Introductory description

The aim of the module is to equip students with a grounding in foundations of computing, to enable students from a wider background to confidently undertake a taught masters programme in advanced computing topics.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

-Computational Mathematics and Statistical Methods (logic, sets, probability and hypothesis testing)

-Programming and Data Structures (procedural and object oriented programming, using appropriate data structures)

-Algorithms and Complexity (analysis of algorithms, searching and sorting algorithms and calculation best, worst and average case complexities)

Learning outcomes

By the end of the module, students should be able to:

• Understand the foundational concepts of computing mathematics and statistical methods.
• Write computer programs with an understanding of type systems and control flow in both procedural and object oriented programming paradigms and have strategies for debugging. Use data structures such as arrays, lists stack, queues, hash tables and trees effectively.
• Understand the relationships between algorithms and complexity.

Indicative reading list

Huth and Ryan, Logic in Computer Science
Daniel Liang Y, Introduction to Java Programming - Comprehensive (7th ed), Pearson, 2008. Goodrich MT, Tamassia R and Goldwasser MH, Data Structures and Algorithms in Java (6/e), Wiley, 2015.
Kleinberg J and Tardos E, Algorithm Design, Addison Wesley, 2005.

Subject specific skills

Computing Mathematics and Statistical Methods
Computing Mathematics: Logical analysis; Propositional Logic; Sets and Relations; Predicate Logic
Statistical Methods: Probability; Distributions; Significance Tests; Confidence Intervals; Hypothesis Testing
Programming and Data Structures
Programming: Procedural and Object Oriented
Programming; Type Systems; Control Flow; Debugging
Data Structures: Arrays, Lists, Stacks and Queues; Hash
Tables; Trees

Algorithms and Complexity
Algorithms: Basics of Algorithm Analysis; Sorting and
Searching; Graph Algorithms
Complexity: Best, Worst and Average Case Complexity;
Applications of the Master Theorem; Problem Instances

Computing Architectures
Processors and Memory Hierarchies; Client Server; Models of Distributed Systems; Application Domains
-Computing Grand Challenges
-Big Data; Internet of Things; Privacy; Quantum Computing; New Approaches in Al; Smart Cities

Transferable skills

Ability to program in Python, to design and analyse efficient computing algorithms, to apply logical analysis as well as probability and statistical tools to computationally address mathematics and statistics problems.