MSc Big Data Science

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Organisation
Queen Mary University of London
Start - End
12 Sep
Study Options
Full Time
Fee
GBP 11850 - 11850
Fee International
GBP 27250 - 27250

This programme is designed for those who want to pursue a career as data scientists, deriving valuable insights and business relevant information from large amounts of data. You will cover the fundamental statistical (eg machine learning) and technological tools (eg cloud platforms, Hadoop) for large-scale data analysis.

The Big Data science movement is transforming how Internet companies and researchers over the world address traditional problems. Big Data refers to the ability of exploiting the massive amounts of unstructured data that is generated continuously by companies, users, devices, and extract key understanding from it.

A Data Scientist is a highly skilled professional, who is able to combine state of the art computer science techniques for processing massive amounts of data with modern methods of statistical analysis to extract understanding from massive amounts of data and create new services that are based on mining the knowledge behind the data. The job market is currently in shortage of trained professionals with that set of skills, and the demand is expected to increase significantly over the following years.

The course leverages the world-leading expertise in research at Queen Mary with our strategic partnership with IBM and other leading IT sector companies to offer to students a foundational MSc on the field of Data Science. The MSc modules cover the following aspects:

  • Statistical Data Modelling, data visualization and prediction
  • Machine Learning techniques for cluster detection, and automated classification
  • Big Data Processing techniques for processing massive amounts of data
  • Domain-specific techniques for applying Data Science to different domains: Computer Vision, Social Network Analysis, Bio Engineering, Intelligent Sensing and Internet of Things
  • Use case-based projects that show the practical application of the skills in real industrial and research scenarios.

The programme is offered by academics from the Networks, Centre for Intelligent Sensing, Risk and Information Management, Computer Vision and Cognitive Science research groups from the School of Electronic Engineering and Computer Science. This is a team of more than 100 researchers (academics, post-docs, research fellows and PhD students), performing world leading research in the fields of Intelligent Sensing, Network Analytics, Big Data Processing platforms, Machine Learning for Multimedia Pattern Recognition, Social Network Analysis, and Multimedia Indexing.

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.

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.

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.

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.

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.



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