Course Structure

The PgCert in Data Analytics contains 60 credits of the established and well-regarded MSc in Data Analytics. The first two modules DSA8001 and DSA8002 will take place from October in Semester 1 and the final module DSA8022 will take place in Semester 2. All modules will be delivered online.

Please note that although the course is part time in terms of number of modules taken, the modules themselves are taught full time in block delivery mode as detailed below.

Course Details

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.

Modules

Semester 1
Data Analytics Fundamentals
Databases and Programming Fundamentals

Semester 2
Frontiers in Data Analytics

Learning and Teaching

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 of each block which requires students to attend lectures/labs Monday –Friday 9am-5pm.


In the four week duration of a module, there will be an intensive teaching week where the schedule will consists of 9am-5pm with approximately equal numbers of lectures (in the mornings) and labs (in the afternoons).

Please note that although the course is part time in terms of number of modules taken, the modules themselves are still taught full time in block delivery mode as detailed above.

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Assessment

Assessments associated with the course are outlined below:

• Coursework
• Written examination
• Practical examination

Modules

• Year 1

  Core Modules

  Data Analytics Fundamentals (20 credits)

  ×

  Data Analytics Fundamentals

  Overview

  This module will introduce data analytics and the basic approaches used to collect and investigate data in a meaningful way. The statistical concepts for understanding distributions and probability will be introduced along with a number of tests and approaches that can be used to evaluate the quality of data assessing it for blunders, missingness, outliers and skewness. Statistical models and the concept of predictive analytics will be introduced and examples given through the introduction of regression analysis. The module will introduce the R software. Topics covered will include:
  
  Introduction to data analytics with examples of analytics from companies such as Facebook and Netflix.
  
  Hypothesis testing for determining the significance of observations.
  Statistical modelling and predictive analytics
  Regression analysis - predicting new data values via regression models.
  R system for working with statistical data.

  Learning Outcomes

  On completion of this module, a student will have achieved the following learning outcomes, commensurate with module classification:
  Knowledge and understanding of the concept of data analytics and predictive analytics.
  Knowledge and understanding of hypothesis testing.
  Be able to carry out predictive analytics using regression analysis.
  The ability to carry out analysis using the R package.

  Skills

  The ability to use statistical tools to assess data quality and distributional form and cleanse data.

  Coursework

  5%

  Examination

  0%

  Practical

  95%

  Credits

  20

  Module Code

  DSA8001

  Teaching Period

  Spring

  Duration

  4 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Frontiers in Analytics (20 credits)

  ×

  Frontiers in Analytics

  Overview

  The module highlights two state-of-the-art disciplines in the general field of analytics: Visual Analytics and Behavioural Analytics. Both disciplines include exploration of how humans are involved analytics, albeit from very different perspectives.
  This module will firstly introduce the concept of Visual Analytics, defined as the science of analytical reasoning that is facilitated by interactive visual interfaces, and its role in decision making for complex systems. The definition of visual analytics will be discussed in detail, and a summary of its integral components explored, including analytical reasoning, data representations and transformations. The module will include introduction to the design and development of interactive dashboards, along with practical implementation. Decision theory and decision making will also be addressed.
  Secondly the module will introduce the basics of Behavioural Measurement and Analytics exploring the key issues involved in measuring and analysing human behaviour. The module addresses the opportunities, difficulties and issues that arise from the use of multiple sensor technologies, wearable and small scale devices. It explores the use of these technologies to measure and understand human behaviour; mood feelings and emotions; social interactions and communication; and movement in different settings.
  Module topics covered will include:
  • Visual Analytics as a science
  • Decision theory and decision making
  • Surrogate modelling
  • Visualization theory and techniques (could take this one out if too many bullets)
  • Creation of interactive decision-making environments, including dashboards
  • Affective Computing and Social Signal Processing
  • Synchronisation and multi-modal behaviour measurement
  • Face and Facial Expression Recognition
  • Auditory behaviour, Language and Sentiment Analysis
  Biometric Measurement, body movement and motion capture

  Learning Outcomes

  • Comprehensively describe visual analytics as a science
  • Assess and interpret large, disparate data sets
  • Design and create bespoke interactive decision-making environments
  • Comprehensive knowledge of behavioural measurement and analytics, affective computing and social signal processing.
  • A theoretical understanding and an ability to assess and be aware of the challenges that arise within and between analysis in various behavioural modalities
  A practical ability to address behaviour analytics problems in one or more modalities.

