This course will equip student with the some basic mathematical knowledge and problem solving skills. The course is intended to give students: a basis for the analysis and interpretation of quantitative information an understanding of the basic ideas underlying statistical methods at an introductory level an understanding of how to overcome problems when analysing big data sets. fter a recap of SQL, this course takes you through the various NoSQL databases, including document stores like MongoDB, column stores like Cassandra and graph databases such as Neo4j. You'll learn to pick the right database for your application and how to build, search and distribute the data in them. You'll learn the practicalities of big data analytics with techniques from data mining, machine learning, statistics, data visualisation and web analytics. You’ll explore how we’re training computers to understand the present and predict the future with data from finance, marketing and social media. This course covers distributed data processing with Hadoop and MapReduce in addition to the use of Condor for distributed computation. With guest lectures from science and industry, this course presents a set of case studies of Big Data in action. You'll learn first-hand how companies are using big data in fields such as banking, travel, telecoms, genetics and neuroscience.

A minimum of a second class honours degree or equivalent in a numerate subject such as maths, computing, engineering or an analytic science. Applicants without these formal qualifications but with significant appropriate work experience are encouraged to apply. English language requirements: IELTS 6.0 with 5.5 in each skill; Pearson Test of English (Academic) 54 overall with a minimum of 51 in each sub-skill; IBT TOEFL 78 overall with a minimum of 17 in listening, 18 in reading, 20 in speaking and 17 in writing.