In this module the student will examine the importance of descriptive analytics to organisational decision making and key metrics that organisations use to measure performance. It will explore the area of dimensional data modelling, the Extract Transform and Load (ETL) process, and data warehousing, as enablers to perform descriptive analytics. The module will provide the student with the skills to prepare, query, aggregate and analyse data. Additionally, it will cover fundamental design principles and techniques for creating meaningful visualisations. At the end of this module, the student should be able to collect, process and query data, create interactive visualisations and demonstrate how it provides insight into managerial decision making.

1. Critically discuss the notion of business intelligence and analytics and its relationship in supporting business decision making.
2. Analyse typical needs of business sectors to select appropriate Key Performance Indicators (KPIs).
3. Critically appraise dimensional modelling techniques for data analysis in a data warehouse
4. Examine and develop structures and strategies to prepare and query data for analytics and reporting.
5. Design and create data visualisations based on data visualisation theory and principles.
6. Develop competency in the use of an industry standard tool to prepare and present effective data visualisations, dashboards and reports for various user needs.

This module will introduce the student to the area of Data Science projects, identifying the different stages of a typical project lifecycle. The student will gain practical experience in gathering, ingesting and pre-processing data for target systems using a suitable programming language. It will also expose the student to the area of data management and the importance of producing quality data for machine learning and artificial intelligent models. The student will investigate tools and techniques for data pipeline infrastructures and create test systems to evaluate their usefulness.

1. Critically reflect on different data science lifecycles and their applications.
2. Evaluate the most important data management processes and how they inter-relate to create a robust and efficient data management system
3. Apply appropriate tools and techniques for building data flow pipelines.
4. Critique a range of approaches, and tools for the Extract, Load and Transform stage of a data pipeline.
5. Critically appraise current trends and examine approaches in dealing with the preparation of data for a target AI or ML model.
6. Implement procedures which will analyse and summarise the data for further model development.

This module provides the student with a deep understanding of the mathematics which underpins data science, machine learning, and deep learning. The student will gain knowledge and understanding of the concepts and techniques of a variety of mathematical topics including linear algebra, multivariable calculus, probability theory, and statistics.

1. Create detailed solutions using appropriate mathematical language.
2. Evaluate statistics derived from data.
3. Analyse the nature of statistical inference.
4. Appraise, review and formulate a mathematically based analysis of data.
5. Examine and discuss probability and its features.
6. Apply calculus techniques

This module develops a strong foundation in machine learning techniques and their application to specific problems including regression, classification, recommendation and optimisation. The focus is on understanding machine learning algorithms and their practical implementation and use.

1. Critically appraise the performance of a learning system.
2. Summarise and evaluate relevant research into machine learning and deep learning.
3. Critically analyse and evaluate the main approaches to machine learning and show similarities and differencesbetween different approaches.
4. Develop and appraise appropriate machine learning classification, optimisation and regression problems.
5. Apply machine learning algorithms to classification, regression and optimisation problems.
6. Evaluate different machine learning algorithms.

Building from knowledge and techniques from previous modules within the course, this module has a focus on the mathematical analysis as well as the application of predictive models. This module will provide the learner with the skills required to gain an appreciation of the complexity of predictive models from the mathematical perspective as well as demonstrating their applications. The learners will strengthen knowledge and understanding of the complete analytical lifecycle from data selection, data preparation, modelling and visualising as well as evaluating and comparing the models.

1. Differentiate between Integrated Development Environments (IDEs).
2. Select an appropriate data types, operations, expressions based on the problem.
3. Identify correct scripting syntax and best practices.
4. Create scripts implementing data structures as appropriate to a provided scenario.
5. Assess the effectiveness of error handling and debugging techniques.
6. Critically appraise object-oriented concepts such as encapsulation, abstraction, inheritance and polymorphism.
7. Implement an object-oriented solution to solve a given problem.

Deep learning is an exciting new area in machine learning. Leveraging high-performance computing (HPC), it is now feasible to use advanced training techniques and create deep neural networks that can handle various types of data including tabular, images, text, audio, video as both input and output. The purpose of this course is to introduce the student to classic neural network architecture, Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), Generative Adversarial Networks (GAN), Deep Belief Networks (DBN), and reinforcement learning. The course also aims to cover the application of deep learning architectures to computer vision, time series, security, natural language processing (NLP), and data generation.

1. Compare neural networks or deep neural networks to other machine learning models.
2. Determine and choose an appropriate deep neural network as a good choice for a particular problem.
3. Develop an ability to use deep learning libraries to create and evaluate network architectures.
4. Apply existing deep learning models to real datasets.
5. Explain underlying mathematical and algorithmic principles of deep learning.
6. Critically appraise recent scientific literature in deep learning
7. Critically appraise the merits and shortcomings of deep neural network architectures on specific problems.

This module involves working on a research dissertation over an extended period. Working under the guidance of an academic supervisor, the module allows the student to develop theoretical and applied skills in using contemporary computing science techniques both from a theoretical perspective and as an applied discipline. The dissertation represents the capstone work on a topic related to their chosen programme of study.

1. Develop appropriate data collection instruments for mixed methods research and to evaluate each for their appropriateness to the research question;
2. Engage in a sustained piece of individual, academic research on a chosen topic within the field(s) specifically relevant to the course;
3. Read widely and critically reflect on a number of pieces of written research in an appropriate and thorough manner;
4. Evaluate varying methodological approaches and to adopt the necessary approaches suitable to the topic being researched;
5. Produce a dissertation that evaluates and synthesises quanti t at ive (statistical) approaches such as empirical tests on a hypothesis;
6. Produce a dissertation that evaluates and synthesises qualitative research methods such as the literature review and dissertation proposal and displays evidence of independent research skills.