Data Analytics (PgCert)

Program Overview

Data Analytics (PgCert)

Overview

This Postgraduate Certificate (PGCert) program is designed for those seeking a flexible, part-time route to achieving a full Master’s (MSc) degree. Starting with the PGCert, you will complete 60 CAT points. Upon successful completion, you will have the opportunity to progress to the MSc by undertaking the remaining 60 unit of taught modules plus the Summer Industry placement project module.

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.

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.

Modules

• Semester 1:
  • Data Analytics Fundamentals
  • Databases and Programming Fundamentals
• Semester 2:
  • Frontiers in Data Analytics

People teaching you

• Dr Felicity Lamrock
  • Programme Co-ordinator
  • School of Maths and Physics
  • Email: felicity.lamrock@qub.ac.uk

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.

Assessment

• Coursework
• Written examination
• Practical examination

Entrance requirements

• 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/NI Cyber 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.

International Students

• Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region.
• Use the dropdown list below for specific information for your country/region.

English Language Requirements

• Evidence of an IELTS* score of 6.5, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required.
• *taken within the last 2 years

Tuition Fees

• Northern Ireland (NI) 1: £2,933
• Republic of Ireland (ROI) 2: £2,933
• England, Scotland or Wales (GB) 1: £3,083
• EU Other 3: £8,600
• International: £8,600

Additional course costs

• 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.

Career Prospects

• 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).
• 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.

Modules

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.
• 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
• Assessment
  • Coursework: 0%
  • Examination: 0%
  • Practical: 100%
• Credits: 20
• Module Code: DSA8022
• Teaching Period: Spring
• Duration: 4 weeks
• Pre-requisite: No
• Core/Optional: Core

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.
• 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
• Assessment
  • Coursework: 65%
  • Examination: 0%
  • Practical: 35%
• Credits: 20
• Module Code: DSA8002
• Teaching Period: Autumn
• Duration: 4 weeks
• Pre-requisite: No
• Core/Optional: Core

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.
• 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.
• Assessment
  • Coursework: 0%
  • Examination: 70%
  • Practical: 30%
• Credits: 20
• Module Code: DSA8001
• Teaching Period: Autumn
• Duration: 4 weeks
• Pre-requisite: No
• Core/Optional: Core

Program Outline

Extracted Information from Queen's University Belfast's MSc Data Analytics PgCert program:

Degree Overview:

Introduction:

• Aims to equip graduates with key knowledge and skills in data analytics and data science.
• Offers a multi-disciplinary education in data analytics for employment in analytics and data science positions.

Learning Outcomes:

• Comprehensive knowledge of data analytics principles in statistics and computer science.
• Advanced knowledge and practical skills in theory and practice of analytics.
• Skills and tools for data analytics and data science careers.
• Skills in analytics practices and processes for commercial and research contexts.
• Exposure and experience in current software packages and new analytics applications.
• Opportunities to develop practical skills in a commercial context.

Program Structure:

• 60 credits from the well-regarded MSc Data Analytics program.
• Two modules in Semester 1, one module in Semester 2.
• All modules delivered online.
• Part-time in terms of modules taken, but modules taught full-time in block delivery mode.
• Intensive teaching week with morning lectures and afternoon labs.

Outline:

Semester 1 Modules:

• Data Analytics Fundamentals (20 credits): Introduction to data analytics, data collection, investigation, statistical concepts, hypothesis testing, regression analysis, and R software.
• Databases and Programming Fundamentals (20 credits): Data extraction, management, manipulation, integration with Python, introduction to SQL, procedural and object-oriented programming concepts.

Semester 2 Modules:

• Frontiers in Analytics (20 credits): Explores Visual Analytics and Behavioural Analytics, covering decision theory, surrogate modelling, visualization techniques, creation of dashboards, affective computing, social signal processing, biometrics, and behavior measurement.

Assessment:

• Coursework
• Examinations
• Practical assignments

Teaching:

• Delivered online
• Taught by experienced lecturers with expertise in data analytics
• Intensive teaching weeks with a mix of lectures and labs
• Use of R software and Python programming

Careers:

• Entry-level data analytics roles (e.g., junior data scientist, data processor)
• Career progression to graduate-level data scientist, data analyst, or data analytics modeller roles
• High demand for data analytics expertise in Northern Ireland and globally

Other:

• Aptitude test required for applicants with a 2.2 or non-cognate degree
• Northern Ireland residents may be eligible for AICC funding, covering full tuition fees
• International students have access to a range of scholarships
• Queen's University Belfast Postgraduates reap exceptional benefits including leadership and executive programs.
• Graduate Plus/Future Ready Award for extra-curricular skills

Additional Notes:

• Information in this extraction is based on the provided context.
• For the latest and most accurate information, please refer to the official Queen's University Belfast MSc Data Analytics PgCert program website.
• If you have any questions or require further assistance, please feel free to reach out to the Queen's University Belfast admissions office.

Summary:

This extraction provides a detailed and comprehensive overview of the MSc Data Analytics PgCert program offered by Queen's University Belfast. The program aims to equip graduates with the necessary knowledge, skills, and experience to thrive in the growing field of data analytics.

Tuition Fees and Payment Information:

For Northern Ireland (NI) residents:

• DfE Funded students: Free
• Other students: £2,934

For Republic of Ireland (ROI) residents:

£2,934

For England, Scotland, or Wales (GB) residents:

£3,083

For EU Other students:

£7,167

For International students:

£7,167

Important notes:

• The tuition fee for this course is not subject to an annual inflationary uplift.
• EU 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.
• EU students who are ROI nationals resident in ROI are eligible for NI tuition 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.

Additional course costs:

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

Payment Information:

Information on how to pay your tuition fees is available on the Queen's University Belfast website: https://www.qub.ac.uk/Study/Fees-and-Funding/How-to-pay/.