NFQ Level 9, Major Award

The MSc in Actuarial Science is a full-time programme running for 12 months.

Programme Requirements

Part I
Core modules (45 credits)
ST6001 Theory of Annuities - Certain for Actuarial Science (10 credits)
ST6005 Life Contingencies for Actuarial Science (10 credits)
ST6015 Computer Analytical Techniques for Actuarial Applications (5 credits)
ST6017 Application of Stochastic Methods in Actuarial Science (5 credits)
ST6020 Actuarial Business and Financial Reporting Methods (5 credits)
ST6022 Survival Methods for Actuarial Science (5 credits)
ST6032 Stochastic Modelling Techniques (5 credits)

Elective Modules (15 credits)
Students must take all modules from either List A or List B

List A (CS1 modules):
ST6003 Probability and Mathematical Statistics for Actuarial Science (10 credits) and
ST6018 Regression and Generalised Linear Model Techniques for Actuarial Science (5 credits)

List B (CM2 modules):
ST6016 Applied Financial Risk Modelling and Analytics for Actuarial Science (5 credits) and
ST6019 Application of Computational Methods in Actuarial Science and Risk Modelling (5 credits) and
ST6023 Modelling and Risk Analysis for Actuarial Science (5 credits)

NOTE: The Choice of electives must be agreed in advance with the programme co-ordinator.

Part II
Core module (30 credits)
PA6007 Market Analysis Methods for Actuarial Science (10 credits)
ST6009 Application of Core Technical Research Methodologies in Actuarial Science (20 credits)

Examinations
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2022/2023 Book and for each module in the Book of Modules, 2022/2023.

Postgraduate Diploma in Actuarial Science
Students who obtain an overall aggregate mark of not less than 480/1200 marks (40%) in Part I, pass modules to the value of at least 50 credits and obtain a mark of not less than 30% in any remaining module, but either fail to achieve the requisite grade of 50% across Part I to progress to Part II, or do not wish to complete Part II, may opt to be exit the programme and be conferred with a Postgraduate Diploma in Actuarial Science.

Programme Learning Outcomes for MSc (Actuarial Science) (NFQ Level 9, Major Award)
On successful completion of this programme, students should be able to:

• Demonstrate a knowledge of the theory of mathematical and statistical finance, actuarial finance methods, probability and statistical methods, survival models and market analysis as described by the syllabi of the first stage subjects of the Institute and Faculty of Actuaries;
• Describe the fundamental theories, models and principles of Actuarial Science and carry out a wide range of calculations involved in financial decision making, valuation and risk modelling;
• Summarize and communicate, in written and oral form, actuarial models and techniques and present such summaries to technical and non-technical audiences;
• Analyse problems of a quantitative nature, encountered in the insurance, pensions and financial industry, and construct solutions to such problems using the tools and skills of modern actuarial practice including the use of statistical and mathematical computer packages and the use of spread sheet and database programmes;
• Enter graduate or research careers in actuarial fields with the ability to significantly contribute to the financial services industry and moreover to society as a whole in using skills and education to identify, assess, manage and quantify uncertainty and risk in various situations;
• Demonstrate the competences, skills and leadership qualities which ultimately lead to the title of Actuarial Fellow.

NFQ Level 9, Major Award

The MSc is a full-time taught Master's Degree programme running for 12 months from the date of first registration for the programme. Students take taught modules in Semesters 1 and 2, followed by a research/development project from May to September.

Programme Requirements
The Masters Degree consists of 90 credits: taught modules to the value of 60 credits and a research/development project to the value of 30 credits. The taught modules comprise core modules to the value of 30 credits and elective modules to the value of 30 credits. Students are required to seek approval of the Head of Department for their choice of elective modules, following consultation with the programme coordinator. Not all elective modules will be offered each year.

Students must choose modules as follows: core modules to the value of 30 credits, plus elective modules to the value of 15 credits chosen from Group I and elective modules to the value of 15 credits chosen from Group II, plus the Research/Development Project (30 credits). Students will have completed all taught modules and related examining prior to commencing the Research/Development Project.

