The MSc in Computational Finance will introduce students to the computational methods that are widely used by practitioners and financial institutions in today's markets. This will provide students with a solid foundation not only in traditional quantitative methods and financial instruments, but also scientific computing, numerical methods, high-performance computing, distributed ledgers, big-data analytics, and agent-based modelling. These techniques will be used to understand financial markets from a post-crisis perspective which incorporates findings from the study of financial markets at high-frequency time scales, modern approaches to understanding systematic risk and financial contagion, and disruptive technologies such as distributed-ledgers and crypto-currencies. The programme is highly practical, and students will have the opportunity to apply their learning to real-world data and case studies in hands-on laboratory sessions.
Computational Finance studies problems of optimal investment, risk management and trade execution from a computational perspective. As with any engineering discipline, computational finance analyses a given problem by first building a model for it and then examining the model. In computational finance, however, our model is typically analysed by running computer programs, rather than solving mathematical equations. In addition to standard computational methods such as Monte-Carlo option pricing, you will also learn more advanced modelling techniques such as agent-based modelling, in which the model itself takes the form of a computer program.
The programme will provide a foundation in the core skills required for successful risk management and optimal investment by giving a grounding in the key quantitative methods used in finance, including computer programming, numerical methods, scientific computing, numerical optimisation, and an overview of the financial markets. You can then go on to study more advanced topics, including the market micro-structure of modern electronic exchanges, high-frequency finance, distributed-ledger technology and agent-based modelling.
Students are expected to go in to careers such as Investment Banking, Hedge Funds and Regulatory Bodies.
The increasing integration of technology into our lives has created unprecedented volumes of data on everyday social behaviour. Troves of detailed social data related to choices, affiliations, preferences and interests are now digitally archived by internet service providers, media companies, other private-sector firms, and governments. New computational approaches based on machine learning, agent-based modelling, natural language processing, and network science have made it possible to analyse these data in ways previously unimaginable.
This is a chance to develop skills in computational techniques alongside a strong grounding in the principles and practice of contemporary social research. The programme’s quantitative methods training will help you harness complex data and use them to explore social theories and fundamental questions about societies. The programme’s theoretical and substantive training will introduce you to the principles of social inquiry and theories of human behaviour, and help you apply your technical skills to pressing social issues such as ethnic segregation in schools, income inequality, entrepreneurship, political change, and cultural diffusion.
During your first year you gain perspectives on the philosophy of social science, primers in the science of human decision-making, and frameworks for connecting individual behaviours to outcomes in social systems. You will also learn to apply advanced computational methods–including discrete choice modelling, social network analysis, agent-based simulation, and machine learning—to draw inferences about micro-level behaviours and macro-level outcomes.
With these building blocks in hand, you spend the third semester assembling critical knowledge of key theories and contemporary research in areas relevant to academic social science, government, and industry. During the third semester, you also have the option to study abroad at a partner institution.
In the final semester, you integrate the knowledge, skills, and theoretical approaches garnered in the first three semesters by writing a master’s thesis. As part of your thesis you conduct your own, original, computational research addressing a social scientific topic of your choosing.
Top archaeological researchers and heritage professionals use a raft of computational methods including GIS, data mining, web science, ABM, point-process modelling and network analysis. To impress employers you need the flexibility to learn on the job, leverage open data and program open source software. This MSc draws on UCL's unparalleled concentration of expertise to equip you for future research or significantly enhance your employability.
Students learn about a wide range of concepts that underpin computational approaches to archaeology and human history. Students become proficient in the archaeological application of both commercial and open source GIS software and learn other practical skills such as programming, data-mining, advanced spatial analysis with R, and agent-based simulation.
Students undertake modules to the value of 180 credits.
The programme consists of two core modules (30 credits), four optional modules (60 credits) and a research dissertation (90 credits).
All students undertake an independent research project which culminates in a dissertation of 15,000 words.
Teaching and learning
The programme is delivered through lectures, tutorials and practical sessions. Careful provision is made to facilitate remote access to software, tutorials, datasets and readings through a combination of dedicated websites and virtual learning environments. Assessment is through essays, practical components, project reports and portfolio, and the research dissertation.
