## MATH2920 Computational Mathematics

### 10 creditsClass Size: 318

Module manager: Dr Richard Elwes; Dr Rob Sturman
Email: R.H.Elwes@leeds.ac.uk; R.Sturman@leeds.ac.uk

Taught: Semester 1 View Timetable

Year running 2019/20

### Pre-requisite qualifications

(MATH1010 or MATH1050) and (MATH1025 or MATH1055)

### This module is mutually exclusive with

 MATH1920 Computational Mathematics

Module replaces

MATH1920

This module is not approved as a discovery module

### Module summary

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.

### Objectives

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.

Learning outcomes
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.

### Syllabus

- 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.

### Teaching methods

 Delivery type Number Length hours Student hours Class tests, exams and assessment 1 1.00 1.00 Lecture 10 1.00 10.00 Practical 10 2.00 20.00 Private study hours 69.00 Total Contact hours 31.00 Total hours (100hr per 10 credits) 100.00

### Private study

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.

### Opportunities for Formative Feedback

Regular example practical sheets handed in and marked.

### Methods of assessment

Coursework
 Assessment type Notes % of formal assessment Computer Exercise . 20.00 Computer Exercise . 20.00 Computer Exercise . 40.00 In-course Assessment 30 minute unseen exam during week 11 lecture 20.00 Total percentage (Assessment Coursework) 100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated