Masters | Postgraduate Course - Big Data Science with Industrial Experience MSc Course Details within Queen Mary University of London

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Course Fees
Home and EU students 2018/19 Academic Year Thick Sandwich £9,250 International students 2018/19 Academic Year Thick Sandwich £19,500
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Contact Name
School of Electronic Engineering and Computer Science
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Big Data Science with Industrial Experience MSc

Study Details

MSc 2 years Thick Sandwich

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.

Module details

 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.

Entry Requirements

You should have a good Honours degree (first or upper-second class honours) in electronic engineering, computer science, mathematics, or a related discipline. Applicants with unrelated degrees will be considered if there is evidence of equivalent industrial experience.

Programme 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.