MT4112 Computational Numerical Analysis

2024 to 2025 Semester 1

Curricular information may be subject to change

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 10

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Not automatically available to General Degree students

Planned timetable

Lectures: 10am Monday (odd weeks), Wednesday, Friday

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr D Lucas

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

The module will introduce students to some topics in numerical analysis and demonstrate their application using various problems in applied mathematics through the Python programming language.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS MT3510

Anti-requisites

IN TAKING THIS MODULE YOU MUST NOT BE ON A SINGLE OR JOINT HONOURS PROGRAMME IN THE SCHOOL OF COMPUTER SCIENCE

Co-requisites

IF NOT ALREADY PASSED, YOU MUST TAKE MT3504

Assessment pattern

1 hour 15 minutes Written Examination = 50%, Coursework = 50%

Re-assessment

Oral examination = 100%

Learning and teaching methods and delivery

Weekly contact

2.5 lectures (x 10 weeks), 1 tutorial/lab (x 10 weeks)

Scheduled learning hours

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

114

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

Analyse and implement numerical methods for root-finding and solving ordinary differential equations
Identify and apply appropriate mathematical techniques to approximate functions.
Analyse and implement iterative methods to solve systems of linear equations.
Demonstrate knowledge of the concepts of rate of convergence, error bounds and numerical stability.
Write well-structured Python code to investigate and analyse problems in numerical analysis.