Big Data Science

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.

Students will be offered lectures that explain the core concepts, techniques and tools required for large-scale data analysis. Laboratory sessions and tutorials will put these elements to practice through the execution of use cases extracted from real domains. Students will also undertake a large project where they will demonstrate the application of Data Science skills in a complex scenario.

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.

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.

Core modules

• Big Data Processing
• Data Mining
• Applied Statistics
• MSc Project

Option modules

• Advanced Program Design
• Advanced Database System Technology
• Sensors and the Internet of Things
• Business Technology Strategy
• Techniques for Computer Vision
• The Semantic Web
• Information Retrieval
• Digital Media and Social Networks
• Machine Learning
• Introduction to Computer Vision

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).
• Degree transcripts. Please provide a transcript of your degree(s). If you have not yet completed your degree please provide a transcript of your results achieved to date. 
• If your degree was from an overseas institution, you should supply a transcript of your marks for each year of your studies and a copy of your degree certificate together with a certified translation if the document is not in English. Please note that original documentation will be required before you enrol. International applicants are also advised to include high school transcripts
• Please provide the contact details of two referees, at least one reference must be from an academic referee who is in a position to comment on the standard of your academic work and suitability for postgraduate level study. Where appropriate, a second referee can provide comment on your professional experience.
• Curriculum Vitae (CV)/ Resume
• Statement of purpose. Your statement of purpose should explain why you want to study your chosen programme and how it will help your life and career. This should typically be one side of A4 paper.

Scholarships

• Global Education