Skip to Content

detail

MSc Data Analytics

Academic Year 2019/20

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 and Enhancement processes as set out in the DASA Policies and Procedures Manual.

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

ATAS Clearance Required

No

Health Check Required

No

Portfolio Required

Interview Required

Mode of Study

Full Time

Type of Programme

Postgraduate

Length of Programme

1 Calendar Year(s)

Total Credits for Programme

180

Exit Awards available

INSTITUTE INFORMATION

Awarding Institution/Body

Queen's University Belfast

Teaching Institution

Queen's University Belfast

School/Department

Mathematics & Physics

Framework for Higher Education Qualification Level 
www.qaa.ac.uk

Level 7

QAA Benchmark Group
www.qaa.ac.uk/quality-code/subject-benchmark-statements

Engineering (2015)

Accreditations (PSRB)

REGULATION INFORMATION

Does the Programme have any approved exemptions from the University General Regulations
(Please see 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 must complete modules in block delivery mode where each module runs in blocks of 4 weeks in a sequential manner where at any one time, the student is working on only one module. Week 1 of block delivery mode requires students to carry out background reading and preparation work in advance of week 2 which requires students to attend lectures/labs Monday -Friday 9am-5pm. Weeks 3 and 4 are for project and coursework.

The programme can also be taken part-time, either as a two-year programme, or as a three-year programme.
The sequence of modules is as follows:
For both options: Year 1 - DSA8001, DSA8002 andDSA8021
Year 2 in the 2-year option: DSA8003, DSA8022, and DSA8023, and the project module DSA8030.
Year 2 in the 3-year option: DSA8003, DSA8022, and DSA8023
Year 3 in the 3-year option: the project module DSA8030.

Students who, at the first attempt, have failed taught modules with a combined value greater than 40 CATS points or who have failed the same module twice will normally not be permitted to proceed to the Individual Industry Based Project. These students may qualify for transfer to the Postgraduate Certificate in Data Analytics, based on performance in DSA8001 and DSA8002, or will be required to withdraw.

Students who obtain 120 CATS points in the taught modules are required to undertake an Individual Industry Based Project and submit a dissertation in September. The Individual Industry Based Project is assessed in September. Project Marks will be moderated by the external examiner and finalised at the Board of Examiners meeting in November. The pass mark for the Individual Industry Based Project module is 50% and there is no resit opportunity. The Individual Industry Based Project must be passed for the award of the MSc. A student, who fails the Individual Industry Based Project, will be eligible to receive the Postgraduate Diploma in Data Analytics

It is normally expected that students will have passed all of the required taught modules (120 CATS points) before they begin their individual industry-based project.

Students may be allowed to defer the submission of the project if the summer time is unsuitable to take on the project work, subject to prior approval by the Programme Director. In this scenario the project may be submitted by a later pre-approved date. Student must request permission from the Programme Director no later than the submission of their list of project preferences. If they do not follow this time frame they may be charged an additional fee for the project.

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 Skills

On 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 & Understanding

On 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 Specific

On 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 Skills

On 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

Programme Requirements

Module Title

Module Code

Level/ stage

Credits

Availability

Duration

Pre-requisite

 

Assessment

 

 

 

 

S1

S2

 

 

Core

Option

Coursework %

Practical %

Examination %

Data Analytics Fundamentals

DSA8001

1

20

YES

4 weeks

N

YES

50%

0%

50%

Database & Programming Fundamentals

DSA8002

1

20

YES

4 weeks

N

YES

70%

30%

0%

Data Mining

DSA8003

1

20

YES

4 weeks

N

YES

20%

40%

40%

Machine Learning

DSA8021

1

20

YES

4 weeks

N

YES

20%

40%

40%

Frontiers in Analytics

DSA8022

1

20

YES

4 weeks

N

YES

60%

40%

0%

Analytics in Action

DSA8023

1

20

YES

4 weeks

N

YES

80%

20%

0%

Individual Industry Based Project

DSA8030

1

60

YES

12 weeks

N

YES

70%

30%

0%

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 listed in the provisional order in which they are taught.