Core Modules
CS6403 Case Studies in Computing Entrepreneurship (5 credits)
CS6408 Database Technology (5 credits)
CS6409 Information Storage and Retrieval (5 credits)
CS6410 Project Development Skills (5 credits)
CS6422 Complex Systems Development (5 credits)
CS6423 Scalable Computing for Data Analytics (5 credits)
CS6400 Dissertation in Computing Science (30 credits)

Elective Modules Group I
CS6301 Design of Cyber-Physical Systems (5 credits)
CS6311 Mobile Network Protocols (5 credits)
CS6312 Mobile Devices and Systems (5 credits)
CS6314 Mobile Applications Design (5 credits)
CS6320 Formal Methods for Distributed Systems (5 credits)
CS6321 Software System Modelling and Development (5 credits)
CS6322 Optimisation (5 credits)
CS6326 Applied Computer Simulation and Analysis (5 credits)
CS6402 Virtualisation Technologies (5 credits)
CS6420 Topics in Artificial Intelligence (5 credits)
CS6424 Special Topics in Computing Science I (5 credits)

Elective Modules Group II
CS6313 Services and Mobile Middleware (5 credits)
CS6315 Mobile Systems Security (5 credits)
CS6316 Cellular Network Services (5 credits)
CS6317 Multimedia Technology in Mobile Networks (5 credits)
CS6318 Advanced Topics in Networking (5 credits)
CS6319 Applied Machine Learning (5 credits)
CS6325 Network Security (5 credits)
CS6327 Internet of Things: Technology and Application (5 credits)
CS6405 Datamining (5 credits)
CS6421 Deep Learning (5 credits)
CS6425 Special Topics in Computing Science II (5 credits)

Examinations
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2022/2023 Book and for each module in the Book of Modules, 2022/2023.

Postgraduate Diploma in Computing Science
Students failing to achieve an aggregate of at least 60% across all modules but who achieve a pass in each of the taught modules at their first attempt graduate with a Postgraduate Diploma in Computing Science. Students may also opt to exit the programme and graduate with a Postgraduate Diploma in Computing Science provided they have achieved a pass in each module.

NFQ Level 9, Major Award

The MSc in Data Science and Analytics is a full-time programme running for 12 months.

Programme Requirements

Students take 90 credits as follows:

Part 1 (60 credits)

Core Modules (30 credits) - All selections are subject to approval of the programme coordinator.

CS6405 Data Mining (5 credits)
CS6421 Deep Learning (5 credits)
ST6030 Foundations of Statistical Data Analytics (10 credits)
ST6033 Generalised Linear Modelling Techniques (5 credits)

Students who have adequate database experience take:
CS6408 Database Technology (5 credits)

Students who have not studied databases take:
CS6503 Introduction to Relational Databases (5 credits)

Elective Modules (30 credits) - All selections are subject to approval of the programme coordinator.

Students must take at least 10 credits of CS (Computer Science) modules and at least 10 credits of ST (Statistics) modules from those listed below:

CS6322 Optimisation (5 credits)
CS6409 Information Storage and Retrieval (5 credits)
CS6420 Topics in Artificial Intelligence (5 credits)
CS6426 Data Visualization for Analytics Applications (5 credits)
ST6034 Multivariate Methods for Data Analysis (10 credits)
ST6035 Operations Research (5 credits)
ST6036 Stochastic Decision Science (5 credits)
ST6040 Machine Learning and Statistical Analytics I (5 credits)
ST6041 Machine Learning and Statistical Analytics II(5 credits)

Students who have adequate programming experience can take:
CS6422 Complex Systems Development (5 credits) and
CS6423 Scalable Computing for Data Analytics (5 credits)

Students who have not studied programming can take:
CS6506 Programming in Python (5 credits) and
CS6507 Programming in Python with Data Science Applications (5 credits)

Part 2 (30 credits)

Students select one of the following modules:
CS6500 Dissertation in Data Analytics (30 credits)
ST6090 Dissertation in Data Analytics (30 credits)

Examinations
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2022/2023 Book and for each module in the Book of Modules, 2022/2023.

Postgraduate Diploma in Data Science and Analytics
Students who pass each of the taught modules may opt to exit the programme and be conferred with a Postgraduate Diploma in Data Science and Analytics.

