UCL (University College London)

Course Introduction

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. The courseprovides a principled understanding of the computational and statistical underpinnings of current and emerging methods.

Course Modules

Information retrieval and data mining; introduction to statistical data science; applied machine learning; introduction to supervised learning; machine vision; cloud computing; web economics; statistical natural language processing; programming and mathematical methods for machine learning; financial data and statistics; statistical models and data analysis; applied Bayesian methods; decision and risk; data analytics; supervised learning; graphical models; bioinformatics; affective computing and human'robot interaction; computational modelling for biomedical imaging; stochastic systems; forecasting.

Course Additional Entry

An upper 2nd Class Bachelor's degree (or equivalent overseas qualification) 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.

Duration & Attendance Qualification Tuition fees
1 year
Full Time
MSc (Postgraduate)