Module Identifier | CS21120 | ||

Module Title | PROGRAM DESIGN, DATA STRUCTURES AND ALGORITHMS | ||

Academic Year | 2001/2002 | ||

Co-ordinator | Dr Mark Ratcliffe | ||

Semester | Semester 2 (Taught over 2 semesters) | ||

Other staff | Dr Frederic Labrosse, Dr Lynda Thomas, Dr Simon Garrett | ||

Pre-Requisite | CS12420 or CS10720 | ||

Course delivery | Lecture | 44 lectures | |

Practical | Up to 16 x 1 hr (start in week 5) | ||

Workshop | Up to 4 x 1hr | ||

Assessment | Supplementary examination | 2 Hours | 100% |

Course work | A2 Two pieces. There will also be regular worksheets with penalties for non-submission. | 60% | |

Exam | 2 Hours A1 The resit assessment will be by written examination only. Written examination | 40% | |

Further details | http://www.aber.ac.uk/compsci/ModuleInfo/CS21120 |

This module builds on the foundations of the first year modules on program design and provides a thorough grounding in the design of data structures and algorithms and gives further insight into object-oriented design.

This module provides an introduction to data structures and their use in solving programming problems. The course emphasises the use of abstract data types and the contribution that abstraction and encapsulation can make to the comprehensibility, reusability and robustness of programs. The course also examines the efficiency of well-known algorithms in order to provide a basis for students to make informed choices about data structures and algorithms. Java is used as the language of implementation with the intent of providing a means of allowing the student to naturally express these design objectives in code.

As well as providing a solid grounding in the major data structures and algorithms of Computer Science, the course stresses the development of problem solving skills through a number of programming worksheets.

On successful completion of this module, students should be able to:

- demonstrate their understanding of the principles of abstraction and encapsulation as they apply to the design of abstract data types and programs (A1, A2);
- analyse and evaluate the time and space behaviour of algorithms and understand how this is expressed and determined (A1, A2);
- recognise the importance of this analysis in the design of software (A1, A2);
- recognise the importance of the classes P and NP in the analysis of algorithms (A1);
- describe some of the main approaches to algorithm design such as greedy algorithms, divide and conquer and dynamic programming (A1);
- demonstrate judgement in evaluating and choosing appropriate data structures and algorithms for a range of programming problems (A1, A2);
- design and implement significant programs in Java (A2).

1. Course Overview - 5 Lectures

An introduction into time/space complexity. Issues of correctness as they relate to the definition of ADTs. The key ideas of abstraction and encapsulation. Notations for describing ADTs. Review of Java support for their implementation: packages, exceptions and interfaces.

2. Introduction to Complexity - 3 Lectures

O() notation, growth rates. Measurement of execution time of some real programs and estimation of their time complexity. Some examples of time/space trade-offs.

3. Classes of Algorithm - 4 Lectures

An overview will be given on the different classes of algorithm; for example, divide and conquer and greedy algorithms. Genetic algorithms will also be discussed. P and NP.

4. Recursion - 2 Lectures

An introduction to recursive thinking. Examples of recursion.

5. Storing and Retrieving Data by Key (1) - 12 Lectures

This problem will be used to motivate the discussion of a wide variety of different implementation techniques. The features of some typical solutions will be related to the dimensions of the problem such as the volume of data to be handled, volatility and the operations required. Internal Storage: linear and binary searching. Linked representations; an introduction to hashing, binary search trees and heaps.

6. Storing and Retrieving Data by Key (2): External storage - 4 Lectures

Performance issues. Hashing and B-tree organisations. The Hashable class in Java.

7. Representing Text - 4 Lectures

String matching algorithms and their performance. Search engines case study.

8. Sorting - 4 Lectures

A comparison of divide and conquer, priority queue and address calculation based sorting algorithms. Performance characteristics of these algorithms will be discussed.

9. Representing Complex Relationships: Graphs - 6 Lectures

Some examples of greedy algorithms. Terminology and implementation considerations. A look at some graph-related problems such as: finding a route (shortest paths); planning a communications network (minimum spanning trees); network routing management (flow graphs); compiling a program or planning a project (topological sorting).

T. Budd. (2001)

Michael Main. (1999)

T.A. Standish. (1998)

Alfred Aho, John Hopcroft, and Jeffrey Ullman. (1983)

Alfred Aho and Jeffrey Ullman. (1995)

Thomas A Standish. (1994)

Rebecca Wirfs-Brock, Brian Wilkerson, and Lauren Wiener. (1990)