  Skills

  TO BE ADDED

  Coursework

  5%

  Examination

  0%

  Practical

  95%

  Credits

  20

  Module Code

  DSA8022

  Teaching Period

  Summer

  Duration

  4 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Database & Programming Fundamentals (20 credits)

  ×

  Database & Programming Fundamentals

  Overview

  The module will provide the basics of how to extract, store, manage, manipulate and integrate both big and small data using Python. The module will also provide the fundamentals of programming, an introduction to procedural programming and object oriented programming, the basic concepts, the differences between the two approaches, their strengths and weaknesses and some practical experience of coding. Topics covered in this module include:
  
  • Data extraction, management, manipulation and integration
  • Introduction to SQL
  • Procedural programming concepts
  • Object oriented programming concepts

  Learning Outcomes

  Understand fundamental concepts in programming such as variables, loops, logic and functions.
  Demonstrate knowledge and understanding of appropriate techniques in Python for building efficient programs.
  Demonstrate knowledge and understanding of the scientific Python infrastructure and use modules for data manipulation and visualisation.
  Demonstrate knowledge and understanding of applying practical programming and database skills to solve common problems.

  Skills

  Demonstrate ability to design, develop, test and debug simple programs

  Coursework

  65%

  Examination

  0%

  Practical

  35%

  Credits

  20

  Module Code

  DSA8002

  Teaching Period

  Spring

  Duration

  4 weeks

  Pre-requisite

  No

  Core/Optional

  Core

Entrance requirements

Graduate

Normally a 2.1 Honours degree in Mathematics, Statistics, or Computer Science or a closely related discipline, or equivalent qualification acceptable to the University.

Applicants with a minimum 2.2 Honours degree in a cognate discipline, a 2.1 Honours degree in a non-cognate discipline, or who have not yet completed their degree, will be required to pass an aptitude test.

AICC funding: A limited number of fully funded places (provided by the Department for the Economy) are available for this programme for eligible applicants resident in Northern Ireland. Where there are more eligible applicants than places available the academic selectors will make offers in rank order based on academic merit and potential as evidenced in the totality of the information provided within each application. We will operate a waiting list as required to allow us to fill all available places. You will be notified as soon as possible after the deadline whether your application has been selected for a funded place. If you have not been selected for a funded place, we will accept self-funded or employer-funded applicants, if spaces are available. More details can be found at the link below.

Application deadline for AICC funding is Friday 14th June at 12 noon.
https://www.qub.ac.uk/about/Leadership-and-structure/Faculties-and-Schools/Engineering-and-Physical-Sciences/AICC/

International Students

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English Language Requirements

Applicants for whom English is not their first language must be able to demonstrate their proficiency in English in order to benefit fully from their course of study. Please see the following link for further information: https://www.qub.ac.uk/International/International-students/Applying/English-language-requirements/.

International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.

For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.

Career Prospects

Introduction

Industry forecasts indicate that Data Analytics is a growing field internationally, with job opportunities set to increase exponentially predicting growths of 160% between 2013 and 2020 (eSkills report, Big Data Analytics 2013-2020). There is a current shortage in qualified staff for these roles, which is also the case in Northern Ireland where there have been a number of recent investments and expansions in the Data Analytics sector.

The course is designed to meet the needs of Industry where graduates have the right combination of the skills and expertise in both computer science, mathematics and statistics along with the experience they gain in their individual industry based project to be highly sought after for employment.

Queen's postgraduates reap exceptional benefits. Unique initiatives, such as the leadership and executive programmes alongside sterling integration with business experts helps our students gain key leadership positions both nationally and internationally.
http://www.qub.ac.uk/directorates/sgc/careers/

Graduate Plus/Future Ready Award for extra-curricular skills

In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Graduate Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.

Tuition Fees

Northern Ireland (NI) 1	DfE Funded students: Free / Other students: £2,934
Republic of Ireland (ROI) 2	£2,934
England, Scotland or Wales (GB) 1	£3,083
EU Other 3	£7,167
International	£7,167

Please note the tuition fee on this course is not subject to an annual inflationary uplift.

¹EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.

² EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.

³ EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.

All tuition fees quoted relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.

More information on postgraduate tuition fees.

Additional course costs

No tuition fees are payable by eligible students for the programme as it is funded by the Department for the Economy’s Skill Up programme. Please refer to https://www.nidirect.gov.uk/skillup for further information.’

Applicants must meet the entry criteria for the course and be:

• over 18 years of age;
• eligible to work in Northern Ireland;
• settled in Northern Ireland, and has been ordinarily resident in the UK for at least three years; or
is a person who has indefinite leave to enter or remain in the UK

All Students

Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.

Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.

Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.

If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.

Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.

There are also additional charges for graduation ceremonies, examination resits and library fines.

How to Apply

Apply using our online Postgraduate Applications Portal and follow the step-by-step instructions on how to apply.

Apply now

Terms and Conditions

The terms and conditions that apply when you accept an offer of a place at the University on a taught programme of study.
Queen's University Belfast Terms and Conditions.

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