MSc Information Technology Management

This module on cybersecurity practices provides students with practical knowledge and hands-on experience in cybersecurity practices. The module covers the implementation of security measures, threat detection and mitigation, incident response, and best practices for protecting information assets. The module aims to equip students with technical skills required to safeguard organisational information systems against cyber threats.

This module provides students with best practices for securing information systems, develop skills in detecting, analysing and mitigating cyber threats, and familiarise students with real-world cybersecurity challenges and solutions.

Competences

At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• a) Be able to use and configure cybersecurity tools and technologies together with competence in performing security assessments and penetration testing.
• b) Be able to analyse and interpret security data to identify threats and vulnerabilities and apply critical thinking to develop effective cybersecurity strategies.
• c) Show how to identify and assess cybersecurity risks and implement risk mitigation strategies and security controls.
• d) Be responsible for ensuring adherence to cybersecurity laws and regulations together with applying ethical principles in cybersecurity practices.

Knowledge

At the end of the module/unit the learner will have been exposed to the following:

• a) Cybersecurity concepts and terminology together with knowledge of common cyber threats, vunerabilities and attacke vectors.
• b) Firewalls, intrusion detection/prevention systems, antivirus software and encryption methods.
• c) Incident response procedures and best practices, and encryption technologies and secure data storage methods.
• d) Cybersecurity laws and regualtions, and compliance requirements and standards in cybersecurity.

Skills

At the end of the module/unit the learner will have acquired the following skills:

• a) How to use cybersecurity tools such as Nessus, Metasploit and Wireshark togther with skills in configuring and managing security technologies.
• b) How to conduct vulnerability assessments and interpret results, and perform penetration tests and report on findings.
• c) Demonstrate how to implement network security measures and monitor network traffic as well as securing network devices and managing network policies.
• d) Apply problem-solving techniques to address cybersecurity challenges and develop innovative solutions to enhance security.
• e) Demonstrate ethical behaviour in cybersecurity practices and understand and apply ethical hacking principles.

Module-Specific Learner Skills

At the end of the module/unit the learner will be able to:

• a) Demonstrate high level and effective verbal, non-verbal, presentational and written communication skills.
• b) Show critical thinking skills through evaluation of concepts, theories and parctices related to cybersecurity practices.
• c) Demonstrate a critical understanding of cybersecurity threats and threat detection and approaches to threat mitigation.
• d) Critically analyse cybersecurity case studies identifying lessons learned and best practices.
• e) Discuss cybersecurity laws and regulations as well as the application of ethical principls.
• f) Access, critically evaluate and apply evidence and information from a variety of sources, including published sources, to analyse case studies in cybersecurity practices.

Module-Specific Digital Skills and Competences

At the end of the module/unit, the learner will be able to:

• a) Demonstrate high level information literacy and information technology skills, including the effective use of artificial intelligence (AI).
• b) Work collaboratively and in teams with peers of group discussions, group tasks, etc. related to cybersecurity practices.
• c) Demonstrate a critical understanding and use of cybersecurity tools and technologies.
• d) Conduct vulnerability assessments using tools such as Nessus and OpenVAS and penetration tools such as Metasploit and Kali Linux.
• e) Demonstrate application of web and application security using, for example, SQLinjection and XSS.
• f) Demonstrate and practice self-reflection of knowledge, understanding and skills related to cybersecurity practices.
• g) Apply digital skills to make effective use of the internet and how to find and access resources and information related to cybersecurity practices.

Formative assessment

Formative assessment support will help students to understand the summative assessment requirement and to improve their confidence in different types of summative assessment. Students will be given clear assessment criteria as part of the assignment brief and will be required to submit substantive formative assessments prior to summative assessments. The formative opportunities are scheduled in such a way as to allow students to reflect on any tutor feedback and feed forward prior to the summative event. The formative feedforward given by the tutor will relate specifically to how students have gone about the learning process, how they have acquired knowledge, skills and their understanding of how to apply their knowledge.

Summative assessment

Summative assessment is the final marked work that will be required to be submitted to a set deadline. This type of assessment is a graded piece of work. Students will receive an assignment brief at the beginning of the module that will clearly state the requirements of the assessment task and the assessment criteria used to mark their work. In preparing for an assessment for submission students must read the brief and the assessment criteria carefully to make sure that it is fully understood. The assessment criteria rubric will illustrate how the final mark has been awarded.

Summative assessment will consist of two components. First, provide a project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements (75%). Second, a 1,000 word essay discussing to importance of leadership, teambuilding and communication (25%).

Overview of learning outcomes mapped against summative assessment components (letters in brackets refer to competences, knowledge and skills given in the module under ‘Learning Outcomes’).

Project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements.

(a), (b), (c), (d) (a), (b) (d), (e), (f) (a), (b), (c), (d), (f)

Essay discussing to importance of leadership, team building and communication.

