Module Code: H8DS
Long Title Data Structures
Title Data Structures
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator:  
Module Author: Isabel O'Connor
Departments: School of Computing
Specifications of the qualifications and experience required of staff

Master’s and/or PhD degree in computing or cognate discipline. May have industry experience also.

Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Explain the theory, concepts and principles of linear and non-linear data structures used in computer science.
LO2 Use object-oriented concepts and generics to develop abstract data types and algorithms.
LO3 Identify, evaluate and implement software solutions that use complex data structures and related algorithms to solve a number of fundamental real-world computational problems.
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).

No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

See section 4.2 Entry procedures and criteria for the programme including procedures recognition of prior learning


Module Content & Assessment

Indicative Content
Review of Software Testing; writing a unit test; unit testing will be used throughout the module, where appropriate, when implementing data structures and their corresponding operations.. Introduction to Data Structures (e.g. linear and non-linear)
The concept of recursion. Fibonacci sequence. Example of real-life problems using recursion
Linear Data Structures
ArrayLists. Stacks. Queues. Priority Queues. Single Linked Lists. Double Linked Lists. Operations on linear structures
Binary Trees
Tree Data Structure – characteristics. Tree organization. Tree traversal. Balancing a tree
Search Trees
Tree search characteristics and applicability. Binary search trees. Balanced binary search. Implementing a binary search tree
Representation of graphs. Types of graphs (e.g. undirected, directed, and weighted graphs). Graph traversals algorithms (i.e. depth- and breadth-first traversals). Common graph algorithms – shortest-path algorithm (e.g. Dijkstra’s algorithm)
Assessment Breakdown%
End of Module Assessment50.00%


Full Time

Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3
Non-Marked: Yes
Assessment Description:
The formative assessment will consist of ongoing independent and group programming practical tasks. In-class discussions and oral feedback will be provided throughout these activities.
Assessment Type: Continuous Assessment % of total: 50
Assessment Date: n/a Outcome addressed: 1,2,3
Non-Marked: No
Assessment Description:
The continuous assessment will consist of in-class practical tests. The practical assessments aim to evaluate the students’ knowledge and ability to identify, evaluate, implement and use data structures and algorithms to solve computational problems. Students will be assessed both on their development skills and their ability to convey understanding of the data structures and algorithms they have developed.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 50
Assessment Date: End-of-Semester Outcome addressed: 1,2,3
Non-Marked: No
Assessment Description:
End-of-Semester Final Examination
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
Reassessment Description
Repeat examinationReassessment of this module will be via repeat examination which evaluates all learning outcomes.

NCIRL reserves the right to alter the nature and timings of assessment


Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations (hours) 24 Per Semester 2.00
Tutorial Other hours (Practical/Tutorial) 36 Per Semester 3.00
Independent Learning Independent learning (hours) 190 Per Semester 15.83
Total Weekly Contact Hours 5.00

Module Resources

Recommended Book Resources
  • Michael T. Goodrich,Roberto Tamassia,Michael H. Goldwasser. (2014), Data Structures and Algorithms in Java, John Wiley & Sons, p.736, [ISBN: 1118771338].
  • Mark Allen Weiss. (2010), Data Structures & Problem Solving Using Java, Addison-Wesley Longman, p.985, [ISBN: 0321541405].
Supplementary Book Resources
  • Donald Ervin Knuth. (1997), The Art of Computer Programming: Fundamental algorithms, Addison-Wesley Professional, p.650, [ISBN: 0201896834].
  • Paul Deitel,Harvey Deitel. (2017), Java How to Program, Early Objects, Pearson, p.1296, [ISBN: 9780134743356].
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: