Algorithms, data structures and computability
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This module aims to help you become a computational problem solver. You’ll learn techniques to solve computational problems efficiently and apply those techniques using Python. You’ll also learn about the limitations of computing: which problems can’t be solved algorithmically or for which no efficient solutions are known. This module is suitable if – whatever your field – you need to design or implement an efficient algorithm or to understand both the power and the limitations of computing. Though the focus is on the underlying ideas, you’ll also work with some mathematical concepts and notation.
What you will study
You’ll learn to take a problem and state it precisely in order that it can be solved with a computer. In other words, you’ll learn to express the problem in a way which allows you to write an algorithm for solving it. However, not all algorithms are equally good solutions. For that reason, you’ll also learn how to analyse the speed and efficiency of algorithms and establish whether an algorithm really does what it is supposed to do. Finally, you’ll delve into the very foundations of computing. You’ll learn which problems cannot be solved with an algorithm. You’ll also learn what the limits are on the speed with which algorithms can solve many important practical problems.
The module comprises three parts, each ending with an assignment:
Part 1
In the first part, you’ll learn about the basic data structures for organising data, like lists, stacks, queues, dictionaries, and sets. You’ll also learn how to analyse the complexity of an algorithm and how to measure its runtime.
Part 2
The second part covers two non-linear data structures: trees and graphs. The former can represent hierarchical data and the latter can model social, transport and other kinds of networks. The main focus of the second part are algorithmic techniques like search (brute-force, breadth-first and depth-first), divide and conquer, recursion and greedy algorithms. These are general-purpose techniques for solving a wide range of problems.
Part 3
In the third part, you’ll further develop your understanding of graphs and algorithmic techniques (backtracking, dynamic programming). You’ll also learn about the limitations of computational problem solving (non-computability and the P ≠ NP conjecture).
Entry requirements
You must have passed or already started one of the following modules:
You need:
- an understanding or experience of computing
- an understanding and experience of programming in Python
- some knowledge of mathematics.
Are you ready for M269?
What's included
You’ll have access to a module website, which includes:
- a week-by-week study planner
- course-specific materials
- audio and video content
- assessment details, instructions and guidance
- online tutorial access
- access to student and tutor group forums.
The module is delivered entirely using Jupyter notebooks which includes the module text and the code for the examples and exercises. The material is also available in PDF and HTML format and the code is also available in separate Python files.
Computing requirements
- Primary device – A desktop or laptop computer with 5GB of free disk space. It’s possible to access some materials on a mobile phone, tablet or Chromebook; however, they will not be suitable as your primary device.
- Peripheral device – Headphones/earphones with a built-in microphone for online tutorials.
- Our OU Study app operates on supported versions of Android and iOS.
- Operating systems – Windows 11 or latest supported macOS. Microsoft will no longer support Windows 10 as of 14 October 2025.
- Internet access – Broadband or mobile connection.
- Browser – Google Chrome is recommended. Mozilla Firefox and Safari may be suitable. Microsoft Edge is not compatible with Jupyter.