(e),(f) (d), (e), (f) (c), (e)

This module on incident response management provides students with knowledge, critical understanding and skills in managing cybersecurity incidents. The module covers the principles, strategies, and tools required to effectively respond to and manage cybersecurity incidents. Students will learn how to develop incident response plans, identify and analyse incidents, and implement recovery procedures. The module aims to prepare students to handle real-world cybersecurity incidents and mitigate their impact on organisations.

Competences

At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• a) Be able to develop and implement incident response plans and create policies and procedures for effective incident response management.
• b) Show proficiency in identifying and analysing cybersecurity threats and incidents using tools and techniques for threat detection and analysis.
• c) Be able to manage the incident response lifecycle from detection to recovery and coordinate incident response efforts across teams and stakeholders.
• d) Be resonsible for conducting forensic investigations to preserve and analyse evidence.
• e) Be able to communicate incident response activities and findings to stakeholders and coordinate with legal compliance and public relations teams during incidents.

Knowledge

At the end of the module/unit the learner will have been exposed to the following:

• a) Industry-standard response frameworks such as NIST and SANS, and the incident response lifecycle.
• b) Common cybersecurity threats (for example, malware, ransomware, phishing) and vulnerabilities, and understanding how these threats expolit vulnerabilities.
• c) Tools and techniques for detecting and analysing cybersecurity incidents (for example, SEIM, IDS/IPS, network traffic analysis) and how to use these tools.
• d) Principles, techniques and tools of digital forensics and handling procedures to ensure legal admissibility.
• e) Legal and regulatory requirements rerlated to cybersecurity incident management incuding compliance and ethical considerations.
• f) Risk management frameworks and practices and how to assess and mitigate risk.

Skills

At the end of the module/unit the learner will have acquired the following skills:

• a) How to develop and maintain effective incident response plans and conduct regular reviews and updates to incident response strategies.
• b) Demonstrate the use of cybersecurity tools to detect and analyse security incidents and interpret data from various sources.
• c) How to conduct digital forensic investigations to collect and preserve evidence and analyse forensic data to determine the cause and extent of security incidents.
• d) How to lead and coordinate incident response efforts during a crisis and manage communications internally and externally.
• e) Demonstrate how to conduct post-incident reviews to identify lessons learned and implement improvements to incident response policies and processes.
• f) Demonstrate adherence to ethical standards and legal requirements and apply relevant laws and regulations.

Module-Specific Learner Skills

At the end of the module/unit the learner will be able to

• a) Demonstrate high level and effective verbal, non-verbal, presentational and written communication skills.
• b) Show critical thinking skills through evaluation of concepts, theories and parctices related to incident response management.
• c) Demonstrate understanding and use of incident response frameworks, incident detection and incident analysis.
• d) Plan responses to incidents using appropriate policies and procedures, and updating incident response plans.
• e) Communicate with internal and external stakeholders during an incident and ensuring legal compliance.
• f) Conduct post-incident reviews and lessons learned.
• g) Access, critically evaluate and apply evidence and information from a variety of sources, including published sources, to analyse case studies in incident response management.

Module-Specific Digital Skills and Competences

At the end of the module/unit, the learner will be able to:

• a) Demonstrate high level information literacy and information technology skills, including the effective use of artificial intelligence (AI).
• b) Work collaboratively and in teams with peers of group discussions, group tasks, etc. related to incident response management.
• c) Use tools and techniques for detecting cybersecurity incidents and analyse incident data.
• d) Demonstrate use of tools and techniques for conduction forensic investigations.
• e) Demonstrate and practice self-reflection of knowledge, understanding and skills related to incident response management.
• f) Apply digital skills to make effective use of the internet and how to find and access resources and information related to incident response management.

Formative assessment

Formative assessment support will help students to understand the summative assessment requirement and to improve their confidence in different types of summative assessment. Students will be given clear assessment criteria as part of the assignment brief and will be required to submit substantive formative assessments prior to summative assessments. The formative opportunities are scheduled in such a way as to allow students to reflect on any tutor feedback and feed forward prior to the summative event. The formative feedforward given by the tutor will relate specifically to how students have gone about the learning process, how they have acquired knowledge, skills and their understanding of how to apply their knowledge.

Summative assessment

Summative assessment is the final marked work that will be required to be submitted to a set deadline. This type of assessment is a graded piece of work. Students will receive an assignment brief at the beginning of the module that will clearly state the requirements of the assessment task and the assessment criteria used to mark their work. In preparing for an assessment for submission students must read the brief and the assessment criteria carefully to make sure that it is fully understood. The assessment criteria rubric will illustrate how the final mark has been awarded.

Summative assessment will consist of two components. First, provide a project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements (75%). Second, a 1,000 word essay discussing to importance of leadership, teambuilding and communication (25%).

Overview of learning outcomes mapped against summative assessment components (letters in brackets refer to competences, knowledge and skills given in the module under ‘Learning Outcomes’).

Project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements.

(a), (b), (c), (d) (a), (b) (d), (e), (f) (a), (b), (c), (d), (f)

Essay discussing to importance of leadership, team building and communication.

