Big Data Science (Subject to approval*)

MSc ( 2 years Part-time Online Learning )

Overview

Due to the popularity of this programme, we encourage you to apply early. Based on past experience, it is very likely that we will stop considering applications by July 2020


*All new courses are required to undergo a two-stage internal review and approval process before being advertised to students. Courses that are marked "subject to approval" have successfully completed the first stage of this process. Applications are welcome but we will not make formal offers for this course until it has passed this second (and final) stage.

Structure

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

Full-time

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

Part-time study options often mean that you take 4 modules 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.

Important note regarding Part Time Study

We regret that, due to complex timetabling constraints, we are not able to guarantee that lectures and labs for part time students will be limited to two days per week, neither do we currently support any evening classes. If you have specific enquiries about the timetabling of part time courses, please contact the MSc Administrator.

Semester 1

Two compulsory modules:

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

Semester 2

One compulsory module:

  • Data Analytics (15 credits)

Select one option from:

  • The Semantic Web (15 credits)
  • Digital Media and Social Networks (15 credits)

Year 2

Semester 1

One compulsory module:

  • Big Data Processing (15 credits)

Select one option from:

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

Semester 2

Select two options from:

  • Bayesian Decision and Risk Analysis (15 credits)
  • Cloud Computing (15 credits)
  • Deep Learning and Computer Vision (15 credits)

Semester 3

  • Project (60 credits)

Please note that modules are subject to change.

Further information

Visit the School of Electronic Engineering and Computer Science website.

Contact:

Virginia Elgar, Postgraduate Administrator
School of Electronic Engineering and Computer Science
Tel: +44 (0)20 7882 7333
email: msc-enquiries@eecs.qmul.ac.uk

Entry requirements

An upper second class degree is normally required, usually in electronic engineering, computer science, maths or a related discipline. Students with a good lower second class degree may be considered on an individual basis. Applicants with unrelated degrees will be considered if there is evidence of equivalent industrial experience.

For international students we require English language qualifications IELTS 6.5 or TOEFL 92 (internet based).

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We will not be accepting applications onto our MSc Big Data Science programme for 2019/20 entry from the 17th June.

We do however have an MSc Data Analytics programme run by the School of Mathematical Sciences that you may be interested in applying for.

This Data Analytics MSc will teach you the core mathematical principles of data analysis and how to apply these to real world scenarios. Building on the statistical foundations of machine learning you’ll then choose from module options which explore the financial, business and scientific applications; such as in trading and risk systems, optimisation of business processes, and relationships across complex systems. 

Further information on this programme, including the structure and entry requirements can be found at www.qmul.ac.uk/msc-data-analytics

If you have any further queries about the MSc Data Analytics programme please contact maths@qmul.ac.uk

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Learning and teaching

As a student at Queen Mary, you will play an active part in your acquisition of skills and knowledge. Teaching is by a mixture of formal lectures and small group seminars. The seminars are designed to generate informed discussion around set topics, and may involve student presentations, group exercise and role-play as well as open discussion. We take pride in the close and friendly working relationship we have with our students. You are assigned an Academic Adviser who will guide you in both academic and pastoral matters throughout your time at Queen Mary.

Teaching for all modules includes a combination of lectures, seminars and a virtual learning environment. Each module provides 36 hours of contact time, supported by lab work and directed further study.

Independent study

For every hour spent in classes you will be expected to complete further hours of independent study. Your individual study time could be spent preparing for, or following up on formal study sessions; reading; producing written work; completing projects; and revising for examinations.

The direction of your individual study will be guided by the formal study sessions you attend, along with your reading lists and assignments. However, we expect you to demonstrate an active role in your own learning by reading widely and expanding your own knowledge, understanding and critical ability.

Independent study will foster in you the ability to identify your own learning needs and determine which areas you need to focus on to become proficient in your subject area. This is an important transferable skill and will help to prepare you for the transition to working life.

Assessment

Modules are assessed through a combination of coursework and written examinations. You will also be assessed through an individual project.

Dissertation

The MSc research project will be conducted under close supervision throughout the academic year, and is evaluated by thesis, presentation and viva examination.

Fees

Tuition fees

Fees are charged at a Home/EU rate for UK and EU nationals, and an overseas rate for International students - find out more about how your tuition fee status is assessed.

Part time fees are charged per annum over two years for a two year programme and per annum over three years for a three year programme. A percentage increase may be applied to the fees in years two and three.

This increase is defined each year and published on the intranet and in the Tuition Fee Regulations. A 3% increase was applied to the unregulated university fees in 2019/20. Further information can be viewed on our University Fees webpage, including details about annual increases.

Funding

There are a number of sources of funding available for Masters students.

These include a significant package of competitive Queen Mary University of London (QMUL) bursaries and scholarships in a range of subject areas, as well as external sources of funding.

Queen Mary bursaries and scholarships

We offer a range of bursaries and scholarships for Masters students including competitive scholarships, bursaries and awards, some of which are for applicants studying specific subjects.

Find out more about QMUL bursaries and scholarships.

Alternative sources of funding

Home/EU students can apply for a range of other funding, such as Professional and Career Development Loans, and Employer Sponsorship, depending on their circumstances and the specific programme of study.

Overseas students may be eligible to apply for a range of external scholarships and we also provide information about relevant funding providers in your home country on our country web pages.

Download our Postgraduate Funding Guide for detailed information about postgraduate funding options for Home/EU students.

Read more about alternative sources of funding for Home/EU students and for Overseas students.

Tel: +44 (0)20 7882 5079
email bursaries@qmul.ac.uk

Other financial help on offer at Queen Mary

We offer one to one specialist support on all financial and welfare issues through our Advice and Counselling Service, which you can access as soon as you have applied for a place at Queen Mary.

Our Advice and Counselling Service also has lots of Student Advice Guides on all aspects of finance including:

Tel: +44 (0)20 7882 8717

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