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