Algorithms, data structures and computability
The aim of this module is to help you become a computational problem solver. You’ll learn techniques to efficiently solve computational problems and apply them using the Python programming language. 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 is the module for you if – whatever your field – you need to 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:
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.
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.
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).
To enrol on M269 to start in October, you must have:
You need an understanding or experience of computing; an understanding and experience of programming in Python; and some knowledge of mathematics – check if you’re ready for M269, with our self-assessed quiz.
If you’re not sure you’re ready, talk to an adviser.
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.
You’ll need a desktop or laptop computer with an up-to-date version of 64-bit Windows 10 (note that Windows 7 is no longer supported) or macOS and broadband internet access.
Additional requirements for this module:
- 5GB free disk space
- Chrome, Firefox or Safari web browser (Microsoft Edge is not compatible with Jupyter).
To join in spoken conversations in tutorials we recommend a wired headset (headphones/earphones with a built-in microphone).
Our OU Study mobile App will operate on all current, supported, versions of Android and iOS. It's not available on Kindle.
It's also possible to access some module materials on a mobile phone, tablet device or Chromebook, however, as you may be asked to install additional software or use certain applications, you'll also require a desktop or laptop as described above.