Data science brings together computational and statistical skills for data'driven problem solving. This rapidly expanding area includes machine learning, deep learning, large'scale data analysis and has applications in e'commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence.
Applied machine learning; information retrieval and data mining; introduction to machine learning; introduction to statistical data science; applied bayesian methods; business analytics; cloud computing; decision and risk; financial data and statistics; machine vision; programming and mathematical methods in machine learning; statistical models and data analysis; web economics; affective computing and human'robot interaction; bioinformatics; computational modelling for biomedical imaging; forecasting; graphical models; stochastic systems; supervised learning.
Course Additional Entry
A minimum of an upper 2nd class Honours degree in a quantitative discipline (such as mathematics, computer science, engineering, physics or statistics) from a UK university or an overseas, qualification of an equivalent standard. Knowledge of mathematical methods including linear algebra and calculus at first'year university level is required. Depending on the modules selected, students undertake assignments that contain programming elements and prior experience in a high'level programming language (R/matlab/python) is useful. Relevant professional experience will also be taken into consideration.
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