Module: Computer Programming 3 (Data Structures & Algorithms)

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Computer Programming 3 (Data Structures & Algorithms)
Module Code	CSC1030 (ITS: CA268)
Faculty	Computing	School	Engineering & Computing
NFQ level	8	Credit Rating	5

Description

The module aims to give students an understanding of basic data structures and algorithms, most especially with respect to managing collections of data such as sets, sequences, and maps. Students will learn how to specify collections using abstract data types (ADTs), how to implement them using a variety of techniques such as linked lists and search trees, and how to package them using object-oriented programming methods. Students will learn a range of fundamental algorithms including searching and sorting, and how to assess their computational cost. Students will also develop practical skills in implementing and testing algorithms on computers.

Learning Outcomes

1. Use iterative and recursive techniques to design and implement elementary algorithms.
2. Describe a variety of basic ADTs including sets, sequences, stacks, queues, graphs, trees and maps.
3. Implement the above ADTs using arrays, linked lists, search trees, and hash tables
4. Use object-oriented techniques such as interfaces, inheritance, and generics to package ADTs appropriately.
5. Analyse the time and space complexity of elementary algorithms, and justify the complexity of the above ADT implementations.
6. Incorporate ADTs and associated implementations appropriately into program solutions.
7. Describe and use a variety of searching and sorting algorithms.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	Theory, practice and examples
Laboratory	20	Completion of problem sets
Directed learning	30	Completion of problem sets
Independent Study	76	Review of lecture material, background reading and independent practice
Total Workload: 150

Section Breakdown
CRN	10204	Part of Term	Semester 1
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Gerard Marks	Module Teacher	Hossein Javidnia

Type	Description	% of total	Assessment Date
Assessment Breakdown
Laboratory Portfolio	Laboratory exercises	15%	Every Week
Assignment	A set of problems that students work on in their own time	15%	Every Week
Formal Examination	End-of-Semester Final Examination	70%	End-of-Semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	1,
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Object-oriented programming
Review of OO Concepts: classes and methods, inheritance, abstract base classes/interfaces

Algorithms
Designing algorithms using iteration and recursion. Basic algorithmic complexity, big-O notation, time vs space complexity, comparison of algorithms

Abstract Data Types
The notion of abstract data type (ADT). Sets, lists, sequences, maps, iterators, generators, stacks, queues as ADTs. Implementing and using them via the built-in collections.

Basic Data Structures
Linked lists, doubly-linked lists, binary search trees, balanced search trees, tree traversal; comparison of time complexities.

Hash Tables
Hash tables, implementation in arrays, collision resolution (e.g. chaining), extensible hash tables. Directory structures.

Searching and Sorting
Bubble sort, insertion sort, selection sort, quicksort, merge sort, radix sort, binary search, string search (Knuth-Pratt-Morris).

Graph Structures and Algorithms
Representation (adjacency matrix vs adjacency list). Graph colouring. Searching strategies (DFS vs BFS), Dijkstra’s Algorithm, Spanning Tree Algorithms (Kruskal, Prim).

Indicative Reading List

Books:

Brad Miller and David Ranum: 2014, Problem Solving with Algorithms and Data Structures, Franklin, 978-1590282571
M Goodrich: 2013, Data Structures and Algorithms in Python, Wiley, 978-1118290279

Articles:
None

Other Resources

None