Programme Specification
MSc Data Analytics
Academic Year 2022/23
A programme specification is required for any programme on which a student may be registered. All programmes of the University are subject to the University's Quality Assurance processes. All degrees are awarded by Queen's University Belfast.
| Programme Title | MSc Data Analytics | Final Award (exit route if applicable for Postgraduate Taught Programmes) |
Master of Science | |||||||||||
| Programme Code | MTH-MSC-DA | UCAS Code | HECoS Code |
101034 - Statistical modelling - 100 |
||||||||||
| ATAS Clearance Required | No | |||||||||||||
| Mode of Study | Full Time or Part Time | |||||||||||||
| Type of Programme | Postgraduate | Length of Programme |
Full Time - 1 Calendar Year Part Time - 2 Calendar Years |
Total Credits for Programme | 180 | |||||||||
| Exit Awards available | No | |||||||||||||
Institute Information
Teaching Institution |
Queen's University Belfast |
School/Department |
Mathematics & Physics |
|
Quality Code Higher Education Credit Framework for England |
Level 7 |
|
Subject Benchmark Statements The Frameworks for Higher Education Qualifications of UK Degree-Awarding Bodies |
Psychology (2010) |
Accreditations (PSRB) |
|
No accreditations (PSRB) found. | |
Regulation Information
|
Does the Programme have any approved exemptions from the University General Regulations |
|
Programme Specific Regulations Students who fail one or more taught modules up to the value of 40 CATS points will have the opportunity to re-sit failed modules only at the next available opportunity. |
|
Students with protected characteristics |
|
Are students subject to Fitness to Practise Regulations (Please see General Regulations) No |
Educational Aims Of Programme
The aim of the programme is to offer a multi-disciplinary education in data analytics that prepares graduates with key knowledge, skills and competencies necessary for employment in analytics and data science positions. In particular, the programme aims to provide students with Comprehensive knowledge and understanding of the fundamental principles of statistics and computer science that underpin analytics. Advanced knowledge and practical skills in the theory and practice of analytics. The necessary skills, tools and techniques needed to embark on careers in data analytics and data science. Skills in a range of practices, processes, tools and methods applicable to analytics in commercial and research contexts. Timely exposure to, and practical experience in, a range of current software packages and emerging new applications of analytics. Opportunities for the development of practical skills in a commercial context.
Consistent with the general Educational Aims of the Programme and the specific requirements of the Benchmarking Statement for Master's degrees in Mathematics, Statistics and Operational Research and Master's degrees in Computing, this specification provides a concise summary of the main features of the Masters in Data Analytics, and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes advantage of the learning opportunities that are provided.
Learning Outcomes
Learning Outcomes: Cognitive SkillsOn the completion of this course successful students will be able to: |
|
|
Demonstrate proficiency in the conduct of small investigations at all stages from setup to final report. |
Teaching/Learning Methods and Strategies Knowledge primarily developed in project modules Methods of Assessment Combination of presentations, practical work, coursework and dissertation |
|
Analyse problems and situations in mathematical/analytical terms. |
Teaching/Learning Methods and Strategies Strongly developed as a key part of the majority of modules. Methods of Assessment Combination of unseen written examinations, practical work, coursework and dissertation |
|
Apply mathematical knowledge accurately in the solution of examples and problems. |
Teaching/Learning Methods and Strategies Strongly developed in modules with an emphasis on laboratory work and strongly developed in the project. Methods of Assessment Combination of unseen written examinations, practical work, coursework and dissertation |
|
Apply programming knowledge to be able to write code to carry out data manipulation and analytics approaches. |
Teaching/Learning Methods and Strategies Strongly developed throughout the course where it is a key part of the majority of modules. Methods of Assessment Combination of practical work, coursework and the dissertation. |
|
Apply programming and computational thinking to find a solution to examples and problems. |
Teaching/Learning Methods and Strategies Strongly developed throughout the course where it is a key part of the majority of modules. Methods of Assessment Combination of practical work, coursework and the dissertation. |
Learning Outcomes: Knowledge & UnderstandingOn the completion of this course successful students will be able to: |
|
|
The underpinning principles of statistics and computing relevant to analytics. |
Teaching/Learning Methods and Strategies Forms a core part of the whole programme and is developed across all modules. Methods of Assessment Combination of unseen written examinations, presentations |
|
The essential theories, practices, languages and tools that may be deployed to carry out analytics. |
Teaching/Learning Methods and Strategies Forms a core part of the whole programme and is strongly developed throughout all modules. Methods of Assessment Combination of unseen written examinations, practical work, coursework, presentations |
|
Demonstrate knowledge and understanding of a wide range of advanced-level topics in analytics (within statistics and computing). |
Teaching/Learning Methods and Strategies Practical skills developed throughout all modules, with key skills delivered through laboratory work. Methods of Assessment Combination of unseen written examinations, practical work, coursework and dissertation |
|
Demonstrate accuracy in reasoning and/or modelling within these advanced level topics |