Programme Learning Outcomes for MSc (Data Science and Analytics) (NFQ Level 9, Major Award)
On successful completion of this programme, students should be able to:

• Interpret large, heterogeneous data sources by comparing and selecting appropriate data analytic techniques, using software tools for data storage/management and analysis, machine learning, and probabilistic and statistical methods;
• Describe the fundamental theories, models and principles of statistical methods, and carry out a wide range of calculations involved in statistical decision making, modelling, hypothesis generation and inference;
• Describe the fundamental theories, models and principles of computational methods for storing, processing and performing inference on large data sets; examples include machine learning, data mining and probabilistic methods;
• Manage large amounts of data using modern database tools, and understand the management implications of hardware, software and bandwidth constraints;
• Analyse data selected from a range of domains such as manufacturing, bio-informatics, marketing, social networking, finance, fraud detection, and drug discovery;
• Perform computational/statistical analyses and create visualizations to aid in understanding heterogeneous data;
• Summarize and communicate, in written and oral form, computational and statistical models and techniques, and be able to visualise this information in order to best present such summaries to technical and non-technical audiences;
• Analyse problems of a computational and/or quantitative nature, encountered in a range of types of large-scale data, and construct solutions to such problems using the tools and skills of modern data analytics, including the use of machine learning, statistical and mathematical computer packages, and the use of database programmes;
• Enter graduate or research careers in analytical fields, with the ability to significantly contribute in a broad range of industries (and moreover to society as a whole) in using skills and education to identify, assess, manage and quantify key findings (e.g., trends, risk, uncertainty) in various situations;
• Understand the privacy, legal and ethical issues associated with the storage and analysis of data.

Programme Learning Outcomes for Postgraduate Diploma in Data Science and Analytics (NFQ Level 9, Major Award)
On successful completion of this programme, students should be able to:

• Interpret large, heterogeneous data sources by comparing and selecting appropriate data analytic techniques, using software tools for data storage/management and analysis, machine learning, and probabilistic and statistical methods;
• Describe the fundamental theories, models and principles of statistical methods, and carry out a wide range of calculations involved in statistical decision making, modelling, hypothesis generation and inference;
• Describe the fundamental theories, models and principles of computational methods for storing, processing and performing inference on large data sets;
• Manage large amounts of data using modern database tools, and understand the management implications of hardware, software and bandwidth constraints;
• Analyse data selected from a range of domains such as manufacturing, bio-informatics, marketing, social networking, finance, fraud detection, and drug discovery;
• Perform computational/statistical analyses and create visualizations to aid in understanding heterogeneous data;
• Analyse problems of a computational and/or quantitative nature, encountered in a range of types of large-scale data, and construct solutions to such problems using the tools and skills of modern data analytics, including the use of machine learning, statistical and mathematical computer packages, and the use of database programmes.

NFQ Level 9, Major Award

The MSc (Financial and Computational Mathematics) is a taught programme that may be taken full-time over 12 months from the date of first registration for the programme.

Programme Requirements
The Master's Degree consists of 90 credits consisting of taught modules for a total of 60 credits and a dissertation for a total of 30 credits.

Part I

Core Modules
Students take modules to the value of 45 credits as follows:
MF6010 Probability Theory in Finance (10 credits)
MF6011 Derivatives, Securities, and Option Pricing (5 credits)
MF6012 Computational Finance I (5 credits)
MF6014 Topics in Financial Mathematics (5 credits)
MF6015 Continuous-Time Financial Models (5 credits)
AM6004 Numerical Methods and Applications (5 credits)
AM6016 Dynamic Machine Learning with Applications (5 credits)
CS6322 Optimisation (5 credits)

Elective Modules
Students take modules to the value of 15 credits from the following:
AM4062 Applied Stochastic Differential Equations (5 credits)
AM6007 Scientific Computing with Numerical Examples (10 credits)
AM6019 Partial Differential Equations (5 credits)
ST4400 Data Analysis II (5 credits)
ST6040 Statistical Analytics Implementations I (5 credits)
ST6041Machine Learning and Statistical Analytics II (5 credits)
CS6503 Introduction to Relational Databases (5 credits)

Part II

MF6016 Dissertation in Financial and Computational Mathematics (30 credits)

Note: Module selection must be approved by the module co-ordinator.

Examinations
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2022/2023 Book and for each module in the Book of Modules, 2022/2023.

