Data Science with specialisation in Computer Science

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.