This programme's emphasis on independent research allows you to work closely with scholars who are leaders in their field.
Research may be in any area of social, urban, environmental, development, political, economic, historical or cultural geography that is supported by the Human Geography Research Group. It is co-delivered with the University’s Graduate School of Social Science.
The programme can stand alone as a masters degree, or form the first year of a ‘1+3’ ESRC-backed PhD programme.
Students who successfully complete this programme will:
This programme is affiliated with the University's Global Environment & Society Academy.
We offer a balance between general and specialist research training. The programme combines lectures, practical work, workshops, essays, seminars and one-to-one supervision of independent research leading to delivery of a dissertation.
In consultation with the Programme Director, you will choose from a range of option courses. We particularly recommend:
The emphasis on independent research allows you to work closely with scholars at the cutting edge in order to advance your own research passions. A highlight of the programme is the postgraduate conference where you present your research to colleagues.
The University of Edinburgh has an unbroken record of teaching and research in the earth sciences going back to 1770, when Robert Ramsay became the first Professor of Natural History.
James Hutton and Arthur Holmes were prominent among those who set an academic tradition in Edinburgh that continues today with the University achieving top ratings in earth sciences teaching and research.
Our interactive and interdisciplinary research environment allows us to tackle difficult research questions, from causes of past glaciations to interactions of earth, climate and society. The ambition and quality of our research was reflected in the latest Research Assessment Exercise: 66 per cent of our research was rated within the top two categories – world-leading and internationally excellent.
Our location at the King’s Buildings campus – home to most of the University’s science and engineering research – benefits our work too. Our King’s Buildings neighbours include external institutes such as the British Geological Survey; our proximity to them strengthens these research links.
As a research student, you will be affiliated to one of our research institutes, benefiting from an excellent peer-supported network.
As groupings of researchers with related interests, the institutes provide a forum for development of ideas, collaboration, and dissemination of results, and an environment for training, development and mentoring of research students and early career researchers.
The School receives strong backing from industry, particularly in areas such as hydrocarbons and carbon capture and storage. We receive support from the EU and from major UK research councils, including the Engineering and Physical Sciences Research Council and the Economic and Social Research Council.
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.
Big data and quantitative methods are transforming political processes and decisions in everyday life. Local, national and international administrations are making "open data" available to wide audiences; giant, world-level web organisations are putting more and more "services" in synergy (search, map, data storage, data treatment, trade, etc.); and some private companies or governments are developing strongly ideological projects in relation with big data, which may have major consequence on the means by which we are ruled. All these issues involve data in text, image, numeric and video formats on unprecedented scales. This means there is a growing need for trained specialists who will have the cpacity to compete and/or collaborate with strictly business or technique-oriented actos on the basis of sound knowledge from political and international studies.
In contrast to degrees such as Data Science or Data Analytics, where the focus ends up being almost exclusively on data practices and computational tools, the MA in Big Data and Quantitative Methods provides you with a knowledge and understanding of the central and innovative quantitative approaches in political science, the debates they have generated, and the implications of different approaches to issues concerning big data and public policy. The MA also draws on the considerable expertise which Warwick now has in quantitative methods located in PAIS, Sociology, the Centre for Interdisciplinary Methodologies (CIM) and the Q-Step Centre.
Given that a noteworthy part of big data is actually social data, this MA programme seeks to attract students from a variety of social science-related disciplines, including politics, sociology, philosophy and economics; you do not need a background in statistics to be eligible for the course. Students are required to take three core modules: Fundamentals in Quantitative Research Methods (previously Quantitative Data Analysis and Interpretation); Big Data Research: Hype or Revolution?, and Advanced Quantitative Research, and have a range of optional modules to choose from in PAIS or from other departments across Warwick including Law, Philosophy, Sociology and the CIM. Graduates of this degree will be able both to engage technically with data released at a new scale and to keep a critical expertise on their relevance and quality, skills which are increasingly required in the competitive global job market.
In addition to regular modules, the Warwick Q-Step Centre is offering a range of different masterclasses. Topics include Reproducibility, Quantitative text analysis, Web data collection, Geostatistics, Inferential network analysis, Machine learning, Agent-based simulation and Longitudinal data analysis. All masterclasses are designed as comprehensive but gentle introductions to methods that are not covered at length in core method modules. They are intended to broaden your horizons and provide concepts and tools to be applied in your future research.
This module on research methods and statistics, particularly relevant to social and healthcare sciences, intends to help students become informed recipients of research evidence and able to understand its application to practice. The module will afford students the opportunity to experience data analysis and to hone their critical appraisal skills.
The aims of this module are (1) to facilitate the development of lifelong learning with respect to the research process by introducing you to research approaches and methodology; and (2) to prepare you for Master’s-level research projects.
Having successfully completed this module you will be able to:
• Research concepts
• Research audit and service improvement,
• Research Governance,
• Risk assessment in research protocols,
• Evidence Based Research
• Literature searching and reference managing,
• Practical collection of data
• Principles of qualitative and quantitative research, samples and populations, scales and categories of measurement, validity, errors and bias, control groups and matched controls.
• Between-subject and within-subject designs,
• Defining experimental hypotheses,
• Statistical methods for description and hypothesis testing,
• Concept of statistical power,
• Calculation of sample size,
• Measures and tests of association,
• Reading research publications critically,
• Writing a critical review of the research literature
• Interpretation and evaluation of qualitative data
• Principles of safety and ethics in research.
• Writing a critical review of the research literature
• Succinct reporting of findings