Postgraduate Diploma in Financial and Computational Mathematics
Candidates must pass all modules in Part I and achieve a minimum aggregate of at least 50% across all modules in Part I at the first attempt in order to proceed to Part II. Students who do not meet this require,ment or choose not to progress to Part II will exit the programme with the Postgraduate Diploma in Financial and Computational Mathematics.

Programme Learning Outcomes for MSc (Financial and Computational Mathematics) (NFQ Level 9, Major Award)
On successful completion of this programme, students should be able to:

• Demonstrate technical competence in the computational aspects of financial mathematics;
• Explain the theoretical basis of mathematical models and techniques used in financial applications;
• Outline how this mathematical framework is influenced by the structure of financial marketsEvaluate a range of digital formats (digital images, websites, digital sound and video, 3D models) and other specialist aspects of interactive media production;
• Identify the limitations of mathematical and statistical models applied to real-world scenariosa;
• Apply appropriate programming languages and software packages to the analysis of problems and mathematical models arising in financial applications;
• Conduct and complete a substantial mathematical research project, and defend their findings in front of one or more domain experts.

Programme Learning Outcomes for Postgraduate Diploma in Financial and Computational Mathematics (NFQ Level 9, Major Award)
On successful completion of this programme, students should be able to:

• Demonstrate technical competence in the computational aspects of financial mathematics;
• Explain the theoretical basis of mathematical models and techniques used in financial applications;
• Outline how this mathematical framework is influenced by the structure of financial marketsEvaluate a range of digital formats (digital images, websites, digital sound and video, 3D models) and other specialist aspects of interactive media production;
• Identify the limitations of mathematical and statistical models applied to real-world scenariosa;
• Apply appropriate programming languages and software packages to the analysis of problems and mathematical models arising in financial applications.

NFQ Level 9, Major Award

The MSc (Interactive Media) is a taught programme that may be taken full-time over 12 months or part-time over 24 months from the date of first registration for the programme.

Programme Requirements
The Master's Degree consists of 90 credits consisting of taught modules for a total of 60 credits and a substantial project undertaken by the students for a total of 30 credits.

Core Modules
Full-time students are required to take the following 30 credits of core modules. Part-time students are required to take three of the following core modules in each year (15 credits), for a total of six separate modules over the two years (30 credits).
CS6100 Authoring (5 credits)
CS6101 Web Development for Digital Media (5 credits)
CS6102 Graphics for Interactive Media (5 credits)
CS6103 Audio and Sound Engineering (5 credits)
CS6104 Digital Video Capture and Packaging (5 credits)
CS6111 3D Graphics and Modelling (5 credits)

Full-time and part-time students are required to take a project as follows:
CS6200 Dissertation in Interactive Media (30 credits)

and
Full-time students are required to take 30 credits from the following elective modules. Part-time students are required to take three of the following elective modules in each year (15 credits), for a total of six separate modules over the two years (30 credits). Selection of elective modules is subject to the agreement of the Programme Director.
CS6105 Future and Emerging Interaction Technologies (5 credits)
CS6110 Animation (5 credits)
CS6112 Image Processing (5 credits)
CS6113 Internet-based Applications (5 credits)
CS6114 Digital Video Compression and Delivery (5 credits)
CS6115 Human Computer Interaction (5 credits)
CS6116 Mobile Multimedia (5 credits)*
CS6117 Audio Processing (5 credits)
CS6118 Speech Processing (5 credits)
CS6119 Interactive Visualisation (5 credits)
CS6120 Intelligent Media Systems (5 credits)
CS6121 Interactive Media Special Project (5 credits)*

* Students who have previously taken CS3032 cannot take CS6116 and must take CS6121.

Note: Not all elective modules may be offered in a particular year.

Examinations
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2022/2023 Book and for each module in the Book of Modules, 2022/2023.

Postgraduate Diploma in Interactive Media
Students who successfully achieve the pass standard in the examination may opt not to proceed to the digital media project and may opt instead to be awarded the Postgraduate Diploma in Interactive Media.

