Module manager: Dr Richard Elwes; Dr Rob Sturman
Email: R.H.Elwes@leeds.ac.uk; R.Sturman@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2020/21
(MATH1005 or MATH1010 or MATH1050) and (MATH1025 or MATH1055)
| MATH1920 | Computational Mathematics |
MATH1920
This module is not approved as a discovery module
This module introduces students to computational techniques, algorithms and numerical solutions. Students will learn basic programming using the language Python and apply computational techniques to the solution of mathematical problems.
On completion of this module, students should:
- understand the use and limitations of computers in a mathematical setting;
- be familiar with the rudiments of programming and syntax using Python;
- understand and construct mathematical and computational algorithmics;
- be able to solve mathematical problems using computational methods;
- be able to interpret correctly the result of a computational procedure.
On completion of the module students should have provided evidence of being able to:
- demonstrate a broad understanding of the comcepts, information, practical competencies and techniques of computational mathematics;
- demonstrate a reasonable level of skill in calculation and manipulation within this basic body of knowledge;
- apply core concepts and principles in well-defined contexts;
- appreciate the coherence, logical structure and broad applicability of mathematics;
- demonstrate an awareness of skills in comprehending problems, formulating them mathematically and obtaining solutions by appropriate methods;
- use a range of techniques to initiate and undertake problem solving.
- introduction to the principles of computational mathematics.
- fundamentals of syntax, structure and file mamagement in Python.
- loops, functions, control flow statements.
- data types (how a computer understands intergers, rationals, irrationals).
- coding of simple algorithms, such as Euclid's algorithm, the Sieve of Eratosthenes, continued fraction algorithm.
- computational solution to more dvanced problems, such as sorting algorithms and random number generation.
- plotting and visualisation.
- limitations of computational methods.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Class tests, exams and assessment | 1 | 1 | 1 |
| Lecture | 5 | 1 | 5 |
| Practical | 10 | 2 | 20 |
| Private study hours | 74 | ||
| Total Contact hours | 26 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Students should work on programming and problem-solving skills either in IT cluster rooms, or using identical freely available software downloaded onto their own machines. Instructions for doing so will be given.
Regular example practical sheets handed in and marked.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Computer Exercise | . | 20 |
| Computer Exercise | . | 20 |
| Computer Exercise | . | 40 |
| In-course Assessment | A short individual online test in Week 11. | 20 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
There is no reading list for this module
Last updated: 8/10/2020
Errors, omissions, failed links etc should be notified to the Catalogue Team