Curating Science will enable you to develop an independent academic and curatorial practice at the intersection of histories, philosophies and social studies of science, science communication and museum studies.
You will engage with current debates in science communication and interpretive practice in museums, including cutting-edge art-science practices that are reimagining ways of knowing and being in the 21st Century. Alongside this, you will be encouraged to develop innovative practices of dialogic and participative engagement, developing their own ways of convening public spaces for debate.
You will undertake a range of active learning activities from developing displays, programmes and events to developing digital content and designing their own research projects. You will be supported throughout by an interdisciplinary academic staff team drawn from museum and curatorial studies and the histories and philosophies of science, as well as professionals from our partner institutions.
Students can specialise in their own areas of interest, through choosing from an array of optional modules that explore contemporary curatorial strategies, technologies and media, cultural memory, histories of medicine, audiences, participation and engagement. You will have the option of undertaking a negotiated placement with a museum or heritage organisation.
All students on the MA in Curating Science will take three core modules.
The History and Theory of Modern Science Communication allows students to explore how science, technology and medicine have been communicated to a wider public in the past. Students will identify how the processes and purposes of science communication has changed over the last two centuries and debate the consequences for science communication of the introduction of new media, ranging from the radio to the internet. The module addresses these questions by surveying the development of science communication since 1750, and by examining the changing theoretical perspectives that have underpinned these developments. Students will learn to re-examine the processes of contemporary science communication in the light of a deeper understanding of this history.
Interpreting Cultures is underpinned by action learning and puts contemporary curation in an international context. From the outset, students work on an interpretation intervention with one of the archives and collections on campus (such as The Stanley & Audrey Burton Gallery; Special Collections; Treasures of the Brotherton; Marks and Spencer Company Archive; ULITA ― an Archive of International Textiles; Museum of the History of Science, Technology and Medicine). This intensive experience of project planning, management, collaboration and team working prepares students for the option of undertaking a negotiated work placement in the second semester or optional modules exploring audiences, participation or engagement.
Through our Advanced Research Skills modules, students are equipped to undertake assessments and ultimately develop their own research project. The modules build to a symposium in Semester 2 where students present initial research findings towards a dissertation on a research topic of interest.
In addition, students choose from a range of optional modules offered by the School of Fine Art, History of Art and Cultural Studies and the School of Philosophy, Religion and History of Science. These include the opportunity to complete a placement or consultancy project role in either curational approaches or engagement.
You will be taught by leading researchers and experienced practitioners in their fields, and you’ll benefit from a range of teaching and learning methods. They include lectures and seminars, gallery and museum visits, as well as hands-on experience of specific collections in library sessions.
We use a range of assessment methods including essays, presentations, assignments and literature reviews among others, depending on the modules you choose.
Through a combination of theory and practice, the programme produces graduates who are able to develop professional careers in the museums and heritage sector whilst retaining a critical and reflexive eye on their own practice and that of the institutions in which they work. It will equip you with a good understanding of the issues and approaches to science communication and curation, interpretation and engagement, as well as practical work experience ― a combination which is very valuable to employers.
To get a flavour of the kinds of career trajectories our graduates of allied MAs have taken see the ‘news’ section of the Centre for Critical Studies in Museums, Galleries and Heritage and the alumni pages of the School website.
We encourage you to prepare for your career from day one. That’s one of the reasons Leeds graduates are so sought after by employers.
The Careers Centre and staff in your faculty provide a range of help and advice to help you plan your career and make well-informed decisions along the way, even after you graduate. Find out more at the Careers website.
In Semester 2 you will have the option to undertake a negotiated work placement to gain first-hand experience of curating science.
We have close links with many of the major cultural institutions and organisations in the region, meaning there are plenty of opportunities for you to explore. If you have a particular ambition in mind for your placement, we usually try to find a role that suits you.
Students on allied MAs have completed placements in organisations such as Leeds City Museum, Leeds Art Gallery, Harewood House, the Henry Moore Institute, National Science and Media Museum, York City Art Gallery, National Railway Museum, Impressions Gallery, The Tetley, Yorkshire Sculpture Park, Lotherton Hall, Abbey House Museum and the Royal Armouries.
Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Data Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).
MSc in Data Science aims to equip students with a solid grounding in data science concepts and technologies for extracting information and constructing knowledge from data. Students of the MSc Data Science will study the computational principles, methods, and systems for a variety of real world applications that require mathematical foundations, programming skills, critical thinking, and ingenuity. Development of research skills will be an essential element of the Data Science programme so that students can bring a critical perspective to current data science discipline and apply this to future developments in a rapidly changing technological environment.
The MSc Data Science programme focuses on three core technical themes: data mining, machine learning, and visualisation. Data mining is fundamental to data science and the students will learn how to mine both structured data and unstructured data. Students will gain practical data mining experience and will gain a systematic understanding of the fundamental concepts of analysing complex and heterogeneous data. They will be able to manipulate large heterogeneous datasets, from storage to processing, be able to extract information from large datasets, gain experience of data mining algorithms and techniques, and be able to apply them in real world applications. Machine learning has proven to be an effective and exciting technology for data and it is of high value when it comes to employment. Students of the Data Science programme will learn the fundamentals of both conventional and state-of-the-art machine learning techniques, be able to apply the methods and techniques to synthesise solutions using machine learning, and will have the necessary practical skills to apply their understanding to big data problems. We will train students to explore a variety visualisation concepts and techniques for data analysis. Students will be able to apply important concepts in data visualisation, information visualisation, and visual analytics to support data process and knowledge discovery. The students of the Data Science programme also learn important mathematical concepts and methods required by a data scientist. A specifically designed module that is accessible to students with different background will cover the basics of algebra, optimisation techniques, statistics, and so on. More advanced mathematical concepts are integrated in individual modules where necessary.
The MSc Data Science programme delivers the practical components using a number of programming languages and software packages, such as Hadoop, Python, Matlab, C++, OpenGL, OpenCV, and Spark. Students will also be exposed to a range of closely related subject areas, including pattern recognition, high performance computing, GPU processing, computer vision, human computer interaction, and software validation and verification. The delivery of both core and optional modules leverage on the research strength and capacity in the department. The modules are delivered by lecturers who are actively engaged in world leading researches in this field. Students of the Data Science programme will benefit from state-of-the-art materials and contents, and will work on individual degree projects that can be research-led or application driven.
Modules for the MSc Data Science programme include:
- Visual Analytics
- Data Science Research Methods and Seminars
- Big Data and Data Mining
- Big Data and Machine Learning
- Mathematical Skills for Data Scientists
- Data Visualization
- Human Computer Interaction
- High Performance Computing in C/C++
- Graphics Processor Programming
- Computer Vision and Pattern Recognition
- Modelling and Verification Techniques
- Operating Systems and Architectures
The Department of Computer Science is well equipped for teaching, and is continually upgrading its laboratories to ensure equipment is up-to-date – equipment is never more than three years old, and rarely more than two. Currently, our Computer Science students use three fully networked laboratories: one, running Windows; another running Linux; and a project laboratory, containing specialised equipment. These laboratories support a wide range of software, including the programming languages Java, C# and the .net framework, C, C++, Haskell and Prolog among many; integrated programme development environments such as Visual Studio and Netbeans; the widely-used Microsoft Office package; web access tools; and many special purpose software tools including graphical rendering and image manipulation tools; expert system production tools; concurrent system modelling tools; World Wide Web authoring tools; and databases.
As part of the expansion of the Department of Computer Science, we are building the Computational Foundry on our Bay Campus for computer science and mathematical science.
- Data Analyst
- Data mining Developer
- