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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 Przemek Grabowicz
Trimester:
Spring
Mode of Delivery:
On Campus
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
Exam (Online): In class multiple-choice 30-minutes test. Week 12 Alternative linear conversion grade scale 40% No
15
 No
Group Work Assignment: Real-World Problem Solving with Algorithms and Data Structures. Week 11, Week 12 Alternative linear conversion grade scale 40% No
35
 No
Assignment(Including Essay): Individual Project: Iterative & Recursive Algorithm Analysis Week 8, Week 9 Alternative linear conversion grade scale 40% No
35
 No
Assignment(Including Essay): Lab assignments Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10 Alternative linear conversion grade scale 40% No
15
 No

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
Dr Przemek Grabowicz Lecturer / Co-Lecturer

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Practical Offering 1 Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Fri 11:00 - 12:50
Spring Tutorial Offering 1 Week(s) - 21, 23, 24, 25, 26, 29, 30, 31, 32 Mon 15:00 - 15:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Tues 11:00 - 11:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 10:00 - 10:50
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