•  Basic Java syntax and semantics
• Classes and objects
•  Methods and constructors
•  Arrays and collections
•  Console and file input/output streams
•  Exceptions and error-handling
•  Interfaces, information-hiding and message-passing
•  Inheritance and polymorphism
•  Unit testing using the JUnit framework
•  Graphical User Interface
•  Using and generating Javadoc documentation

Tools Used: Java, IntelliJ/VSCode/Eclipse

Credit Hours: 20

Procedures in creating and maintaining a database for use with a web application. Programming Concepts: Programming concepts relating to: programming fundamentals User Interfaces: Techniques for utilising keyboard and mouse controls inputs to create user interfaces, Control and Manipulation of Media: Techniques for controlling playback and manipulating the presentation of various types of media such as: images, sound and video. Dynamic Content: Linking media applications to dynamic data sources.

Tools Used: Php, Mysql

Credit Hours: 20

• Overview of AI and ML
• Machine Learning Concepts and Terminology
• Data Exploration and Pre-processing
• Data Visualisation
• ML algorithms: Regression modelling
• ML algorithms: Clustering modelling
• ML algorithms: Classification modelling
• Model Evaluation
• ntroduction to Deep Learning
• Introduction to Natural Language Processing (NLP)
• Agent Technology and Game Theory
• Reinforcement Learning

Tools Used: Numpy, Pandas, matplotlib, seaborn, scikit-learn,Keras, Tensorflow,NLTK, SpaCy

Credit Hours: 20

• Asymmetric and symmetric cryptography.
• Hash functions
• PKI.
• Financial security models.
• Discretionary access control, mandatory access control.
• Malware
• Firewalls
• VPNs
• Code injection attacks and defences.
• Security-relevant legislation and best practice frameworks.

Tools Used: OpenSSL,Python, SELinux ,Cuckoo Sandbox/VirusTotal, Nessus/OpenVAS

Credit Hours: 20

Identify and explain concepts, notions and approaches related to software design and requirements engineering; · Construct requirements use-case models based on UML use case diagrams and accompanying use-case specifications;

• Draw UML class diagrams to describe data (or domain) models
• Draw system sequence diagrams to describe interactions of systems with environment
• Write operation contracts, based on pre- and post-conditions, using natural language
• Construct UML-based models with behaviour expressed as statecharts
• Validate models using snapshots based on object diagrams. Synthesise implementations from UML-based designs.
• Explain the software engineering notion of design patterns and identify relevant design patterns

Tools Used:Could be any UML modelling tools like draw.io/ MS Vision/ Lucid chart.

Credit Hours: 20

Introduction to Data Storage concepts for Analytics workloads, Ethical and Legislative dimensions relevant to data security and information privacy when storing and accessing data, Introduction to MLOps theories and method, Machine learning project lifecycle, Introducing Machine Learning pipelines in productionFrom the laboratory to the real world, Deploying Machine Learning models in production, Monitoring Machine Learning in production, Exploring the ethical and legal issues related to the wider societal impacts that can arise from producing and leveraging data-driven decision-making systems based on Artificial Intelligence and Machine Learning techniques

Tools Used:Microsoft Azure, Google Colab and other machine learning tools mentioned above

Credit Hours: 20

Placement Pre-requisite courses based on the current entry level industry standards.

Planned : NodeJS, Linear Algebra, Mobile Programming(Flutter)

The University will work with the placement provider to ensure that they are committed to providing appropriate, subject-related work opportunities and/or experiences to support you in meeting the module’s learning outcomes. Individual assessment briefs will be provided for you at subject level, which will enable you to reflect on and demonstrate the appropriate skills, behaviours and attitudes relevant to your chosen area of professional practice. This module requires a minimum of 40 weeks of work within a professional placement setting. You will be supported by an academic supervisor and a workplace supervisor. The workplace supervisor will provide workplace support but will not mark the portfolio to preserve the workplace supervisor relationship. The academic supervisor will provide formative feedback on the developing portfolio on at least two occasions prior to submission of the summative portfolio at the end of the placement.

Credit Hours : 120

Fully connected Artificial Neural Networks , Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, Ensemble Learning, Ethics in Practical Artificial Intelligence, Carbon footprint of Artificial Intelligence, Artificial Intelligence for sustainability

Tools Used: Keras, Tensorflow, PyTorch

Credit Hours: 20

Why NoSQL, Principles, Taxonomy, Distribution Models, Consistency in Distributed Databases Key-Value Stores, practical experience with Redis, Graph databases, practical experience with Neo4J, Column-family Stores, practical experience with Cassandra., XML Databases, practical experience with MarkLogic Server, Document Databases, practical experience with MongoDB, MongoDB University- review courses and careers available, Replication and database sharding, practical experience with MongoDB, MapReduce + Hadoop, Security Considerations in Accessing NoSQL Databases, Database Administration, Basics of JavaScript and JSON, Introduction to MongoDB, Schema Design in MongoDB, Creating, Updating and Deleting Documents in MongoDB, Querying in MongoDB, Indexing in MongoDB, Aggregation in MongoDB, Replication in MongoDB Sharding in MongoDB, Database Administration in MongoDB, Installing and Configuring Apache Hadoop, Running MapReduce in Hadoop

Tools Used : MarkLogic, Redis, Cassandra, MongoDB, and Neo4j, MapReduce + Hadoop

Credit Hours: 20

On successful completion of the module, students will be able to:

1. Plan a research-informed project using appropriate literature and / or professional outputs.

2. Design an artefact using appropriate techniques and tools.

3. Implement a design to produce an artefact using appropriate techniques.

4. Critically evaluate the implementation of the artefact and the overall project.

5. Assemble and organise information to successfully communicate the results and findings of the project.

Tools Used: Depends on student's project

Credit Hours: 20

Introduction to NLP, Application and Challenges, Probability and Statistics, Morphology Analysis, Syntax Analysis, Semantic Analysis, Pragmatics, Pre-processing Techniques, Tokenisation, N-grams, Stemming and Lemmatisation, Synsets and Hypernyms, Tagging and Stop Words, Named Entity Recognition, Word and Document Representations, Vector Space Models, Regularisation, Types of Text Classification, Multiple Inputs and Text Entailment, Sentiment Analysis, Generative Models: Unigram, Bigram, Trigram and Smoothing, Discriminative Language Models, Language Models using NNs (Neural Networks), Sequence Modelling, Neural Language Models, Hidden Markov Models, Part of Speech Tagging, Sentence Structure Modelling, Context Free Grammars (CFG), Probabilistic CFG, Dependency ParsingSemantic Role Labelling, Sequence to Sequence Mapping with LSTMs, Information Extraction, Sentence-level Relation Extraction, Corpus-level Relation Extraction, Coreference, Entity Linking, Question Answering, Text Generation, Machine Translation, Text Summarisation, Textual Entailment, Reading Comprehension

Tools Used : NLTK, spaCy, Gensim, transformers, Keras, Tensorflow, Pytorch

Credit Hours: 20

On the Cloud: Computing, networking, storage, databases Automation, Load Balancing, Scaling Security, Monitoring

Tools Used: AWS

Credit Hours: 20

On successful completion of the module, students will be able to:

1. Plan a research-informed project using appropriate literature and / or professional outputs.

2. Design an artefact using appropriate techniques and tools.

3. Implement a design to produce an artefact using appropriate techniques. 4. Critically evaluate the implementation of the artefact and the overall project.

5. Assemble and organise information to successfully communicate the results and findings of the project.

Tools Used: Depends on student's project

Credit Hours: 20