Courses

Postgraduate Programme Specification

PgCert Data Analytics

Academic Year 2023/24

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

PgCert Data Analytics

Final Award
(exit route if applicable for Postgraduate Taught Programmes)

Postgraduate Certificate

Programme Code

MTH-PC-AN

UCAS Code

HECoS Code

101034 - Statistical modelling - 100

ATAS Clearance Required

Health Check Required

Portfolio Required

Interview Required

Mode of Study

Full Time

Type of Programme

Postgraduate

Length of Programme

Full Time - 4 Months

Total Credits for Programme

180

Exit Awards available

Institute Information

Teaching Institution	Queen's University Belfast
School/Department	Mathematics & Physics
Quality Code https://www.qaa.ac.uk/quality-code Higher Education Credit Framework for England https://www.qaa.ac.uk/quality-code/higher-education-credit-framework-for-england	Level 7
Subject Benchmark Statements https://www.qaa.ac.uk/quality-code/subject-benchmark-statements The Frameworks for Higher Education Qualifications of UK Degree-Awarding Bodies https://www.qaa.ac.uk/docs/qaa/quality-code/qualifications-frameworks.pdf	Mathematics, Statistics and Operational Research (2019)
Accreditations (PSRB)
No accreditations (PSRB) found.

Regulation Information

Does the Programme have any approved exemptions from the University General Regulations
(Please see General Regulations)

Programme Specific Regulations

The Postgraduate Certificate is awarded to students who have successfully completed taught modules worth 60 credits from the following taught modules available on the MSc Data Analytics programme: DSA8001, DSA8002 and DSA8022.

Taught modules start in January 2021 and run through to the end of March 2021 with all assessments completed by April 2021. Students must complete modules in block delivery mode where each module runs in a sequential manner where at any one time, the student is working on only one module. Each module consists of 4 full weeks and requires one full time week of taught classes per module along with online material and support available for the remaining 3 weeks.

In order to replicate the interactive nature of on-campus delivery, the online delivery will include:
• Several masterclasses that both present and explore course topics. The masterclasses will be delivered live to permit learners to connect and ask / answer questions. The classes will also be recorded to permit flexible on-demand access.
• Weekly practice activities including set exercises, coding challenges and other tasks to reinforce learning and build practical skills.
• Online learning materials (directed reading, study packs, etc.) which learners will study in their own time each week.
• Regular formative assessment to measure learner progress and to provide advice and direction.
• Online advisory support for learners to connect with experts to provide bespoke one-to-one support. Offered Monday to Friday, daytime to early evening, to flexibly support learners.

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 at the next available opportunity. Students are allowed to re-sit failed modules only once.

Maximum capacity on this course is set at 100

Students with protected characteristics

Are students subject to Fitness to Practise Regulations

(Please see General Regulations)

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. The necessary skills, tools and techniques needed to embark on careers in data analytics and data science. Timely exposure to, and practical experience in, a range of current software packages and emerging new applications of analytics.
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 PGCert 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:

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 practical work and coursework.

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.

Methods of Assessment

Combination of unseen written examinations, practical work, and coursework.

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 unseen written examinations, practical work, and coursework.

Apply programming and computational thinking to find a solution to examples and problems.es.

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 and coursework.

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

Unseen written examinations

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, and coursework

Demonstrate accuracy in reasoning and/or modelling within these essential 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 and coursework.

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

Moderately addressed across the whole programme.

Methods of Assessment

Combination of unseen written examinations, and practical work.

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.

Methods of Assessment

Combination of practical work and coursework.

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 and coursework.

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 coursework

Learning Outcomes: Transferable Skills

On the completion of this course successful students will be able to:

Use appropriate computational tools efficiently in the solution of analytics problems, where applicable, and in the presentation of these.

Teaching/Learning Methods and Strategies

Moderately developed through coursework in taught modules.

Methods of Assessment

Combination of practical work and coursework

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, and coursework

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.

Methods of Assessment

Combination of practical work, coursework, and unseen examination questions.

Module Information

Stages and Modules

Module Title	Module Code	Level/ stage	Credits	Availability		Duration	Pre-requisite			Assessment
				S1	S2			Core	Option	Coursework %	Practical %	Examination %
Frontiers in Analytics	DSA8022	1	20	--	YES	4 weeks	N	YES	--	5%	95%	0%
Database & Programming Fundamentals	DSA8002	1	20	--	YES	4 weeks	N	YES	--	65%	35%	0%
Data Analytics Fundamentals	DSA8001	1	20	--	YES	4 weeks	N	YES	--	5%	95%	0%

Notes

Students must take the THREE 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.