Course Information Undergraduate prospectus

Analytical Methods for Computing

Course summary

Course code: MATH1111
Level: 4
Credits: 15
School: Architecture, Computing and Hums
Department: Computing and Information Sys.
Course Coordinator(s): Yvonne Fryer


Pre and co requisites

MATH1110 Logical Foundations.


This course teaches analytical skills and techniques needed to underpin a rigorous approach to computing. The emphasis will be on developing precise thinking, and analytical, modelling and algorithmic skills in the context of Computing. It extends the knowledge gained in Logical Foundations and give an introduction to discrete and continuous techniques.

Learning outcomes

At the end of the course the student should be able to:
A. Use functions in the context of computing.
B. Design and use simple algorithms.
C. Use vectors and matrices in a variety of applications.
D. Understand small network graphs and apply them to a variety of problems.
E. Understand some basic concepts of differential and integral calculus and apply them in the context of computing.
F. Manipulate simple complex numbers.
G. Use simple probability.

Indicative content

Function definitions and types, function composition and inversion.
Logarithmic, exponential and trigonometric functions.

Introduction to Algorithms:
Structure and interpretation of algorithms. Issues of computability, efficiency, complexity.

Matrices and Vectors:
Vectors, matrices and arrays. Applications of matrices e.g. to solve equations and to effect 2D graphical transformations.

Graphs and Networks:
Definitions, Spanning trees, Algorithms, Application to paths and searches.

Definitions of integration and differentiation.
Manipulation of simple functions.
Application to area under a curve and gradient.

Complex numbers:
Definition of i.
Manipulation of complex numbers.
Representation on the Argand diagram.

Definitions and rules for combining probabilities.
Probability distributions including the Normal Distribution.

Teaching and learning activity

Concepts will be introduced in lectures and problem solving will be done through tutorials.
Student time will be: Lecture 67% and Tutorial 33%.

Learning Time (1 credit = 10 hours).

Scheduled contact hours:

Note: include in scheduled time: project supervision, demonstrations, practical classes and workshops, supervised time in studio or workshop, scheduled lab work , fieldwork, external visits, work-based learning where integrated into a structured academic programme.
Lectures 24;
seminars 0;
supervised practical sessions 0;
Tutorials 12;
formative assessment 2;
other scheduled time 0.
Guided independent study:

Note: include in guided independent study preparation for scheduled sessions, follow up work, wider reading or practice, revision.
Independent coursework 25;
Independent laboratory work 25;
other non-scheduled time 87;
Placements (including work placement and year abroad) 0;
Total hours (Should be equal to credit x 10) 150.


Methods of Assessment - 5 part assignment; grading mode - 20 marks per part. Maximum 100 = 100%; weighting% - 50%; pass mark - 40%; word length - n/a; outline details - students submit work at 5 intervals through the course that is marked and returned providing feedback. Each piece receives a mark out of 20; last item of assessment - no; are students required to pass all components in order to pass the course - no.

Methods of Assessment - exam; grading mode - marked out of 100 = 100%; weighting% - 50%; pass mark - 40%; word length - n/a; outline details - The exam is a 2hour exam at the end of the course, that helps students' complete work in a timely manner; last item of assessment - yes.