This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Undergraduate Handbook 2016-2017 (archived)
Module COMP1081: ALGORITHMS AND DATA STRUCTURES
Department: Computer Science
COMP1081: ALGORITHMS AND DATA STRUCTURES
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Open | 1 | 20 | Available in 2016/2017 | Durham |
Pre-requisites
- A-level Mathematics Grade A.
Corequisites
- Introduction to Programming OR Computational Thinking
Excluded Combinations
- None
Aims
- To introduce the theory and practice of problem solving in computing through the development of algorithms, and their associated data structures, for common computer science problems.
Content
- Machine models.
- Pseudocode and control flow structures.
- Basic data structures.
- Paradigms and techniques.
- Analysis of algorithms.
- Basic sorting and searching algorithms.
- Basic graph algorithms.
- Basic string algorithms.
Learning Outcomes
Subject-specific Knowledge
- On completion of the module, students will be able to demonstrate:
- a knowledge of common data structures and their relative advantages and disadvantages
- familiarity with common algorithmic techniques
- an appreciation and knowledge of asymptotic notation.
Subject-specific Skills
- On completion of the module, students will be able to demonstrate:
- an ability to implement and use common data structures
- an ability to select, apply and analyse algorithms.
Key Skills
- On completion of the module, students will be able to demonstrate:
- the acquisition of a wide range of problem-solving skills
- a facility to apply numeric and systematic reasoning to problem-solving.
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures enable the students to learn new material relevant to algorithms and their data structures.
- Problem classes enable the students to put into practice learning from lectures and strengthen their understanding through application; in particular, thorugh the implementation of algorithms.
- Students are assessed by formative and summative assessment and examinations.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| lectures | 44 | 2 per week | 1 hour | 44 | |
| practical classes | 22 | 1 per week | 2 hours | 44 | Yes |
| preparation and reading | 112 | ||||
| total | 200 |
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Examination | 66% | Examination | 2 hours | 100% | Yes |
| Coursework | 34% | Practical work | 100% | Yes |
Formative Assessment
Example formative exercises are given during the course. Additional revison lectures are given in the module's lecture slots in the 3rd term.
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.