(e),(f) (d), (e), (f) (c), (e)

This module on contemporary topics in data analytics explores advanced and emerging topics in data analytics, providing students with an understanding of the latest trends, techniques, and applications in the field. This module covers contemporary topics such as big data analytics, machine learning, deep learning, and the ethical implications of data use. The module aims to equip students with cutting-edge skills and knowledge to tackle complex data challenges in various industries.

Competences

At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• a) Be able to apply statistical and machine learning techniques to analyse data sets using data analytic tools and technologies effectively.
• b) Show proficiency in managing and processing data sets using big data technologies together with designing and implementaing data solutions.
• c) Be able to apply emerging data analytic technologies and methodologies in the context of existing data analytics frameworks.
• d) Be able to identify ethical implications of data analytics
• e) Show competence in ensuring data privacy, security and compliance with regulations.

Knowledge

At the end of the module/unit the learner will have been exposed to the following:

• a) Statistical techniques for data analysis including regression, classification, clustering and dimensionality reduction.
• b) Machine learning algorithms and their applications including supervised, unsupervised and reinforcement learning.
• c) Deep learning architectures such as convolution neural networks (CNNs) and recurrent neural netwroks (RNNs).
• d) Big technologies including data frameworks such as Hadoop and Spark together with distributed computing and data processing techniques.
• e) Neural language processing (NLP) and applications in text mining and sentiment analysis.
• f) Tools for creating interactive and insightful visualisations.
• g) Ethical considerations and legal regulations related to data analytics.

Skills

At the end of the module/unit the learner will have acquired the following skills:

• a) How to use programming languages commonly used in data analytics such as Python and R.
• b) How to use data analytics and maching learning libraries, for example, TensorFlow, PyTorch and scikit-learn.
• c) Demonstrate data pre-processing, cleaning and transformation and how to handle large data sets.
• d) How to create effective data visualisations using tools such as Tableau, Power BI and D3.js.
• e) How to identify and apply innovative solutions to data analytics challenges.
• f) Show understanding and application of ethical, data privacy and security in data anlaytic projects.

Module-Specific Learner Skills

At the end of the module/unit the learner will be able to

• a) Demonstrate high level and effective verbal, non-verbal, presentational and written communication skills.
• b) Show critical thinking skills through evaluation of concepts, theories and parctices elated to contemporary topics in data analytics.
• c) Demonstrate critical understanding of advanced data analytic topics and big data analytics
• d) Critically evaluate current and potential future developments in machine and deep learning.
• e) Show a critical appreciation of key concepts and techniques in natural language processing.
• d) Access, critically evaluate and apply evidence and information from a variety of sources, including published sources, to analyse case studies in contemporary topics in data analytics.

Module-Specific Digital Skills and Competences

At the end of the module/unit, the learner will be able to:

• a) Demonstrate high level information literacy and information technology skills, including the effective use of artificial intelligence (AI).
• b) Work collaboratively and in teams with peers of group discussions, group tasks, etc. related to incident response management.
• c) Use tools and techniques for detecting cybersecurity incidents and analyse incident data.
• d) Demonstrate use of tools and techniques for conduction forensic investigations.
• e) Demonstrate and practice self-reflection of knowledge, understanding and skills related to incident response management.
• f) Apply digital skills to make effective use of the internet and how to find and access resources and information related to incident response management.

Formative assessment

Formative assessment support will help students to understand the summative assessment requirement and to improve their confidence in different types of summative assessment. Students will be given clear assessment criteria as part of the assignment brief and will be required to submit substantive formative assessments prior to summative assessments. The formative opportunities are scheduled in such a way as to allow students to reflect on any tutor feedback and feed forward prior to the summative event. The formative feedforward given by the tutor will relate specifically to how students have gone about the learning process, how they have acquired knowledge, skills and their understanding of how to apply their knowledge.

Summative assessment

Summative assessment is the final marked work that will be required to be submitted to a set deadline. This type of assessment is a graded piece of work. Students will receive an assignment brief at the beginning of the module that will clearly state the requirements of the assessment task and the assessment criteria used to mark their work. In preparing for an assessment for submission students must read the brief and the assessment criteria carefully to make sure that it is fully understood. The assessment criteria rubric will illustrate how the final mark has been awarded.

Summative assessment will consist of two components. First, provide a project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements (75%). Second, a 1,000 word essay discussing to importance of leadership, teambuilding and communication (25%).

Overview of learning outcomes mapped against summative assessment components (letters in brackets refer to competences, knowledge and skills given in the module under ‘Learning Outcomes’).

Project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements.

(a), (b), (c), (d) (a), (b) (d), (e), (f) (a), (b), (c), (d), (f)

Essay discussing to importance of leadership, team building and communication.

(e),(f) (d), (e), (f) (c), (e)

This module on software development and software management provides students with a critical understanding of software development and software management practices specifically tailored for data analytics projects. This module covers the software development lifecycle, with a focus on building scalable and efficient data analytics applications. The module also addresses project management techniques, software quality assurance, and the use of modern development tools and environments in the context of data analytics.

