INTRODUCTION TO COMPUTER SCIENCE AND CLUSTER COMPUTING - 2027/8
Module code: ENG0026
Module Overview
This exciting and interesting new module provides an accessible introduction to some fundamental topic areas in computer science. There is a strong focus on practical work. You will develop a wide range of skills and knowledge that will be very useful on your subsequent undergraduate studies as well as enhancing your employability.
Module provider
Computer Science and Electronic Eng
Module Leader
HARRISON Richard (CS & EE)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 3
Module cap (Maximum number of students): 50
Overall student workload
Independent Learning Hours: 59
Lecture Hours: 11
Tutorial Hours: 11
Laboratory Hours: 22
Guided Learning: 36
Captured Content: 11
Module Availability
Semester 2
Prerequisites / Co-requisites
None required
Module content
(1) Fundamental mathematics, including set theory, logic, relations, probability, Baye's Theorem and Combinatorics. Computer arithmetic: Binary and Hexadecimal, Directed Acyclic Graphs. (2) Data structures such as dictionaries and trees. (3) Algorithms, including examples used for specific purposes such as sorting. (4) Software engineering including paradigms, requirements engineering, version control, software ethics, the software engineer and society. (5) Fundamentals of High Throughput computing including developing familiarity with Linux OS, VS Code and relevant tools. (6) Environmental impact of the global digital infrastructure: resources, e-waste etc.
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| Coursework | Individual Short Coursework Task 1 | 10 |
| Coursework | Individual Short Coursework Task 2 | 10 |
| Coursework | Individual Short Coursework Task 3 | 10 |
| Coursework | Individual Short Coursework Task 4 | 10 |
| Coursework | Individual Short Coursework Task 5 | 10 |
| School-timetabled exam/test | Mid Semester Test [30 minutes] | 15 |
| Examination | End of module written examination [1 hour] | 35 |
Alternative Assessment
Single, extended coursework task plus written examination.
Assessment Strategy
The assessment strategy comprises 50% Coursework (five short coursework tasks at 10% each) to enable students to demonstrate practical problem solving and implementation skills. Summative written assessments (mid-semester test 15% and exam 35%) will enable students to demonstrate their recall, theoretical problem solving and design skills.
Module aims
- To provide Foundation Year students with a relatively broad, accessible introduction to some fundamental topic areas in Computer Science.
- To enable students to develop skills and knowledge that will help to prepare them for year 1 of their Computer Science degree programme as well as developing additional professional/employability skills and knowledge that will be attractive to employers.
Learning outcomes
| Attributes Developed | ||
| 001 | Students will be able to recall and apply fundamental mathematical concepts, including set theory, logic, relations, probability, Baye's Theorem and Combinatorics. Computer arithmetic: Binary and Hexadecimal, Directed Acyclic Graphs. | CK |
| 002 | Students will be develop familiarity with different types of data structure and be able to implement these structures programmatically | CK |
| 003 | Students will be able to describe some examples of widely used algorithms and be able to implement these algorithms in specific problem solving contexts, such as sorting. | CK |
| 004 | Students will develop familiarity with established software engineering paradigms and processes, including requirements engineering and version control | CKT |
| 005 | Students will be able to describe and discuss the importance of the software engineer in society, the impact of software engineering and ethical considerations. | CK |
| 006 | Students will develop practical experience of coding with Python and using Linux | CKPT |
| 007 | Students will be able to describe and discuss the hardware associated with cluster computing and the similarities and differences between HTC, HPC and Blockchain. | CK |
| 008 | Students will be able to describe and discuss the basic principles of a variety of machine learning algorithms and apply these algorithms to solving practical problems | CKPT |
| 009 | Students will gain practical experience of HTC usage in collaboration with international partners. This will include an introduction to using Powershell, Linux, VS Code, HTCondor and DAG pipelines for Python based Machine Learning tasks | CKPT |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
Lectures will be used to introduce fundamental theories, techniques, case studies and briefings, mainly through powerpoint presentations.
Tutorials will be used for discussion based and problem solving activities set out in worksheets.
Laboratory sessions will include briefings on practical tasks and detailed task worksheets.
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: ENG0026
Other information
This module presents the opportunity to collaborate on projects with international students at overseas partnering institutions. All students are expected to engage with this via online training sessions. This is not assessed directly but is of direct relevance to the practical laboratory tasks which are assessed.
Programmes this module appears in
| Programme | Semester | Classification | Qualifying conditions |
|---|---|---|---|
| Computer Science with Foundation Year BSc (Hons) | 2 | Compulsory | Each unit of assessment must be passed at 50% to pass the module |
| Mathematics with Data Science with Foundation Year BSc (Hons) | 2 | Optional | A weighted aggregate mark of 50% is required to pass the module |
| Mathematics with Foundation Year BSc (Hons) | 2 | Optional | A weighted aggregate mark of 50% is required to pass the module |
| Financial Mathematics with Foundation Year BSc (Hons) | 2 | Optional | A weighted aggregate mark of 50% 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 2027/8 academic year.