Overview
Data Analytics is an exciting field of rapid developments. Data is everywhere and continuing to grow massively, creating huge growth in demand for qualified experts to be able to extract the real benefit from the data.
The role of a data scientist is highly diverse overlapping many areas from computer science, to the fundamentals of mathematics, statistics, modelling and analytics while also requiring the right skills to be able to see the detail, solve the problem (having specified the problem!), and communicate effectively the findings to colleagues to empower them to make decisions.
The diversity of data analytics opens up many job opportunities from working in software companies, healthcare, banking, insurance, policing, tech companies to applying your knowledge to intelligent buildings and behaviour analytics of customers.
The programme provides a balanced route to learning through a blend of academic study and lab sessions, with a heavy focus on practical engagement with industry. In the first and second semesters, you will study 6 modules full-time which include opportunities for blended and collaborative learning. In the third semester you will undertake a significant industry based project.
Data Analytics highlights
Please note: Applications for this course, received after 30th June may not be accepted. A deposit will be required to secure a place.
Industry Links
•Special features of the course include the Analytics in Action module and the commitment of industry to provide real data for "Analytathons" and projects. The module offers real world examples of data analytics presented by the industry experts working alongside the academics who will provide the theory, and the unique provision of this course across many academic disciplines in the University.
World Class Facilities
•The Frontiers in Analytics module shows off some of latest state-of-the-art techniques analytics in particular in Visual Analytics and Behavioural Analytics.
Student Experience
•This course is unique having been developed from engagement with industry rather than the traditional academic subject areas. The key core skills that a data scientist needs have been clearly defined and forms the basis for the course. As a result, there are no optional modules or choice as it is essential that in order to produce the "all rounded" data scientist that all these skills are packaged into each individual.
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.
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.
Weeks 3 and 4 of each block are for project and coursework. Full-time students are expected to be present at Queen's during weeks 2, 6, 10, 14, 18 and 22 of the academic year.
In the four week duration of a module, there will be an intensive week the schedule will consists of 9am-5pm with approximately equal numbers of lectures (in the mornings) and labs (in the afternoons).