Competences

At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• a) Be able to design, implement and maintain software systems in the context of data analytics.
• b) Be able to develop scalable and efficient software solutions that handle large data sets and complex processing tasks.
• c) Show ability to implement robust testing and quality assurance processes for data analytic applications.
• d) Show competence in ensuring the reliability, accuracy and performance of software systems.
• e) Show competence in data integration (ETL) processes and data pipeline development.
• f) Be able to comply with ethical and regulatory standards related to software development and software management.

Knowledge

At the end of the module/unit the learner will have been exposed to the following:

• a) The phases of the software development lifecycle and knowledge of software development methodoligies, for example, Agile, Scrum and DevOps.
• b) Programming languages commonly used in data analytics, for example, Python, R and SQL and software design principles.
• c) Data analytic techniques and how to apply these to software solutions.
• d) Data management practices and big data technologies, for example, Hadoop, Spark and database management systems and data warehousing.
• e) Continuous implementation and continuous deployment and tools, for example, Jenkins, GitLab CI, and how to implement pipelines.
• f) Software testing methodologies and tools including software quality models and standards, for example, SO/IEC 25010.

Skills

At the end of the module/unit the learner will have acquired the following skills:

• a) Demonstrate proficiency in using development environments and version control (for example, Git).
• b) Show skills in writing clean, maintainable and efficient code for data analytic applications.
• c) How to integrate diverse data sources and ensure data quality.
• d) How to develop and execute test plans, test cases and automated tests together with the ability to debug and resolve issues in data analytic software.
• e) Demonstrate skills in collaborating with cross-functional teams and involved specialists.
• f) Demonstrate skills in applying critical thinking to evaluate and improve software development solutions.
• g) Show how to assess and address ethical implications of software development and data analytic practices together with skills in implementing data privacy and security measures to ensure compliance with regulations.

Module-Specific Learner Skills

At the end of the module/unit the learner will be able to

• a) Demonstrate high level and effective verbal, non-verbal, presentational and written communication skills.
• b) Show critical thinking skills through evaluation of concepts, theories and practice-related to software development and software management.
• c) Demonstrate a critical evaluation of the software development cycle including requirements for analysis and design of software.
• d) Show a critical understanding of project management for data analytics.
• e) Critically appreciate quality assurance issues and standards, for example, ISO/IEC 25010, for software quality management.
• f) Access, critically evaluate and apply evidence and information from a variety of sources, including published sources, to analyse case studies in software development and software management.

Module-Specific Digital Skills and Competences

At the end of the module/unit, the learner will be able to:

• a) Demonstrate high level information literacy and information technology skills, including the effective use of artificial intelligence (AI).
• b) Work collaboratively and in teams with peers of group discussions, group tasks, etc. related to incident response management.
• c) Use tools and techniques for detecting cybersecurity incidents and analyse incident data.
• d) Demonstrate use of tools and techniques for conduction forensic investigations.
• e) Demonstrate and practice self-reflection of knowledge, understanding and skills related to incident response management.
• f) Apply digital skills to make effective use of the internet and how to find and access resources and information related to incident response management.

Formative assessment

Formative assessment support will help students to understand the summative assessment requirement and to improve their confidence in different types of summative assessment. Students will be given clear assessment criteria as part of the assignment brief and will be required to submit substantive formative assessments prior to summative assessments. The formative opportunities are scheduled in such a way as to allow students to reflect on any tutor feedback and feed forward prior to the summative event. The formative feedforward given by the tutor will relate specifically to how students have gone about the learning process, how they have acquired knowledge, skills and their understanding of how to apply their knowledge.

Summative assessment

Summative assessment is the final marked work that will be required to be submitted to a set deadline. This type of assessment is a graded piece of work. Students will receive an assignment brief at the beginning of the module that will clearly state the requirements of the assessment task and the assessment criteria used to mark their work. In preparing for an assessment for submission students must read the brief and the assessment criteria carefully to make sure that it is fully understood. The assessment criteria rubric will illustrate how the final mark has been awarded.

Summative assessment will consist of two components. First, provide a project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements (75%). Second, a 1,000 word essay discussing to importance of leadership, teambuilding and communication (25%).

Overview of learning outcomes mapped against summative assessment components (letters in brackets refer to competences, knowledge and skills given in the module under ‘Learning Outcomes’).

Project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements.

(a), (b), (c), (d) (a), (b) (d), (e), (f) (a), (b), (c), (d), (f)

Essay discussing to importance of leadership, team building and communication.

(e),(f) (d), (e), (f) (c), (e)

This module on cloud systems and visualisation provides an in-depth critical understanding of cloud computing systems and data visualisation techniques. Students will explore the architecture, deployment, and management of cloud systems, as well as methods for visualising data to derive meaningful insights. The module aims to equip students with the knowledge and skills required to implement and manage cloud solutions and effectively visualise complex data sets.

The module covers principles and architecture of cloud computing systems, deploying and managing cloud infrastructure and services, data visualisation techniques and tools for data analysis, integration of cloud computing with data visualisation to support decision-making and security, privacy and compliance aspects of cloud systems.

