Computer Science online course curriculum

This unit introduces students to the development of computer software, including problem analysis, establishing requirements, designing, implementing, and evaluating. This is a practical unit which will set up students with the programming skills that will be used in later units. 

You’ll learn to:

• Describe the design of a computer program separately from its implementation
• Explain the basic concepts of procedural and object-orientated programming in the design and implementation of computer programs
• Explain debugging and testing methods and how they contribute to robust code
• Design, construct and evaluate simple data structures and algorithms
• Plan, organise and implement program code to support reuse and maintainability of the software (project)
• Provide a critical review of software in terms of quality, design, reuse and robustness, and offer solutions to correct issues encountered
   

Topics covered in this unit may include (but are not limited to) the following:  

• An introduction to a programming language such as Java or C
• Procedural Control: Introduction to procedural system development. Discussion of control structures: sequence, selection, iteration and recursion.
• Objects: Introduction to object-oriented system development. Introduction of objects and classes, inheritance, and polymorphism. Comparison of procedural and object programming.
• Introductions to scope and extent, abstract data types. Design methods for such systems. 

In this unit, students examine how problems are analysed to create requirements specifications, how systems are designed through abstract models, how to plan, test, and maintain software, and how to organise these processes into a software life cycle. They will learn how software engineering is a collaborative process that involves conversations, not just with clients, but with fellow engineers too. Students will learn to think about the software they are producing and to build functioning teams that can work together to deliver software to clients. There will be content about implementation in general, and we will talk about program design from an object-oriented perspective. 

You’ll learn to:

• Identify issues and appropriate solutions for the design and implementation of complex software problems
• Perform evaluations of design solutions to determine if they are fit for purpose
• Demonstrate an understanding of the principles of software development paradigms and their relationship to the appropriateness of an eventual software solution
• Critically evaluate and contrast contemporary software engineering paradigms for defined software engineering problems, given a set of relevant development constraints
   

Topics covered in this unit may include (but are not limited to) the following:  

• The building blocks of software
• Software design paradigms: Waterfall, Iterative, Agile, (for example: Scrum, XP, Kanban), Test-Driven Development (TDD)
• Software engineering concepts
• Principles for engineering complex software systems

This unit will go back to the early days of computing, long before any physical machines existed which would be recognisable today as computers. Do computational problems exist which have no possible ‘method’ for solving? And when a ‘method’ exists, on which resources does it rely upon, and what is its cost? In this unit, students will learn that to answer these questions, they must investigate formally what a 'method' actually is, and appreciate its limits, as well as learn about some unsolvable problems. Students will learn some techniques that are used in many modern systems, including the core component of how compilers are able to process source code when programming. This unit also covers how formal computation models can be used to reason about the relative speed or complexity of various algorithms without needing to write any actual code. 

You’ll learn to:

• Demonstrate an understanding of the fundamental models of computation and the corresponding classes of formal languages, suitably expressed
• Design and apply algorithms for specific computational problems involving languages and computational models of appropriate types (e.g. automata)
• Prove formally that some computational problems are undecidable within a particular class of computational models
• Demonstrate an understanding of the role of grammars in the context of parsing algorithms, by applying the theoretical knowledge to concrete cases
• Give upper bounds on complexities of some decidable computational problems

In this unit, which builds upon the Software Engineering 1 unit, students will further develop their understanding of the concepts and methods required for developing large software intensive systems. The main focus of this unit is to give students practical experience of software engineering as a practice. To do this, students will be undertaking a group project where they will be working in a small agile team developing software. 

You’ll learn to:

• Critically evaluate and contrast contemporary software engineering paradigms for defined software engineering problems, given a set of relevant development constraints
• Compare and contrast the roles, responsibilities, benefits, and drawbacks of different team organization structures for software development, given a set of relevant development constraints
   

Topics covered in this unit may include (but are not limited to) the following:  

• The building blocks of software
• Software design paradigms: Waterfall, Iterative, Agile, (for example: Scrum, XP, Kanban), Test-Driven Development (TDD)
• The effect of the programming language on design and implementation issues. Team structures.

Students will be introduced to cloud computing and will learn how moving to a cloud platform, particularly for databases, has many advantages but also some challenges to overcome, including legal and professional considerations. 

