POSTGRAD PLACEMENTS. Placements are an integral part of the Masters programmes, so international students will not require additional work visas.

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1 POSTGRAD PLACEMENTS COMPUTATIONAL FINANCE DATA SCIENCE AND ANALYTICS MACHINE LEARNING KEY INFORMATION Placements can start in the middle of June 2015 or later and must finish by the middle of June 2016 at the latest. Placements are an integral part of the Masters programmes, so international students will not require additional work visas. Only students with good academic performance will be placed. All students will have acquired a solid background and practical skills in handling and analysing data. Students will have been taught by world experts in Machine Learning and Distributed Systems/Cloud Computing.

2 THE UNIVERSITY Royal Holloway is a research-intensive university, ranked 102nd in the World (12th in the UK) by the Times Higher Education World University Rankings We were placed 1st in the UK and Europe and 8th in the World for citations (citations measure the influence of publications on the research community). Computer Science is an elite department with world-leading researchers in algorithms, artificial intelligence, bioinformatics, distributed and global computing, machine learning, software language engineering and type theory. In the latest Research Assessment Exercise, we ranked 11th in the UK for the quality of our research output. We have a broad network of industrial and government collaborators. We are renowned for our expertise in the area of Machine Learning, in particular in kernel methods, prediction with expert advice, reinforcement learning, and conformal prediction. Vladimir Vapnik and Alexey Chervonenkis, the inventors of vector support machines, are now Emeriti Professors; Alex Gammerman, Volodya Vovk and Chris Watkins are among our senior staff. We are also involved in industrial applications of machine learning, including in energy, transport, medicine and finance. bigdata.aspx

3 BIG DATA BIG DATA IS NOW PART OF EVERY SECTOR AND FUNCTION OF THE GLOBAL ECONOMY. DECISION-MAKING PROCESSES RELY ON LARGE POOLS OF DATA THAT NEED TO BE CAPTURED, AGGREGATED, STORED, AND ANALYSED. PEOPLE WITH THE RIGHT SET OF SKILLS DATA SCIENTISTS ARE IN HIGH DEMAND. Placement students are an excellent opportunity for companies to test the waters or obtain highly-qualified members on their data analytics teams for 1 year. They help discover if students would make suitable long-term hires. GUIDANCE FOR EMPLOYERS Designate one member of staff to serve as the Placement Supervisor and as a point of contact with the Department. Agree with the Department procedures for monitoring the attendance of Tier-4 Visa students while they are on their placement. A Placement Handbook, detailing responsibilities for students and employers, is available on request. Agree a programme of work with the student and their Academic Adviser in the Department, and agree in advance terms of employment including hours of work and salary. Ensure that Health and Safety regulations are complied with during the placement, and organise appropriate training and induction sessions at the start.

4 SKILLS ACQUIRED This list summarises the skills students will acquire during their studies, depending on which course units they take. Students are expected to have experience in an object-oriented language such as Java. A highly analytical approach to problem solving and a strong background in data modelling and business intelligence. Ability to develop, validate, and use effectively machine learning models and statistical models. Knowledge of and ability to work with software to automate tasks and perform data analysis. Knowledge of and ability to work with structured, unstructured, and time-series data. Knowledge of and ability to work with methods and techniques such as clustering, regression, support vector machines, boosting, decision trees, and neural networks. Appreciation and knowledge of non-statistical approaches to data analysis and machine learning. Ability to work with software packages such as MATLAB and R. Knowledge of and ability to work with Relational Database Systems and SQL. Understanding of and ability to work with highly-scalable data-storage paradigms, such as NoSQL Data Stores (MongoDB, Cassandra, HBase,...) and Distributed Hash Tables. Knowledge of and ability to work with modern tools for massively distributed data processing, such as Hadoop and Pig. Familiarity with Cloud Computing tools for large-scale data storage and processing (such as Amazon S3, EC2 and Elastic MapReduce). Students in Computational Finance will apply methods of computational finance to practical problems, including pricing of derivatives and risk assessment. Students will have the opportunity to take courses in Information Security, Mathematics and Economics.

5 For more information contact: +44 (0) computerscience/ prospectivestudents/ postgraduatetaught/ bigdata.aspx