Msc big data science with industrial experience electronic engineering

Structure

MSc Big Data is currently available for one year full-time study, two years part-time study.

The programme is organised in three semesters. The first semester is composed by three core modules plus one optional module that cover the foundational techniques and tools employed for Big Data Science analysis.

The second semester has four modules that are chosen among a set of options. The module selection allows students to focus on domain-specific research or industry applications for Big Data Science. Module options allow students to specialize in several areas: Computer Vision, Internet Services (Semantic Web and Social Media), Business, and Internet of Things.

Students carry out a large project full time in the third semester, after agreeing to a topic and supervisor in the first semester, and completing the preparation phase over the second semester.

Undertaking a masters programme is a serious commitment, with weekly contact hours being in addition to numerous hours of independent learning and research needed to progress at the required level. When coursework or examination deadlines are approaching independent learning hours may need to increase significantly. Please contact the course convenor for precise information on the number of contact hours per week for this programme.

Part-time study options often mean that the number of modules taken is reduced per semester, with the full modules required to complete the programme spread over two academic years. Teaching is generally done during the day and part-time students should contact the course convenor to get an idea of when these teaching hours are likely to take place. Timetables are likely to be finalised in September but you may be able to gain an expectation of what will be required.

The modules listed below provide some general guidance on what you may be expected to learn during each semester and year of this degree. The exact modules available may vary depending on staff availability, research interests, new topics of study, timetabling and student demand.

Semester 1

• Applied Statistics (15 credits)
• Big Data Processing (15 credits)
• Data Mining (15 credits)

Select one option from:

• Machine Learning (15 credits)
• Introduction to IOT (15 credits)
• Semi-Structured Data and Advanced Data Modelling (15 credits
• Introduction to Object Oriented Programming (15 credits)

Semester 2

Four options from:

• The Semantic Web (15 credits)
• Digital Media and Social Networks (15 credits)
• Bayesian Decision and Risk Analysis (15 (credits)
• Cloud Computing (15 credits)
• Data Analytics (15 credits)
• Deep Learning and Computer Vision (15 credits)
• Machine Learning for Visual Data Analytics (15 credits)

Semester 3

• Project (60 credits)

Please note module availability is subject to change.

We aim to deliver your programme so that it closely matches the way in which it has been described to you by QMUL in print, online, and/or in person. Please be assured that we review our modules on a regular basis, in order to continue to offer innovative and exciting programmes.

Please check the School website for further module information.

Contact:

Jennifer Richards, Postgraduate Administrator
School of Electronic Engineering and Computer Science
Tel: +44 (0)20 7882 7333
email: