Data Structures & Algorithms (G5117)
Data Structures & Algorithms
Module G5117
Module details for 2026/27.
15 credits
FHEQ Level 4
Full Module Description
This module provides an introduction to data structures and algorithms for computer scientists.
You’ll learn about a number of key data structures, including:
static arrays
dynamic arrays
linked lists
stacks
queues
various kinds of trees
hash maps
directed graphs
undirected graphs.
These are presented as implementations of appropriate interfaces.
You’ll also study fundamental computational problems, including the sorting problem and the shortest path problem in graphs. You’ll present solutions to them in the form of important algorithms, such as:
insertion sort
selection sort
merge sort
heap sort
Dijkstra’s algorithm
Bellman-Ford’s algorithm
Algorithms will be presented in pseudocode, with a focus on their asymptotic efficiencies. You’ll also prove the correctness of some of these algorithms.
Module Outline
This module provides an introduction to data structures and algorithms for computer scientists.
You’ll learn about a number of key data structures, including:
static arrays
dynamic arrays
linked lists
stacks
queues
various kinds of trees
hash maps
directed graphs
undirected graphs.
These are presented as implementations of appropriate interfaces.
You’ll also study fundamental computational problems, including the sorting problem and the shortest path problem in graphs. You’ll present solutions to them in the form of important algorithms, such as:
insertion sort
selection sort
merge sort
heap sort
Dijkstra’s algorithm
Bellman-Ford’s algorithm
Algorithms will be presented in pseudocode, with a focus on their asymptotic efficiencies. You’ll also prove the correctness of some of these algorithms.
Module learning outcomes
Evidence knowledge of a variety of fundamental data structures in terms of the operations they support and their asymptotic efficiencies
Evidence understanding of a variety of important algorithms in terms of their execution steps, asymptotic efficiency, and correctness
Evidence ability to efficiently solve basic computational problems using the data structures and algorithms presented in class.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 25.00% | |
| Coursework components. Weighted as shown below. | ||
| Problem Set | T2 Week 7 | 100.00% |
| Unseen Examination | Semester 2 Assessment | 75.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Lecture | 1 hour | 333333333330 |
| Spring Semester | Laboratory | 1 hour | 111111111110 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
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