FOUNDATIONS OF COMPUTING II - 2017/8
Module code: COM1033
GRUNING A Dr (Computer Sci)
Number of Credits
FHEQ Level 4
Module cap (Maximum number of students)
Overall student workload
Lecture Hours: 33
Laboratory Hours: 11
|Assessment type||Unit of assessment||Weighting|
|Coursework||COURSEWORK I INDIVIDUAL||40|
|Examination||2HR UNSEEN EXAM||60|
Prerequisites / Co-requisites
The course builds upon COM1026, Foundations of Computing, and introduces the key concepts of differentiation/integration of a function and their applications. It also provides a short introduction to solving linear equations using matrix manipulation and a primer on statistics.
This module aims to deepen the students' understanding of mathematical functions and their applications, and demonstrate how these are relevant to the discipline. Octave will be used practically to illustrate how functions can be differentiated and integrated. The module also aims to show how sets of linear equations can be solved by simple matrix manipulations. Finally, students will gain insights into how statistics can be used to summarise and interpret data.
|Differentiate and integrate some elementary functions, including polynomials, exponential and trigonometric functions;||KCT|
|Apply differentiation, e.g. to solve optimisation problems||KCT|
|Apply integration, e.g. to find the mean value of function and the area between curves||KCT|
|Solve linear equations using matrix manipulations||KCT|
|Understand and apply simple statistical methods;||KCT|
|Translate real-world problems into mathematical expressions to be solved||CPT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Indicative content includes:
Limits and continuity
What is a derivative
Derivatives of functions
Definite integrals of simple functions
Fundamental theorem of calculus
Numerical methods of integration and their application.
Linear equations and matrices:
Solve linear equations systematically
Matrices and matrix manipulation
A primer on statistics:
Describing and summarising data
Samples and populations
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Help students be confident in manipulating mathematical functions
Provide opportunities to explore mathematical concepts, like differentiation, using Octave
Practise solving real-world problems by translating them into mathematical expressions
Enable students to interpret data using simple statistical techniques
The learning and teaching methods include:
Lectures (11 weeks at 2h) using EVS handsets to gauge the students’ understanding
Laboratory session (10 weeks at 2h)
The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module learning outcomes.
Thus, the summative assessment for this module consists of:
· An individual coursework on differentiation/ integration of functions and matrix manipulation. This addresses LO1, LO2, LO3, LO4, LO6.
· A 2h unseen examination on the whole course content. This addresses all learning outcomes.
The individual coursework will be due around week 8.. The exam takes place at the end of the semester during the exam period.
Formative assessment and feedback
EVS handsets may be used extensively in the lectures, with each lecture consisting of a number of slides explaining the theory followed by a number of slides gauging the students’ understanding. The answers are discussed when necessary, eg if a high proportion (more than 25%) of the students get the answer wrong. Individual formative feedback will also be given during the lab sessions and as part of the summative assessment.
Reading list for FOUNDATIONS OF COMPUTING II : http://aspire.surrey.ac.uk/modules/com1033
Programmes this module appears in
|Computer Science BSc (Hons)||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Computing and Information Technology BSc (Hons)||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Software Development for Business BSc (Hons)||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
|Data Science for Health BSc (Hons)||2||Compulsory||A weighted aggregate mark of 40% is required to pass the module|
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.