Programme Structure for MSc Computational Archaeology: GIS, Data Science and Complexity

The MSc Computational Archaeology draws on UCL's unparalleled concentration of expertise to equip students for future research or significantly enhance their employability.

Degree co-ordinator: Mark Lake

Other Major Contributors: Mark Altaweel, Andrew Bevan and Kris Lockyear 

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 students need the flexibility to learn on the job, leverage open data and program open source software.

Degree Handbook

• The degree handbook may be downloaded here»

Modules

The programme offers exceptional flexibility: students take two foundational core courses covering computer programming and data management, and complexity science; they then choose optional courses to further develop their knowledge and skills in techniques such as advanced landscape GIS, modern spatial and multivariate statistics, remote-sensing and geophysical prospection, web and mobile GIS.  An extended research project offers the possibility of bespoke training in additional leading-edge technologies such as agent-based modelling. 

A great virtue of the breadth of the programme is that students do not need to commit prematurely to an academic or vocational pathway, indeed, our alumni include lecturers at the University of Cambridge and the University of Colorado, professional archaeologists with the US National Parks Service, English Heritage and Oxford Archaeology, and analysts with companies including Deloitte, ESRI Ireland and Helyx Secure Information Systems.  Teaching typically occurs in small seminar groups and includes a combination of of theoretical and practical work, much of the latter taking place in the Institute's dedicated Archaeological & Geographical Information Systems Laboratory.

Careful provision is made to facilitate remote access to software, tutorials, datasets and readings through a combination of dedicated websites and virtual learning environments, remote thin-client access to UCL and Institute computing systems and the UCL library's extensive digital resources.

Core Modules

All students must take the following:

• Archaeological Data Science (ARCL0160, 15 credits, 11 weeks)
• Complexity, Space and Human History (ARCL0161, 15 credits, 11 weeks)

Option Modules

Students choose to follow further option modules up to the value of 60 credits from an outstanding range of Masters module options available at the UCL Institute of Archaeology. Out of this longer list of possibilities, some popular options for those following this degree in previous years have been:

• GIS Approaches to Past Landscapes (ARCL0095, 15 credits, 11 weeks)
• GIS in Archaeology and History (ARCL0094, 15 credits, 11 weeks)
• Geophysical Survey in Archaeology (ARCL0143, 15 credits, 11 weeks)  
• Exploratory Data Analysis in Archaeology (ARCL087, 15 credits, 11 weeks)  
• Web and Mobile GIS (UCL Dept. of Civil and Geomatic Engineering)
• Spatial Statistics, Network Analysis and Human History (ARCL0103, 15 credits, 11 weeks)
• Other options available within the UCL Institute of Archaeology

Please note not all modules are available every year.

Dissertation

(90 credits) - All students undertake an independent research project over a period of about 4 months. The topic may be chosen to provide a pilot study for further academic research or to showcase skills to potential employers. Part-time students working in archaeology may choose to analyse a data set made available by their employer. Students are typically allocated two supervisors to provide guidance during the dissertation research; depending on the topic this may involve intensive one-to-one tuition in advanced methods.

Examples of past projects include:

• agent-based simulation for visitor management of a candidate World Heritage Site in the Ukraine
• the integration of GIS and landscape phenomenology in the study of Italian Neolithic settlement landscapes
• cost-path analysis of Inca trade networks in Peru
• modelling of buried land-surfaces in the City of London
• a network model of the evolution of the British canal system
• use of remote-sensing data to monitor a World Heritage Site along the Silk Route
• spatio-temporal statistics for modelling the settlement patterns of Jomon hunter-gatherers in Japan