Teaching/Learning Methods and Strategies Knowledge primarily developed in lectures and applied through practical sessions and coursework assignments. Methods of Assessment Combination of unseen written examinations, practical work, coursework, presentations and dissertation |
|
Read and master topics presented in the statistics and computing literature, with a specific topic studied in significant depth |
Teaching/Learning Methods and Strategies Developed in the project modules. Methods of Assessment Coursework and dissertation |
Learning Outcomes: Subject SpecificOn the completion of this course successful students will be able to: |
|
|
Apply a range of concepts, tools and techniques to the solution of a wide range of analytics problems, with application to one topic studied in significant depth. |
Teaching/Learning Methods and Strategies Very strongly addressed across the whole programme. Methods of Assessment Combination of unseen written examinations, practical work and dissertation |
|
Deploy appropriate computing and statistics theory and practices to a wide range of analytics problems, with application to one topic studied in significant depth. |
Teaching/Learning Methods and Strategies Strongly developed in modules with an emphasis on laboratory work and strongly developed in the project Methods of Assessment Combination of practical work, coursework and dissertation |
|
Effectively use tools for developing and testing a wide range of analytics models, with application to one topic studied in significant depth. |
Teaching/Learning Methods and Strategies Strongly addressed across the whole programme. Methods of Assessment Combination of practical work, coursework and dissertation |
|
Implement algorithms, and programs using programming languages to solve a wide range of analytics problems, with application to one topic studied in significant depth. |
Teaching/Learning Methods and Strategies Strongly addressed across the whole programme, particularly those with a major software aspect. Methods of Assessment Combination of practical work and dissertation |
|
Articulate and effectively communicate the rationale for a wide range of analytics solutions interpret the results / make recommendations through appropriate technical reports and presentations. |
Teaching/Learning Methods and Strategies Strongly developed in the research project and well developed in all other modules. Methods of Assessment Combination of unseen written examinations, coursework, presentations and dissertation |
Learning Outcomes: Transferable SkillsOn the completion of this course successful students will be able to: |
|
|
Work effectively with and for others, including as part of a team. |
Teaching/Learning Methods and Strategies Strongly developed in practical project work, particularly where undertaken with industry. Also, developed in technical modules with shared laboratory work elements. Methods of Assessment Combination of practical work, coursework, presentations |
|
Use appropriate computational tools efficiently in the solution of analytics problems, where applicable, and in the presentation of these. |
Teaching/Learning Methods and Strategies Very strongly developed in project work, but also moderately developed through coursework in taught modules. Methods of Assessment Combination of practical work, coursework and dissertation |
|
Explain advanced-level analytics to specialist and non-specialist audiences in both oral and written form. |
Teaching/Learning Methods and Strategies Strongly developed in project work, and also moderately developed through coursework. Methods of Assessment Combination of unseen written examinations, practical work, coursework, presentations and dissertation |
|
Adopt an analytical approach to problem solving. |
Teaching/Learning Methods and Strategies Forms a core part of the majority of the programme and is strongly developed across the programme. Methods of Assessment Combination of unseen written examinations, practical work, coursework, presentations and dissertation |
|
Learn independently in familiar and unfamiliar situations with open-mindedness and a spirit of critical enquiry. |
Teaching/Learning Methods and Strategies Is supported through practical sessions, group work and work placements. Methods of Assessment Combination of practical work, coursework, analyticathons, project work and unseen examination questions. |
|
Motivate, take responsibility for and lead others effectively to accomplish objectives and goal. |
Teaching/Learning Methods and Strategies Strongly developed in the Analytics in Action module through Analyticathons, practicals, group work and the Individual Industry Based Project. Methods of Assessment Combination of group work, coursework, practical work and projects. |
Module Information
Stages and Modules
| Module Title | Module Code | Level/ stage | Credits | Availability |
Duration | Pre-requisite | Assessment |
|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S1 | S2 | Core | Option | Coursework % | Practical % | Examination % | ||||||
| Analytics in Action | DSA8023 | 1 | 20 | -- | YES | 4 weeks | N | YES | -- | 60% | 40% | 0% |
| Individual Industry Based Project | DSA8030 | 1 | 60 | YES | -- | 12 weeks | N | YES | -- | 70% | 30% | 0% |
| Frontiers in Analytics | DSA8022 | 1 | 20 | -- | YES | 4 weeks | N | YES | -- | 5% | 95% | 0% |
| Machine Learning | DSA8021 | 1 | 20 | -- | YES | 4 weeks | N | YES | -- | 5% | 55% | 40% |
| Database & Programming Fundamentals | DSA8002 | 1 | 20 | YES | -- | 4 weeks | N | YES | -- | 70% | 30% | 0% |
| Data Mining | DSA8003 | 1 | 20 | YES | -- | 4 weeks | N | YES | -- | 5% | 25% | 70% |
| Data Analytics Fundamentals | DSA8001 | 1 | 20 | YES | -- | 4 weeks | N | YES | -- | 5% | 25% | 70% |
Notes
Students must take the seven compulsory modules listed. Modules are taught in block mode, with each module taking four weeks full-time, including self-study and assessment. Modules are taught in increasing module code number.