You’ll learn to:

• Understand the nature, capture, formal description, and representation of data and its processing
• Be conversant with emerging data exchange and database developments such as big data, data mining, meta data, and the semantic web
• Critically review the migration towards cloud-based data storage in the context of its professional, legal, and ethical implications
• Practically demonstrate how programs and users can interact with databases through query languages
   

Topics covered in this unit may include (but are not limited to) the following: 

• Data modelling methodologies such as UML and the e-r model
• Relational model, relational algebra, SQL and normalization
• Metadata, the semantic web, data exchange methods, and formats such as XML
• Datamining, big data and pattern discovery in large datasets
• Cloud-based, distributed data storage, and its professional, legal and ethical implications

This unit looks at programming from the perspective of functions. Students will be introduced to the lambda calculus, a mathematical theory developed in the 1930s, which uses functions as its main basis. Students will also discover how functions can be used as the main component within programming by developing programs using Haskell, a functional programming language. Students will explore how functional programming can create complex structures to work efficiently and robustly with minimal side effects and how recursion can be central to creating such effective code. 

You’ll learn to:

• Define and explain the syntax and semantics of the lambda-calculus and its role as a model of computation
• Demonstrate the difference between reduction orders and explain their relationship with call-by-name, call-by-value, and call-by-need evaluation
• Define and explain the simply typed lambda calculus, Hindley-Milner polymorphism, and type inference
• Write programs over structured datatypes in a typed higher-order functional programming language
• Formally reason about and prove properties of functional programs using the formalism of the typed lambda-calculus

This unit gives a broad overview of some foundational topics in artificial intelligence, introducing some of the techniques that are used in this field. Students will implement theoretical knowledge in a practical way, with programming exercises to accompany many of the algorithms discussed, where students can apply these algorithms to solve new problems. 

You’ll learn to:

• Demonstrate understanding of a range of AI techniques, their strengths, and their limitations
• Demonstrate understanding of the fundamentals of probability theory and its role in AI
•  Apply various AI techniques to simple problems
•  Understand a wide range of artificial intelligence (AI) techniques, their advantages, and their disadvantages
•  Appreciate AI as a mechanism to deal with computationally difficult problems in a practical manner
•  Understand the concepts of formal AI and put them into practice
•  Write small to medium-sized programs for aspects of AI
•  Critically evaluate state-of-the-art AI applications
   

Topics covered in this unit may include (but are not limited to) the following:  

• Goals and foundations of AI
• Problem solving (uninformed, heuristic, and adversarial search; constraint satisfaction)
• Logical reasoning (propositional logic, first-order logic, logic programming)
• Probabilistic reasoning (probability models, Bayesian networks)
• Machine learning (possible topics include nearest-neighbour methods, reinforcement learning, neural networks)

This unit introduces key frameworks of entrepreneurial thinking and activity, as well as key considerations and challenges for the entrepreneur and an introduction to fundamental business concepts (e.g. how to seek funding, key operational considerations etc.). Throughout this unit, there are a variety of examples of entrepreneurial activity. Some focus on technology start-ups, but others focus on other industries and sectors with examples chosen to highlight particular approaches, activities, thinking, and opportunities.  

You’ll learn to:

• Identify and analyse market and entrepreneurial opportunities
• Develop business strategy to take advantage of that opportunity
• Critically consider key operational issues
• Investigate alternative funding and financial strategies
• Identify and address key IPR, legal, social, ethical, and professional issues
• Locate and use entrepreneurial resources
• Develop a business plan
• Reflect on the skills and behaviours of entrepreneurs
• Evaluate the phases and challenges of the entrepreneurial process
• Evaluate the resource challenges associated with entrepreneurship
   

Topics covered in this unit may include (but are not limited to) the following:  

• History of entrepreneurial thought
• Market analysis, corporate structure, business funding, hiring, operations, marketing, risk management
• Legal, social, ethical, and professional issues
• Writing a business plan

This unit will give students an advanced level of understanding of current theoretical, methodological, and practical research issues and trends in human computer interaction. Specifically, the unit will communicate to students the importance of usability, how it can be achieved and measured, give them the relevant knowledge and skills related to usability design and evaluation, and provide them some experience of researching advanced HCI topics, summarising the current state of the art, undertaking a relevant study, and presenting the results. 

You’ll learn to:

• Understand human computer interaction theory, be aware of interaction design issues, and be able to carry out different types of evaluation
• Challenge and recognize advances in the state of the art in human-computer interaction research
• Develop research programmes to overcome problems in usability research, development, and evaluation
• Summarise current theoretical, methodological, and practical research issues and trends in human-computer interaction
• Discuss the key issues in usability, usability design, and evaluation
• Critically evaluate the key issues in interaction design
   

Topics covered in this unit may include (but are not limited to) the following:  

• What is usability?  
• What aspects of humans need to be considered in user centred design?  
• What is effective, useful and engaging interaction design?  
• Participatory design and the role of the end user.
• Different approaches to evaluation - analytical, discount, formal, etc.

