City, University of London
Data science is the area of work concerned with the extraction of insight from large collections of data. The course will prepare students for a successful career as a data scientist and will develop specialist skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation, which are in high demand. The course covers the study and integration of advanced methods and techniques from data analysis and machine learning, data visualisation and visual analytics, high-performance, parallel and distributed computing, knowledge representation and reasoning, neural computation, signal processing, data management and information retrieval. It will enable students to specialise in an application area of data science, from health to retail, and engage with researchers and industrial partners.
Applicants will normally have a UK 1st class or an upper 2nd class Honours degree (or equivalent) in computing, engineering, physics or mathematics. Or a UK 1st class or an upper 2nd class Honours degree (or equivalent) in business, economics, psychology or health, with a demonstrable mathematical aptitude. Or a UK lower second class Honours degree (or equivalent) with a demonstrable mathematical aptitude or relevant work experience. For those students whose first language is not English, one of the following qualifications is also required: IELTS: 6.5 (minimum of 6.0 in all four components); TOEFL (internet based): 90.
Core modules: introduction to data science: understand the foundations of the DS process, methods and techniques; represent and organise knowledge about large heterogeneous data collections; use mathematical models and tools for large-scale data analysis and reasoning; critically evaluate the choice of DS techniques and tools for particular scenarios. Machine learning: understand the workings of important DS algorithms for learning under uncertainty; rationally exploit both statistical and machine learning approaches in applications; rigorously assess the validity of inferences and generalisations; critically evaluate the choice of algorithms for specific scenarios and requirements. Big data: understand the theory and techniques for data acquisition, cleansing, and aggregation; identify and understand the principles and functionalities of Big Data programming models and tools; acquire, process and manage large heterogeneous data collections; develop algorithms and systems for information and knowledge extraction from large data collections. Neural computing: understand how to use neural computing and deep learning in an application domain; select and apply supervised, unsupervised and hybrid neural networks to different problems and data types; critically evaluate a range of neural systems in comparison with a number of learning techniques; design and implement neural network models; apply them and evaluate their performance. Visual analytics: learn the principles and rules underlying the design of visual data representations and human-computer interactions; understand, adapt and apply representative visual analytics methods and systems for diverse types of data and problems; analyse and evaluate the structure and properties of data to select or devise appropriate methods for data exploration; combine visualization, interactive techniques, and computational processing to develop practical data analysis for problem solving. Research methods and professional issues: understand and apply research methodologies such as inductive and deductive reasoning, explanation and prediction; recognise and apply the scientific method and a range of secondary data sources when performing a research task; communicate effectively with individuals and groups using a range of media; evaluate the legal, ethical and professional dimensions of typical information professions and information industry practices. Electives:advanced programming: concurrency; readings in computer science; advanced database technologies; information retrieval; data mining; data visualisation; information and knowledge management; data analysis in healthcare; computer graphics; digital signal processing; service oriented architectures.
|Qualification||Study mode||Fee||Course duration|
|MSc||Full-time||£ 9,000 per Academic year (home fees)||1 years|
|MSc||Full-time||£ 14,500 per Academic year (overseas fees)||1 years|
|Campus name||Town||Postcode||Region||Main campus||Campus||Partner|
|Northampton Square||Islington||EC1V 0HB||South East|
020 7040 5060