathematics, providing a solid foundation for entering chemistry and related physical science programmes.
The course will explore how mathematical concepts apply directly to real-world scientific problems, enhancing your analytical thinking and problem-solving skills. It course covers a range of topics essential for physical sciences, including:
Advanced mathematical techniques and their applications in scientific contexts;
Fundamental principles of calculus and how they relate to physical sciences;
Basic statistical methods for analysing scientific data Introduction to programming;
Essential research skills for aspiring scientists.
Practical skills developed include using mathematical and statistical software, basic programming, and applying these tools to solve problems in chemistry and related fields.
This course aims to prepare you for the quantitative aspects of your undergraduate degree and instil a deep appreciation for the role of mathematics in physical sciences.
You will engage in a varied learning experience combining lectures, tutorials, and computer lab workshops. If you require any additional support, you will find this available through office hours.
Lectures introduce key concepts and theories, while tutorials provide opportunities for problem-solving and discussion in small groups. 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, practice problems, and additional resources. Regular formative assessments, including online quizzes (with automated feedback) and problem sets allow you to track your progress.
The course culminates in a final assessment and a data analysis project. The project involves analysing a real-world dataset relevant to chemistry, which requires you to apply your mathematic and research skills.
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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,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
148 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
50% written assessment, 30% practical, 20% continuous assessment.
Components correspond to:
An in-person timed written assessment, in the style of a centrally organised mathematics exam.
A data analysis project, which involves practical use of a programming language to analyse a dataset.
Regular online quizzes that review and consolidate the learnings on each week's topic.
To pass the course, students must achieve a minimum of 40% overall, meeting all Learning Outcomes.
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| Feedback |
You will receive ongoing feedback through:
Weekly tutorial sessions with immediate feedback on problem-solving approaches.
Regular online quizzes with automated feedback on areas needing improvement.
Detailed written feedback on the data analysis project.
Comprehensive feedback on the final assessment, highlighting areas of strength and areas for further development.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Apply algebraic and calculus techniques to solve problems relevant to physical sciences.
- Analyse and interpret scientific data using appropriate statistical methods.
- Demonstrate basic skills in data visualisation techniques.
- Integrate mathematical, computational, and research skills to analyse data.
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Reading List
In addition to the course fee, students are expected to provide the following list of materials and equipment:
University approved scientific calculator
Laptop computer
Notebook and basic stationary.
There is no single core textbook. All essential learning materials will be provided through the University of Edinburgh's online platform.
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Additional Information
| Graduate Attributes and Skills |
Mindset:
You are encouraged to develop a reflective approach to your mathematical knowledge and skills, critically identifying areas for improvement and growth.
You are encouraged to cultivate an inquiring mindset and develop an appreciation for mathematics in physical sciences.
You will build confidence in applying mathematical concepts to real-world scientific problems.
You will be introduced to diverse mathematical perspectives to support your development of an international outlook on the role of mathematics in global scientific research.
Skills:
You are encouraged to use creative problem-solving skills to address complex scientific questions.
You are encouraged to build personal and intellectual autonomy in approaching mathematical and computational challenges in physical sciences.
You will develop strong communication skills in presenting mathematical and scientific ideas clearly and concisely.
You will gain practical experience in using mathematical software and basic programming for scientific problem-solving.
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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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