Competences

At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• a) Be able to design and implement scalable and resilient cloud architectures and deploy and manage cloud infrastructure and services.
• b) Be responsible for creating effective and insightful data visulualisations and using visualisation tools to analyse and represent data.
• c) Show how to integrate cloud systems with data visualisation tools for real-time data analysis.
• d) Be able to ensure security and privacy of data in cloud environments and implement compliance measures to adhere to data protection regulations.
• e) Show competence in developing solutions that apply cloud computing for enhanced data visualisations

Knowledge

At the end of the module/unit the learner will have been exposed to the following:

• a) Fundamental concepts of cloud computing including IaaS, PaaS and SaaS, and cloud services models and eployment strategies.
• b) Leading cloud platforms such as AWS, Microsoft Azure and Google Cloud, and cloud deployment tools (Terraform, Ansible).
• c) Principles and techniques of data visualisation and different types of visualisations and their appropriate applications.
• d) Data visualisation tools such as Tableau, Power BI and D3.js and how to create interactive and dynamic visualisations.
• e) Security challenges in cloud environments and best practices for securing cloud infrastructure and data.
• f) Data privacy regulations and compliance requirements, and implementing complinace measures in cloud systems.

Skills

At the end of the module/unit the learner will have acquired the following skills:

• a) How to deploy and manage cloud infrastructure using platforms such as AWS, Azure and Google Cloud together with how to use automation tools to streamline cloud deployment and management processes.
• b) Demonstrate creation of effective and visually appealing data visualisations and development of custom visualisations using programming libraries.
• c) How to integrate cloud data sources with visualisation tools for real-time data analysis.
• d) Demonstrate the application of security measures to protect data in cloud environments and conduct security assesssments and implementation of apporpriate controls.
• f) Apply critical thinking skills to evaluate the effectiveness of cloud and visualisation strategies.

Module-Specific Learner Skills

At the end of the module/unit the learner will be able to

• a) Demonstrate high level and effective verbal, non-verbal, presentational and written communication skills.
• b) Show critical thinking skills through evaluation of concepts, theories and parctices elated to contemporary topics in data analytics.
• c) Demonstrate critical understanding of advanced data analytic topics and big data analytics
• d) Critically evaluate current and potential future developments in machine and deep learning.
• e) Show a critical appreciation of key concepts and techniques in natural language processing.
• d) Access, critically evaluate and apply evidence and information from a variety of sources, including published sources, to analyse case studies in contemporary topics in data analytics.

Module-Specific Digital Skills and Competences

At the end of the module/unit, the learner will be able to:

• a) Demonstrate high level information literacy and information technology skills, including the effective use of artificial intelligence (AI).
• b) Work collaboratively and in teams with peers of group discussions, group tasks, etc. related to cloud systems and visualisation.
• c) Use and evaluate leading cloud platforms, for example, AWS, Azure, Google Cloud and how to monitor and manage cloud resources.
• d) Use and apply data visualisation tools such as Poer BI and D3.js including creating interactive dashboards.
• e) Connect cloud data sources to visualisation tools including real-time data processing and visualisation.
• c) Demonstrate and practice self-reflection of knowledge, understanding and skills related to cloud systems and visualisation.
• d) Apply digital skills to make effective use of the internet and how to find and access resources and information related to cloud systems and visualisation.

Formative assessment

Formative assessment support will help students to understand the summative assessment requirement and to improve their confidence in different types of summative assessment. Students will be given clear assessment criteria as part of the assignment brief and will be required to submit substantive formative assessments prior to summative assessments. The formative opportunities are scheduled in such a way as to allow students to reflect on any tutor feedback and feed forward prior to the summative event. The formative feedforward given by the tutor will relate specifically to how students have gone about the learning process, how they have acquired knowledge, skills and their understanding of how to apply their knowledge.

Summative assessment

Summative assessment is the final marked work that will be required to be submitted to a set deadline. This type of assessment is a graded piece of work. Students will receive an assignment brief at the beginning of the module that will clearly state the requirements of the assessment task and the assessment criteria used to mark their work. In preparing for an assessment for submission students must read the brief and the assessment criteria carefully to make sure that it is fully understood. The assessment criteria rubric will illustrate how the final mark has been awarded.

Summative assessment will consist of two components. First, provide a project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements (75%). Second, a 1,000 word essay discussing to importance of leadership, teambuilding and communication (25%).

Overview of learning outcomes mapped against summative assessment components (letters in brackets refer to competences, knowledge and skills given in the module under ‘Learning Outcomes’).

Project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements.

(a), (b), (c), (d) (a), (b) (d), (e), (f) (a), (b), (c), (d), (f)

Essay discussing to importance of leadership, team building and communication.

(e),(f) (d), (e), (f) (c), (e)

This module on DevOps (Development and Operations) provides students with an in-depth understanding of DevOps principles, practices, and tools. The module focuses on the integration of development and operations to improve collaboration, productivity, and the continuous delivery of high-quality software. Students will learn about automation, infrastructure as code, continuous integration and continuous deployment (CI/CD), and monitoring and logging in a DevOps environment. Students will also cover cultural and organisational aspects of DevOps.

