The main aim of this module is to provide an introduction to skills and knowledge needed for the strategic management of information technology. It begins by exploring how innovative technologies/technology trends can be identified, assessed, and incorporated into organisational strategies. In addition to benefits, legislative, security management and governance implications of emerging technologies are addressed.

The module begins with horizon scanning, where emerging technologies/technology trends are identified and their potential impacts on businesses and organisations identified. Innovation and hype cycles, innovation systems, business/IT alignment and IT strategic planning methods are introduced as mechanisms through which emerging technologies can be identified and their value to businesses analysed. The construction of business cases, and the role of the business analyst in this process are also introduced. The management implications of social, ethical and environmental impacts of new technologies are then considered. Topics here include ethics, legislation and compliance requirements, security management and an introduction to IT governance frameworks.

1. Utilise methods enabling the alignment of IT innovations with strategic business objectives.
2. Construct a business case, providing justification for the adoption of a new technology as well as identifying potential risks.
3. Identify governance implications (e.g. legislative compliance, security) of adopting innovative information technologies.
4. Describe IT governance approaches and mechanisms in areas such as compliance and security.

The aim of this module is to provide learners with an understanding of common security vulnerabilities associated with modern software applications and the various remediation strategies associated with same. In addition to this, the module covers the usage of cryptography in application software for securing both data at rest and data in transit.

1. Analyse source code and identify the presence/absence of common security vulnerabilities.
2. Compose countermeasure solutions when faced with security vulnerabilities at the source code level.
3. Compose application security requirements as part of the Requirements Engineering and Software Design/Architecture phases of the SDLC.
4. Discuss the role of cryptography in both the secure storage and transportation of data.

The module is intended to help students to understand the necessary skills to interpret numerical and graphical information and describe data appropriately. It will introduce some basic concepts for statistical inference and utilise computer software to interpret data. Additionally, it will introduce students to the concept of Big Data.

1. Appraise Data Analytics and the emergence of big data.

2. Analyse results from data using appropriate statistical methodology.
3. Examine and implement different hypothesis testing techniques for decision making.

4. Develop computer software for the solution of statistical problems.

The aim of this module is to provide learners with the skills to simulate malicious attacks against software applications from a black-hat hacker/black-box testing perspective using industry standard ethical-hacking tools.

1. Locate and analyse relevant publicly available information with regard to the software application under test.
2. Simulate attacks on a software application from a black-box perspective using industry standard ethical hacking tools.
3. Evaluate automated testing tool results with respect to business impact and false positives.
4. Compose exploits and countermeasures in response to vulnerabilities identified.

This will focus on working with database objects such as triggers, user-defined functions. It will further examine storing and manipulating non-relational data objects such as xml (producing data, schemas and schematrons), JSON and other non-relational objects such as Spatial and Hierarchical data. How to model, develop and manipulate graph tables will also be covered.

1. Design and create database triggers
2. Design and create user defined functions
3. Create and manipulate XML objects
4. Store and manipulate other non relational data objects
5. Model, create and manipulate complex data relationships using graph tables

The module is intended to help students understand the range of techniques deploying in Machine/Deep learning environments. It will introduce students to neural networks, training sets and how to study the input/output of a machine learning system.

1. Obtain an understanding of machine learning approaches and neural networks.
2. Examine and distinguish the areas of supervised and unsupervised machine learning.
3. Discuss the relationships between training set, test set, generalisation, cross validation.
4. Utilise data mining and appropriate machine learning techniques to solve real problems.

Academy Cloud Architecting covers the core requirements of building IT infrastructure on Amazon Web Services, or AWS. The course is designed to teach solutions architects how to optimise the use of the AWS Cloud by understanding AWS services and how these services fit into cloud-based solutions.

1. Evaluate the different networking concepts in cloud computing architectures

2. Implement appropriate AWS services to ensure high availability on a web scale

3. Describe the need for elasticity, appropriate use of management tools and automation on AWS

4. Describe the need for a well-architected Framework in pertinent areas of cloud computing

5. Apply best practice for deployment, implementation and troubleshooting of sample architectures