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COMP20230

Academic Year 2024/2025

Data Structures & Algorithms (Conversion) (COMP20230)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
2 (Intermediate)
Credits:
5
Module Coordinator:
Dr Mark Matthews
Trimester:
Spring
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This is a core module on the MSc. Computer Science (Conversion) and H.Dip in Computer Science programmes. The module introduces the design and analysis of the efficiency of algorithms and the principles of data structures in Python and in the Object Oriented Programming paradigm. In this module students will learn how to analyse, design and implement a finite set of well-defined instructions for accomplishing some task (an algorithm) along with ways of storing data in a computer so that it can be used efficiently (a data structure). A well-designed data structure or algorithm allows a variety of critical operations to be performed using as little resource, both execution time and memory space, as possible. In this module students will learn how to implement the basic data structures using Python. Students will be able at the end of this module to design and implement robust and reusable algorithms and data structures.

About this Module

Learning Outcomes:

On successful completion of this module the learner will be able to:

1. Understand how to determine the amount of resources (such as time and storage) necessary to execute a particular algorithm (algorithm analysis).
2. Understand the structure, nature and use of fundamental data structures including, Arrays, Linked Lists, Stacks, Queues, Trees and Dictionaries.
3. Implement the data structures in Python.
4. Understand the object-oriented programming constructs needed to encode a data structure and its access algorithms.
5. Design programs using these constructs to solve large problems.
6. Successfully write, compile, debug and run programs using these constructs.

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Tutorial

12
Practical

16
Autonomous Student Learning

58
Total

110

Approaches to Teaching and Learning:
active/task-based learning; peer and group work; lectures; lab/tutorial based problems

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
COMP20280 - Data Structures, COMP20290 - Algorithms


 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered

Not yet recorded.

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Peer review activities

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Assoc Professor Madhusanka Liyanage Lecturer / Co-Lecturer
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