This unit is designed to provide a comprehensive understanding of deep learning techniques in the first six weeks, ranging from foundational knowledge to the latest advancements in the field. Students will explore a variety of deep learning models. Through a hands-on approach, students will gain practical experience in implementing deep learning algorithms, including how to build, train, evaluate, and fine-tune deep learning algorithms to solve real-world problems. Students will also gain experience in training deep neural networks with Cloud GPU using services such as Google Colab. In the last two weeks, one of the most important aspects of intelligent behaviour, AI planning, will be introduced. Students will learn how to represent an AI planning problem and also practice solving it with a language called PDDL.

You’ll learn to:

• Demonstrate an understanding of a range of deep learning techniques, including MLP, CNN and RNN
• Demonstrate an understanding of how deep learning algorithms work, how to build them, and how to train them
• Apply deep learning techniques to solve real-life problems using deep learning libraries
• Solve AI planning problems with PDDL
   

Topics covered in this unit may include (but are not limited to) the following:  

• Multi-Layer Perception (MLP), a network inspired by the biological neural networks in our brains
• Convolutional Neural Network (CNN) for computer vision
• Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) for sequence data processing
• PDDL as an approach to solve planning problems

Cybersecurity is often in the news these days. Either theft of personal data, or organisations closed by ransomware, or both. But how do these attacks arise, and what can people, specifically computer people, do about them? We will see that many attacks could have been prevented by very simple measures, sometimes in programming, but also in systems, such as multi-factor authentication. 

You’ll learn to:

• Describe common security models
• Discuss what it means for a given system to be 'secure'
• Identify security weaknesses in proposed systems
• Understand the principles and application of cryptography
   

Topics covered in this unit may include (but are not limited to) the following:  

1. Philosophical, legal, ethical issues. What is a person? Passwords, user ids and biometrics.

2. What are authorisation and delegation? What are data? Security against theft, destruction, interception, tampering. Some thoughts on physical security. Data Protection Act, Freedom of Information Act, Regulatory and Investigatory Powers Act. Military/government requirements for security. 

3. Security within a computer. Hardware support for security: States and memory protection. Memory mapping, virtual memory, and security. The Unix Security model: chown, chgrp, setuid, and chroot. Strengths and weaknesses of the Unix security model: Common attacks.

4. Security within networks. 'Man in the middle' attacks. What does the 's' in https signify?

5. Case studies: For example, internet worm. Power attacks and other covert channels. A chain can be weaker than its weakest link: the Crouch-Davenport attack.

The aims of this unit are to prepare students for their dissertation research project, giving them an advanced level of understanding of what a research project is, what the various research themes are in the Department of Computer Science at the University of Bath, and how to find and critically evaluate relevant literature. Throughout this unit, students will develop a feasible project proposal that will lead to an effective dissertation. As part of their research proposal students will need to start thinking about project methodologies and the ethical considerations needed for their project. 

You’ll learn to:

• Summarise and critique research papers in Computer Science and AI
• Distinguish various research themes in the selected field with a broad understanding of suitable approaches and methodologies
• Determine which research topic they would like to work on for their dissertation
• Critically analyse and review previous work in the chosen subject area
• Create a feasible project proposal for the dissertation
• Understand the principles of structuring a dissertation
• Reason for methodological and ethical considerations of their chosen topic
   

Topics covered in this unit may include (but are not limited to) the following:  

• Selecting an appropriate topic for a dissertation research project
• Researching relevant academic literature
• Assessing the relevance of research publications
• Assessing the quality of secondary research resources, such as web resources
• Critical analysis of research papers
• Preparation of a research proposal

Students will follow an appropriate problem-solving route, building on the detailed dissertation project proposal written in the Research Project Preparation unit. The student will analyse possible problem solutions based on an extensive literature and technological review of related research work and choose appropriate methods and approaches. This will lead to the implementation of the chosen solution, its testing and evaluation. In most cases the project will be a synthesis of both an analytical and a computational approach to solving or investigating a substantial computer science problem. However, projects will vary in style, and some may be more experimentally based, and some may be purely theoretical. A comprehensive dissertation will be submitted at the completion of the project.

After completing the research you will be able to:

•  Identify the tasks to be completed in a research project, plan a scheme of work, and complete the project to a professional standard
• Conduct independent research, following the ethical principles and processes
• Assemble and create the necessary analysis, design and development tools, carry out the development of the solution of a technical problem in Computer Science, and evaluate the effectiveness of the solution against common standards of quality
• Demonstrate the successful completion of these tasks in a well-structured and coherently written dissertation, which will include a discussion of the research outcomes of the work, and future directions
• Evaluate and critique the project