Programme Learning Outcomes for MSc (Interactive Media) (NFQ Level 9, Major Award)
On successful completion of this programme, students should be able to:

• Describe the technologies of interactive media production;
• Identify the elements that are likely to make for effective interactive media;
• Evaluate a range of digital formats (digital images, websites, digital sound and video, 3D models) and other specialist aspects of interactive media production;
• Apply theoretical models and concepts to issues surrounding interactive media;
• Use programming techniques and specialist applications to develop interactive media applications;
• Work as individuals and together in teams;
• Plan, implement and deliver a practical interactive media production.

NFQ Level 9, Major Award

The MSc in Molecular Cell Biology with Bioinnovation is a full-time programme that runs for 12 months from the date of first registration for the programme.

Programme Requirements:

Students take 90 credits as follows:

Core Modules:
IS6306 Technology and Business Planning (5 credits)
MG6705 Marketing for Technology Entrepreneurs (5 credits)
ML6002 Biological and Clinical Perspectives of Human Disease (10 credits)
ML6003 Scientific Communication of Current Topics in Molecular Cell Biology (5 credits)
ML6004 Cell and Molecular Biology (10 credits)
ML6005 Molecular Techniques in the Life Sciences (5 credits)
ML6006 Human Molecular Genetics and Genetic Engineering Techniques (5 credits)

Research Module
ML6001 Molecular Cell Biology Research Dissertation (40 credits)

Elective Modules
Students select one of the following modules:
BU6011 Innovation Finance (5 credits)
IS6307 Creativity and Opportunity Recognition (5 credits)
LW6104 Principles of Intellectual Property Law (5 credits)

Examinations
Full details and regulations governing Examinations for each programme will be contained in the Marks and Standards 2022/2023 Book and for each module in the Book of Modules, 2022/2023.

Postgraduate Certificate in Molecular Cell Biology with Bioinnovation
Students passing modules to the value of at least 30 credits (including ML6004, ML6006, ML6002 and ML6005) and achieving an aggregate pass across all taught modules to the value of 50 credits may opt to exit the programme and be awarded a Postgraduate Certificate in Molecular Cell Biology with Bioinnovation. Similarly, students who pass the taught modules and do not wish to complete the research dissertation may opt to be conferred with a Postgraduate Certificate in Molecular Cell Biology with Bioinnovation.

Programme Learning Outcomes for MSc (Molecular Cell Biology with Bioinnovation) (NFQ Level 9, Major Award)
On successful completion of this programme, students should be able to:

• Display advanced theoretical knowledge and practical understanding in the area of Molecular Cell Biology;
• Describe the basis and application of laboratory methods used in Molecular Cell Biology Research including state-of-the-art techniques and their limitations;
• Develop and complete an independent research project addressing emerging questions in Molecular Cell Biology, and acquire transferable skills in experimental design and critical analysis, written and oral communication and project management;
• Investigate the current approaches used in patient diagnosis, and treatment options available for the management of different diseases including cancer, infectious and inflammatory diseases and neurodegeneration through a programme module taught predominantly by clinicians;
• Source, review, critically assess and evaluate relevant primary literature and summarize material for presentation to peers;
• Demonstrate knowledge of the structure of the Biotechnology Industry and best-practice Intellectual Property Management;
• Integrate science and business skills and develop a Marketing and Business plan for a fledgling Biotech company;
• Describe the financial processes of biotech innovation;
• Develop professional practice skills including team-work, negotiation, presentation, writing and oral communication etc.

Programme Learning Outcomes for Postgraduate Certificate in Molecular Cell Biology with Bioinnovation (NFQ Level 9, Minor Award)
On successful completion of this programme, students should be able to:

• Display advanced theoretical knowledge and practical understanding in the area of Molecular Cell Biology;
• Understand the basis and application of laboratory methods used in Molecular Cell Biology Research including state-of-the-art techniques and a knowledge of their limitations;
• Investigate the current approaches used in patient diagnosis, and treatment options available for the management of different diseases including cancer, infectious and inflammatory diseases and neurodegeneration through a programme module taught predominantly by clinicians;
• Demonstrate knowledge of the structure of the Biotechnology Industry and best-practice Intellectual Property Management;
• Integrate science and business skills and develop a Marketing and Business plan for a fledgling Biotech company;
• Describe the financial processes of biotech innovation;
• Develop professional practice skills including team-work, negotiation, presentation, writing and oral communication etc.