2015/16 | Engineering, Mathematics and Physical Sciences Intranet

MODULE TITLE	Mathematics and Computing: Integrative Tools for Natural Sciences	CREDIT VALUE	30
MODULE CODE	NSC1002	MODULE CONVENER	Ms Aileen MacGregor (Coordinator)

DURATION: TERM	1	2	3
DURATION: WEEKS	11	11

Number of Students Taking Module (anticipated)	40

DESCRIPTION - summary of the module content

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 should be taken in parallel with NSC1001 Frontiers in Science 1 and NSC1003 Foundations in Natural Science.

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) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

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

Discipline Specific Skills and Knowledge

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

Personal and Key Transferable / Employment Skills and Knowledge

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 - summary of the structure and academic content of the module

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

Part VII. Visualisation and algorithms in Matlab

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	141.00	Guided Independent Study	159.00	Placement / Study Abroad	0.00

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of study time	Description
Scheduled Learning and Teaching	77	Lectures (four 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	12	Mathematics tutorials (one hour every other week)
Scheduled Learning and Teaching	8	Surgeries (one for each assessment)
Guided independent study	159	Additional research, reading and preparation for module assessments

ASSESSMENT

FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
8 x problem sets – mix of mathematics and computer science questions	1 hour per set	1,2,6-9,11	Individual Form

SUMMATIVE ASSESSMENT (% of credit)

Coursework	60	Written Exams	40	Practical Exams	0

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	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 exercise 1	10	5 hours	2-5, 7-10, 12	Individual form
Programming exercise 2	10	5 hours	2-5, 7-10, 12	Individual form
Mid-term test	10	1 hour	1, 3, 5, 7-9, 11	Marked, then discussed in tutorials
Final examination	40	2 hours	1, 3, 5, 7-9, 11	Mark via MyExeter, 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	Time Scale 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, 4, 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.

RESOURCES

INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

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.

ELE: http://vle.exeter.ac.uk/course/view.php?id=3803

Web based and Electronic Resources:

Other Resources:

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE	30	ECTS VALUE	15

PRE-REQUISITE MODULES	None
CO-REQUISITE MODULES	None

NQF LEVEL (FHEQ)	4	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Friday 09 January 2015	LAST REVISION DATE	Thursday 14 May 2015

KEY WORDS SEARCH	Mathematics, computing, natural sciences, differential equations, Matlab, Python

Natural Sciences

NSC1002 - Mathematics and Computing: Integrative Tools for Natural Sciences (2015)