# FOUNDATIONS OF COMPUTING II - 2020/1

Module code: COM1033

## Module Overview

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.

### Module provider

Computer Science

### Module Leader

LI Yunpeng (Computer Sci)

### Number of Credits: 15

### ECTS Credits: 7.5

### Framework: FHEQ Level 4

### Module cap (Maximum number of students): N/A

## Overall student workload

Lecture Hours: 33

Laboratory Hours: 11

## Module Availability

Semester 2

## Prerequisites / Co-requisites

None

## Module content

Indicative content includes:

- Differentiation:

- Limits and continuity
- What is a derivative
- Derivatives of functions
- Optimisation problems

- Integration:

- 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
- Distributions
- Samples and populations
- Significance testing

## Assessment pattern

Assessment type | Unit of assessment | Weighting |
---|---|---|

Coursework | COURSEWORK I INDIVIDUAL | 40 |

Examination | 2HR UNSEEN EXAM | 60 |

## Alternative Assessment

N/A

## Assessment Strategy

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.

## Module aims

- 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.

## Learning outcomes

Attributes Developed | ||

1 | Differentiate and integrate some elementary functions, including polynomials, exponential and trigonometric functions; | KCT |

2 | Apply differentiation, e.g. to solve optimisation problems | KCT |

3 | Apply integration, e.g. to find the mean value of function and the area between curves | KCT |

4 | Solve linear equations using matrix manipulations | KCT |

5 | Understand and apply simple statistical methods; | KCT |

6 | Translate real-world problems into mathematical expressions to be solved | CPT |

Attributes Developed

**C** - Cognitive/analytical

**K** - Subject knowledge

**T** - Transferable skills

**P** - Professional/Practical skills

## 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)

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

## Reading list

https://readinglists.surrey.ac.uk

Upon accessing the reading list, please search for the module using the module code: **COM1033**

## Programmes this module appears in

Programme | Semester | Classification | Qualifying conditions |
---|---|---|---|

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 |

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 2020/1 academic year.