Competences

At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• a) Be able to design, implement and manage continuous integration and continuous deployment pipelines and competence in automating the software development and deployment process.
• b) Show proficiency in using infrastructure as code (IaC) tools to automate infrastructure provisioning and management and maintain version control infrastructure configurations.
• c) Be able to set up and manage monitoring and logging systems to ensure application performance and reliability.
• d) Be able to integrate security practices into the Dev Ops pipieline and ensure compliance with security standards.
• e) Show how to foster a collaborative environment between development and operations teams.

Knowledge

At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• a) Be able to design, implement and manage continuous integration and continuous deployment pipelines and competence in automating the software development and deployment process.
• b) Show proficiency in using infrastructure as code (IaC) tools to automate infrastructure provisioning and management and maintain version control infrastructure configurations.
• c) Be able to set up and manage monitoring and logging systems to ensure application performance and reliability.
• d) Be able to integrate security practices into the Dev Ops pipieline and ensure compliance with security standards.
• e) Show how to foster a collaborative environment between development and operations teams.

Skills

At the end of the module/unit the learner will have acquired the following skills:

• a) How to build and maintain CI/CD pipelines to automate the software release process ensuring reliability and efficiency of the deployment process.
• b) Apply IaC tools to automate the provisioning and configuration of infrastructure and how to manage infrastructure as code to ensure consistency and scalability.
• c) Demonstrate how to create and manage Docker containers for application deployment and how to use Kubernetes to orchestrate and scale containerised applications.
• d) How to set up monitoring systems to track application performance and analyse logs and metrics to resolve issues.
• e) Demonstrate how to analyse complex problems and develop effective solutions.
• f) Apply critical thinking to optimise DevOps practices and processes.

Module-Specific Learner Skills

At the end of the module/unit the learner will be able to

• a) Demonstrate high level and effective verbal, non-verbal, presentational and written communication skills.
• b) Show critical thinking skills through evaluation of concepts, theories and parctices related to DevOps.
• c) Critically evaluate principles and practices and goals with respect to DevOps.
• d) Appreciate the importance of and apply DevOps culture and how to enhance collaboration between development and operations teams.
• e) Critically evaluate case studies of DevOps implementations and industry standard best practices.
• f) Access, critically evaluate and apply evidence and information from a variety of sources, including published sources, to analyse case studies in DevOps.

Module-Specific Digital Skills and Competences

At the end of the module/unit, the learner will be able to:

• a) Demonstrate high level information literacy and information technology skills, including the effective use of artificial intelligence (AI).
• b) Work collaboratively and in teams with peers of group discussions, group tasks, etc. related to DevOps.
• c) Appreciate how to use tools for automation and infrastructue as code including Terraform, Ansible, Chef and Puppet.
• d) Appreciate how to use tools for monitoring and logging including Prometheus, Grafana and ELK Stack.
• e) Critically understand the importance of security in DevOps and the use of tools for security and compliance including Aqua, Twistlock and Clair.
• f) Demonstrate and practice self-reflection of knowledge, understanding and skills related toDevOps.
• g) Apply digital skills to make effective use of the internet and how to find and access resources and information related to DevOps.

Formative assessment

Formative assessment support will help students to understand the summative assessment requirement and to improve their confidence in different types of summative assessment. Students will be given clear assessment criteria as part of the assignment brief and will be required to submit substantive formative assessments prior to summative assessments. The formative opportunities are scheduled in such a way as to allow students to reflect on any tutor feedback and feed forward prior to the summative event. The formative feedforward given by the tutor will relate specifically to how students have gone about the learning process, how they have acquired knowledge, skills and their understanding of how to apply their knowledge.

Summative assessment

Summative assessment is the final marked work that will be required to be submitted to a set deadline. This type of assessment is a graded piece of work. Students will receive an assignment brief at the beginning of the module that will clearly state the requirements of the assessment task and the assessment criteria used to mark their work. In preparing for an assessment for submission students must read the brief and the assessment criteria carefully to make sure that it is fully understood. The assessment criteria rubric will illustrate how the final mark has been awarded.

Summative assessment will consist of two components. First, provide a project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements (75%). Second, a 1,000 word essay discussing to importance of leadership, teambuilding and communication (25%).

Overview of learning outcomes mapped against summative assessment components (letters in brackets refer to competences, knowledge and skills given in the module under ‘Learning Outcomes’).

Project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements.

(a), (b), (c), (d) (a), (b) (d), (e), (f) (a), (b), (c), (d), (f)

Essay discussing to importance of leadership, team building and communication.

(e),(f) (d), (e), (f) (c), (e)

This module in artificial intelligence and machine learning provides students with an in-depth understanding of artificial intelligence (AI) and machine learning (ML) principles, techniques, and applications. The module covers foundational concepts, modern algorithms, and practical implementation of AI and ML in various domains. The module aims to equip students with the knowledge and skills necessary to develop and apply AI and ML solutions to real-world problems. Students will study the fundamental concepts and techniques of AI and ML, develop skills in designing, implementing and evaluating AI and ML, explore applications, learn how to use popular AI and ML frameworks and tools, and discuss ethical and societal implications of these technologies.