Further information on modules and degree structure is available on the department website: Computational Archaeology: GIS, Data Science and Complexity MSc
Approximately one third of graduates of the programme have gone on to do PhDs at universities such as Cambridge, Leiden, McGill, Thessaloniki and Washington State. Of these, some continue to pursue GIS and/or spatial analysis techniques as a core research interest, while others use the skills and inferential rigour they acquired during their Master's as a platform for more wide-ranging doctoral research. Several graduates who went on to doctoral research are now lecturers in computational Archaeology: at the University of Cambridge, Queen's University Belfast and the University of Colorado. Other graduates have gone to work in a range of archaeological and non-archaeological organisations worldwide. These include specialist careers in national governmental or heritage organisations, commercial archaeological units, planning departments, utility companies, the defence industry and consultancies.
This degree offers a considerable range of transferable practical skills as well as instilling a more general inferential rigour which is attractive to almost any potential employer. Graduates will be comfortable with a wide range of web-based, database-led, statistical and cartographic tasks. They will be able to operate both commercial and oper source software, will be able to think clearly about both scientific and humanities-led issues, and will have a demonstrable track record of both individual research and group-based collaboration.
The teaching staff bring together a range and depth of expertise that enables students to develop specialisms including industry-standard and open-source GIS, advanced spatial and temporal statistics, computer simulation, geophysical prospection techniques and digital topographic survey.
Most practical classes are held in the institute's Archaeological Computing and GIS laboratory. This laboratory contains Linux servers, ten powerful workstations running Microsoft Windows 10, a digitising table and map scanner.
Students benefit from the collaborations we have established with other institutions and GIS specialists in Canada, Germany, Italy and Greece together with several commercial archaeological units in the UK.
The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.
The following REF score was awarded to the department: Institute of Archaeology
73% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)
Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.
The Spatial Data Science and Visualisation MSc teaches cutting-edge data analysis, mining, modelling and visualisation techniques for spatial systems. Students carry out their own research project, supported by academics, researchers and other students in one of the most exciting, interdisciplinary research teams in the field. The programme takes place within The Bartlett, UCL's Faculty of the Built Environment.
The programme consists of four core modules (60 credits), a group mini-project (30 credits), two elective modules (30 credits), and a dissertation (60 credits).
The core modules focus on technical skills, leading to applications in mapping, visualising and analysing spatial data.
Students select two elective modules from a wide range available at UCL, subject to approval.
All students submit a dissertation of 10-12,000 words.
Teaching and learning
The programme is delivered through a combination of lectures, seminars, tutorials and practical-based workshops and classes. The interlinked laboratory research-based mini project with data collection focuses on ‘remote data mining’ rather than fieldwork in the traditional planning/geographical/architectural sense. Assessment is through group and individual projects and the dissertation.
Further information on modules and degree structure is available on the department website: Spatial Data Science and Visualisation MSc
For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.
Recent graduates of our related Spatial Data Science and Visualisation MRes have gone on to work as developers, in spatial analysis, and a number have continued to PhDs. Through our PhD partners, Knowledge Transfer Partnerships and substantial outreach, graduates will be able to take advantage of CASA's links to the world outside academia.
The Spatial Data Science and Visualisation MSc provides a unique skill set in computation mapping, visualisation and spatial research. Research-led skills are increasingly a key element in our understanding of complex spatial functions, particularly as vast amounts of previously unused data are becoming available either from changes in accessibility regulation or more widely as a result of new mass data collection methodologies.
The Centre for Advanced Spatial Analysis (CASA) is a research centre specialising in computational and mathematical approaches, with cutting-edge research in GIS, urban simulation, mapping, data visualisation, and 3D environments in cities and space.
Students on this programme will be exposed to a range of programming languages (Java/Processing, R, Python and MySQL), 3D visualisation packages, and be given a substantive grounding in GIS, programming structure, mathematical methods and data design.
The combination of skills involved in this programme is unique – graduates will be able to lead institutions and companies in new directions and be involved in changing cultures across the sector.