Mathematics and Computing: Integrative Tools for Natural Sciences
| Module title | Mathematics and Computing: Integrative Tools for Natural Sciences |
|---|---|
| Module code | NSC1002 |
| Academic year | 2020/1 |
| Credits | 30 |
| Module staff | Dr David Horsell (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 | 11 |
| Number students taking module (anticipated) | 60 |
|---|
Module description
Mathematical and computational methods play an increasingly important role in understanding the complex observational data that are used to understand problems across the natural sciences, for example developmental biology, biochemistry, physics and medicine. These systems are often best explored using the power of mathematical and computational tools and the purpose of this module is to introduce some of the fundamental techniques and tools that are used to study these problems.
This is a compulsory module for students on the BSc/MSci Natural Sciences, and is not open to students on other programmes.
Module aims - intentions of the module
The intentions of the module are to teach fundamental mathematical and computational techniques and to demonstrate their relevance to the natural sciences through specific examples from biology, biochemistry, physics and medicine throughout the module.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Understand and apply a variety of mathematical techniques including linear algebra, calculus and statistics
- 2. Write, compile, test, and debug a computer program
- 3. Explain how a program written in a procedural language is translated into a form that allows it to be executed on a computer
- 4. Document software to accepted standards
- 5. Use a high-level programming language for basic numerical analysis, simulation and data visualisation
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 6. Formulate problems from natural sciences in a mathematically rigorous manner
- 7. Systematically break down a problem into its components
- 8. Understand and choose appropriate programming techniques
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 9. Apply mathematical and computational methods in a multidisciplinary setting
- 10. Work co-operatively and develop time-management strategies to meet deadlines for work
- 11. Analyse a problem and synthesise a solution
- 12. Use technical manuals and books to interpret specifications and technical errors
Syllabus plan
In the first part of the module, lectures will be used to introduce the fundamental aspects of mathematics and computing (one for each discipline per week). In the second part of the module you will have sufficient knowledge to cover integrated material. Throughout the module, workshops will introduce the basic tools and environment for programming and tutorials will be used to support the mathematical concepts that are being taught.
Mathematics:
Part I. Introductory material, summary overview and background
Part II. Linear algebra and complex numbers
Part III. Sequences, Series, Limits and Convergence
Part IV. Calculus and Differential Equations
Part V. Multivariable calculus and vector calculus
Part VI. Probability and Statistics
Computing:
Part I. Programming overview and introduction to Python as a language (statements, comments and simple arithmetic operations).
Part II. Variables, scope and data types
Part III. Control flows, conditionals, loops and iterations
Part IV. Functions, debugging and testing
Part V. Input and Output
Part VI. Scientific computing in Python
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 119 | 181 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching | 55 | Lectures (two hours a week in Term 1, three hours a week in Term 2) |
| Scheduled Learning and Teaching | 44 | Programming workshops (two hours a week in Term 1 and in Term 2) |
| Scheduled Learning and Teaching | 20 | Tutorials, surgeries and feedback sessions |
| Guided independent study | 181 | Additional research, reading and preparation for module assessments |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| 14 x problem sets mix of mathematics and computer science questions | 1 hour per set | 1-2, 6-9, 11 | Oral and solutions on ELE |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 60 | 40 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Problem set 1 | 5 | 5 hours | 1-2, 6-11 | e/written assessment, then discussed in tutorials/workshops |
| Problem set 2 | 5 | 5 hours | 1-2, 6-11 | e/written assessment, then discussed in tutorials/workshops |
| Problem set 3 | 5 | 5 hours | 1-2, 6-11 | e/written assessment, then discussed in tutorials/workshops |
| Problem set 4 | 5 | 5 hours | 1-2, 6-11 | e/written assessment, then discussed in tutorials/workshops |
| Problem set 5 | 5 | 5 hours | 1-2, 6-11 | e/written assessment, then discussed in tutorials/workshops |
| Problem set 6 | 5 | 5 hours | 1-2, 6-11 | e/written assessment, then discussed in tutorials/workshops |
| Programming exercises | 10 | 10 x 15 minutes | 2-5, 7-10, 12 | in-class marking with oral feedback |
| Programming project | 10 | 5 hours | 2-5, 7-10, 12 | Individual form |
| Mid-term test | 10 | 1.5 hours | 1, 3, 5, 7-9, 11 | Marked, then discussed in tutorials |
| Final examination | 40 | 2 hours | 1, 3, 5, 7-9, 11 | Marked, collective feedback via ELE and solutions |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Problem sets, programming exercises | Continuous assessment (problem sheet with a programming exercise) | All | August/September assessment period |
| Final examination and mid-term test | Written examination | 1, 3, 5, 7-9, 11 | August/September assessment period |
Re-assessment notes
If deferred, both forms of re-assessment must take place with the continuous assessment counting for 60% of the overall mark, and the written examination counting for 40% of the marks. If referred in the final exam only, only the written exam must be taken. If referred in all, both re-assessment components must be taken and a mark of 40% achieved in both.
The mark given for a re-assessment taken as a result of referral will be capped at 40%. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment
Indicative learning resources - Basic reading
- Engineering Mathematics. K. A. Stroud. Palgrave Macmillan
- Advanced Engineering Mathematics. K. A. Stroud. Palgrave Macmillan
- Core Maths for the Biosciences. M. B. Reed. Oxford University Press
- Elementary Statistics. Mario F Triola. Pearson.
- A Primer on Scientific Programming with Python (Texts in Computational Science and Engineering). Hans Petter Langtangen. Springer.
Indicative learning resources - Web based and electronic resources
| Credit value | 30 |
|---|---|
| Module ECTS | 15 |
| Module pre-requisites | None |
| Module co-requisites | NSC1003 Foundations in Natural Science, NSC1004 Experimental Science, NSC1005 Frontiers in Science 1 |
| NQF level (module) | 4 |
| Available as distance learning? | No |
| Origin date | 01/06/2012 |
| Last revision date | 21/06/2017 |