Competences

At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• a) Be able to identify and addressing complex problems using AI and machine learning.
• b) Be able to deploy AI and machine learning solutions to real-world applications.
• c) Be able to take into account ethical implications of AI and machine learning technologies
• d) Show competence in developing fair and unbiased AI and machine learning systems.

Knowledge

At the end of the module/unit the learner will have been exposed to the following:

• a) Principles and theories of AI and machine learning together with knowledge of the history and evolution of AI and machine learning.
• b) Various supervised learning alogorithms and unsupervised learning techniques as well as reinforcement learning neural networks.
• c) Architecture and functioning of deep learning models and topics in deep learning, for example, transfer learning, neural architecture search.
• d) Techniques for data cleaning, normalisation and transformation; feature selection and extraction methods.
• e) Performance metrics for evaluationg AI and machine learning models and cross-validation techniques and model selection criteria.
• f) Application of AI and machine learning in various industries and AI techniques for natural lnaguage processing, computer vision and robotics.
• g) Ethical issues realted to AI and machine learning and societal impact of AI technologies and regulatory considerations.

Skills

At the end of the module/unit the learner will have acquired the following skills:

• a) Proficiency in programming languages commonly used in AI and machine learning (for example, Python, R) together with skills in debugging AI and ML algorithms.
• b) How to analyse data sets together with skills in creating visualisations to communicate data effectively.
• c) How to develop and train AI and ML models using appropriate algorithms and frameworks together with fine-tuning model parameters to optimise preformance.
• d) Apply critical thinking to identify and solve complex problems using AL and ML and evaluate different approaches.
• e) Demonstrate effective communication of technical concepts and findings to diverse audiences.
• f) How to plan and manage AI and ML projects to ensure timely delivery.

Module-Specific Learner Skills

At the end of the module/unit the learner will be able to

• a) Demonstrate high level and effective verbal, non-verbal, presentational and written communication skills.
• b) Show critical thinking skills through evaluation of concepts, theories and parcticesrelated to artificial intelligence and machine learning.
• c) Critically appreciate the fundamentals of machine learning including supervised learning, unsupervised learning and reinforcement learning.
• d) Understand and know how to use AI and machine learning algorithms, including linear regression neural networks and other algorithms.
• e) Critcially discuss ethical and societal implications of AI.
• f) Access, critically evaluate and apply evidence and information from a variety of sources, including published sources, to analyse case studies in artificial intelligence and machine learning.

Module-Specific Digital Skills and Competences

At the end of the module/unit, the learner will be able to:

• a) Demonstrate high level information literacy and information technology skills, including the effective use of artificial intelligence (AI).
• b) Work collaboratively and in teams with peers of group discussions, group tasks, etc. related to artificial intelligence and machine learning.
• c) Undersatnd and know how to use AI and machine learning tools and frameworks such as TensorFlow, Keras, Py Torch and Scikit-learn.
• d) Use AI algorithms such as leanear regressions, logistic reasoning, decision tree and support vectors.
• e) Know how to use and apply neural networks and deep learning in AI together with ensemble methods.
• f) Demonstrate and practice self-reflection of knowledge, understanding and skills related to artificial intelligence and machine learning.
• g) Apply digital skills to make effective use of the internet and how to find and access resources and information related to artificial intelligence and machine learning.

Formative assessment

Formative assessment support will help students to understand the summative assessment requirement and to improve their confidence in different types of summative assessment. Students will be given clear assessment criteria as part of the assignment brief and will be required to submit substantive formative assessments prior to summative assessments. The formative opportunities are scheduled in such a way as to allow students to reflect on any tutor feedback and feed forward prior to the summative event. The formative feedforward given by the tutor will relate specifically to how students have gone about the learning process, how they have acquired knowledge, skills and their understanding of how to apply their knowledge.

Summative assessment

Summative assessment is the final marked work that will be required to be submitted to a set deadline. This type of assessment is a graded piece of work. Students will receive an assignment brief at the beginning of the module that will clearly state the requirements of the assessment task and the assessment criteria used to mark their work. In preparing for an assessment for submission students must read the brief and the assessment criteria carefully to make sure that it is fully understood. The assessment criteria rubric will illustrate how the final mark has been awarded.

Summative assessment will consist of two components. First, provide a project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements (75%). Second, a 1,000 word essay discussing to importance of leadership, teambuilding and communication (25%).

Overview of learning outcomes mapped against summative assessment components (letters in brackets refer to competences, knowledge and skills given in the module under ‘Learning Outcomes’).

Project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements.

(a), (b), (c), (d) (a), (b) (d), (e), (f) (a), (b), (c), (d), (f)

Essay discussing to importance of leadership, team building and communication.

