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# Algorithms, data structures and computability

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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:

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, greedy algorithms and dynamic programming. 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 sets, functions, logic and proofs, using formal mathematical notation. This will be in the context of concrete applications, such as databases. At this point, you’ll also learn about the limitations of computational problem solving (non-computability and the P ≠ NP conjecture).

## Entry requirements

To enrol on M269 to start in October, you must have:

• passed Introduction to computing and IT 2 (TM112) or be enrolled on it and have started it in April.

You need an understanding or experience of computing; an understanding and experience of programming; and some knowledge of mathematics – check if you’re ready for M269, with our self-assessed quiz.

## What's included

• a week-by-week study planner
• course-specific materials
• audio and video content
• assessment details, instructions and guidance
• online tutorial access

The first two parts of the module are delivered using Jupyter notebooks that include the module’s text and the code for the examples and exercises. The first two parts are also available in PDF and HTML. You'll also be provided with one printed module book, covering the third part of the module.

### Computing requirements

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.

To join in spoken conversations in tutorials we recommend a wired headset (headphones/earphones with a built-in microphone).

Our module websites comply with web standards and any modern browser is suitable for most activities.

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.

## Teaching and assessment

• Marking your assignments (TMAs) and providing detailed feedback for you to improve.
• Guiding you to additional learning resources.
• Providing individual guidance, whether that’s for general study skills or specific module content.
• Facilitating online discussions between your fellow students, in the dedicated module and tutor group forums.

Module tutors also run tutorials throughout the module. Where possible, recordings of online tutorials will be made available to students. While these tutorials won’t be compulsory for you to complete the module, you’re strongly encouraged to take part. If you want to participate, you’ll likely need a headset with a microphone.

### Assessment

The assessment details for this module can be found in the facts box above.

You must use the online eTMA system to submit your tutor-marked assignments (TMAs).

## If you have a disability

The OU strives to make all aspects of study accessible to everyone and this Accessibility Statement outlines what studying M269 involves. You should use this information to inform your study preparations and any discussions with us about how we can meet your needs.

## Future availability

Algorithms, data structures and computability (M269) starts once a year – in October (places are limited and in high demand, so enrol early).