StudyQA — Master: Data Science with specialisation in Computer Science

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Description Contents Requirements

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 programme comprises core machine learning methodology and an introduction to statistical science, combined with a set of more specialised and advanced options covering computing and statistical modelling. Projects are offered both within UCL Computer Science and from a wide range of industry partners.

Students undertake modules to the value of 180 credits.

The programme consists of three compulsory modules (45 credits), five optional modules (75credits) and a dissertation/report (60 credits).

Core modules

Applied Machine Learning
Introduction to Machine Learning
Introduction to Statistical Data Science

Optional modules

Students choose a minimum of 30 credits and a maximum of 60 credits from the following optional modules.
Cloud Computing (Birkbeck)
Machine Vision
Information Retrieval & Data Mining
Statistical Natural Language Processing
Web Economics
Students choose a minimum of 0 credits and a maximum of 30 credits from these optional Statistics modules.
Statistical Design of Investigations
Applied Bayesian Methods
Decision & Risk
Students choose a minimum of 15 credits and a maximum of 15 credits from these elective modules.
Supervised Learning
Graphical Models
Bioinformatics
Affective Computing and Human-Robot Interaction
Computational Modelling for Biomedical Imaging
Stochastic Systems
Forecasting

Dissertation/report

All students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning

The programme is delivered though a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed through a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.

A minimum of an upper second-class Bachelor's 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.