(e),(f) (d), (e), (f) (c), (e)

This module provides students with a comprehensive understanding of business intelligence (BI) principles, tools, and techniques. The module covers the collection, integration, analysis, and presentation of business data to support decision-making processes. The module aims to equip students with the skills necessary to transform data into actionable insights, improving business performance and strategy. Students will study the fundamental concepts and importance of business intelligence in organisations, develop skills in using business intelligence tools and techniques for data analysis and reporting, learn how to design and implement business intelligence solutions that support business decision-making, explore various data visualisation methods for present effective business insights, and appreciate ethical and privacy issues related to business intelligence.

Competences

At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

• a) Be able to analyse complex data sets and derive actionable insights together with competence in using statistical methods and data mining techniques.
• b) Show proficiency in using business intelleignece tools such as Tableau, Power BI, etc.
• c) Be able to integrate business intelligence tools with various data sources and systems.
• d) Be able to create effective and insightful visualisations, including dashboards, to communicate data findings and support business decision-making.
• e) Show competence in knowing how to manage business intelligence projects from inception to completion.
• f) Be able to apply ethical considerations and comply with data privacy laws and regulations.

Knowledge

At the end of the module/unit the learner will have been exposed to the following:

• a) Components of business intelligence systems and how they interact.
• b) Data warehousing concepts and architectures and Extract, Transform and Load (ETL) processes and tools.
• c) Leading business intelligence tools and their functionalities, including data integration and data management techniques.
• d) Statistical analysis, data mining and machine learning techniques and their application to real-world business problems.
• e) Best practices for data visualisation and dashboard design.
• f) Application of business intelligence to various business domains such as marketing, finance, operations and human resources.
• g) Ethics and privacy in business intelligence.

Skills

At the end of the module/unit the learner will have acquired the following skills:

• a) How to use SQL for data querying and manipulation together with skills using programming languages such as Python or R for data analysis.
• b) How to apply statistical and machine learning techniques to analyse data and use data mining tools to detect patterns and trends.
• c) How to create effective data visulaisations and interactive dashboards.
• d) Demonstrate an ability to translate business intelligence insights into strategic business action plans.
• e) Show skills in planning, executing and managing business intelligence projects together with managing timelines, resources and deliverables.
• f) Demonstrate ability to assess ethical implications of business intelligence practices.

Module-Specific Learner Skills

At the end of the module/unit the learner will be able to

• a) Demonstrate high level and effective verbal, non-verbal, presentational and written communication skills.
• b) Show critical thinking skills through evaluation of concepts, theories and parctices related to business intelligence.
• c) Critically appreciate the definition and scope of business intelligence together with its role in modern business organisations.
• d) Demonstrate critical understanding of data warehousing and ETL processes as well as how to design and manage data warehouses.
• e) Understand and critically discuss ethical and privacy issues in business intelligence.
• f) Access, critically evaluate and apply evidence and information from a variety of sources, including published sources, to analyse case studies in business intelligence.

Module-Specific Digital Skills and Competences

At the end of the module/unit, the learner will be able to:

• a) Demonstrate high level information literacy and information technology skills, including the effective use of artificial intelligence (AI).
• b) Work collaboratively and in teams with peers of group discussions, group tasks, etc. related to business intelligence.
• c) Use and apply business intelligence tools such as Power BI, Tableau and QLIKView.
• d) Evaluate and apply business intelligence techniques such as predictive analytics and forecasting, machine learning applications and business intelligence roadmaps.
• c) Demonstrate and practice self-reflection of knowledge, understanding and skills related to business intelligence.
• d) Apply digital skills to make effective use of the internet and how to find and access resources and information related to business intellience.

Formative assessment

Formative assessment support will help students to understand the summative assessment requirement and to improve their confidence in different types of summative assessment. Students will be given clear assessment criteria as part of the assignment brief and will be required to submit substantive formative assessments prior to summative assessments. The formative opportunities are scheduled in such a way as to allow students to reflect on any tutor feedback and feed forward prior to the summative event. The formative feedforward given by the tutor will relate specifically to how students have gone about the learning process, how they have acquired knowledge, skills and their understanding of how to apply their knowledge.

Summative assessment

Summative assessment is the final marked work that will be required to be submitted to a set deadline. This type of assessment is a graded piece of work. Students will receive an assignment brief at the beginning of the module that will clearly state the requirements of the assessment task and the assessment criteria used to mark their work. In preparing for an assessment for submission students must read the brief and the assessment criteria carefully to make sure that it is fully understood. The assessment criteria rubric will illustrate how the final mark has been awarded.

Summative assessment will consist of two components. First, provide a project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements (75%). Second, a 1,000 word essay discussing to importance of leadership, teambuilding and communication (25%).

Overview of learning outcomes mapped against summative assessment components (letters in brackets refer to competences, knowledge and skills given in the module under ‘Learning Outcomes’).

Project plan for a given business improvement scenario to include project development, planning, implementation and closure and other project requirements.

(a), (b), (c), (d) (a), (b) (d), (e), (f) (a), (b), (c), (d), (f)

Essay discussing to importance of leadership, team building and communication.

(e),(f) (d), (e), (f) (c), (e)