# Module ICE-2201:Data Structures & Algorithms

### Module Facts

Run by School of Computer Science and Electronic Engineering

20 Credits or 10 ECTS Credits

Semester 1

### Overall aims and purpose

To allow students to use, select, implement and analyse a range of common abstract data types and algorithms.

### Course content

Indicative content includes:

• Data Types, Abstract Data Types, Theory, implementations and uses of stacks, queues, lists, trees, graphs, hash tables, binary search trees.
• Constraints and Trade offs (time vs. space).
• Criteria for algorithms. Recursive algorithms: Towers of Hanoi. Data processing algorithms: sorting and searching. Graph theoretic algorithms: traversal, shortest path.
• Efficiency measures for time and space: rates of growth; asymptotic behaviour, big-O notation. Algorithm complexity classes (P, NP, NP-complete).

### Assessment Criteria

#### good

Equivalent to the range 60%-69%. Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.

#### threshold

Equivalent to 40%. Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.

#### excellent

Equivalent to the range 70%+. Assemble critically evaluated, relevent areas of knowledge and theory to constuct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.

### Learning outcomes

1. Discuss the operation of common search and sort algorithms relating to the ADTs.

2. Identify common Abstract Data Types (ADTs) and their respective qualities and limitations.

3. Estimate the impact, in terms of space and time, of algorithms using Complexity Theory.

### Assessment Methods

Type Name Description Weight
EXAM Examination

End of semester unseen examination.

60
COURSEWORK Assignment 1

Programming assessment examining use of ADTs.

20
COURSEWORK Assignment 2

Written assessment involving some programming, to compare the complexity of algorithms with two specified ADT implementations.

20

### Teaching and Learning Strategy

Hours
Lecture

Traditional lectures (2 hrs x 12 weeks).

24
Private study

Tutor-directed private study including individual assessment and revision.

176

### Transferable skills

• Numeracy - Proficiency in using numbers at appropriate levels of accuracy
• Computer Literacy - Proficiency in using a varied range of computer software
• Information retrieval - Able to access different and multiple sources of information
• Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
• Argument - Able to put forward, debate and justify an opinion or a course of action, with an individual or in a wider group setting
• Self-awareness & Reflectivity - Having an awareness of your own strengths, weaknesses, aims and objectives. Able to regularly review, evaluate and reflect upon the performance of yourself and others

### Subject specific skills

• Apply an understanding and appreciation of continuous improvement techniques
• Solve problems logically and systematically;
• Knowledge and understanding of facts, concepts, principles & theories
• Use of such knowledge in modelling and design
• Problem solving strategies
• Analyse if/how a system meets current and future requirements
• Deploy theory in design, implementation and evaluation of systems
• Evaluate systems in terms of quality and trade-offs
• Deploy tools effectively
• Development of general transferable skills
• System Design
• Knowledge and understanding of mathematical principles
• Knowledge and understanding of computational modelling
• Specify, deploy, verify and maintain computer-based systems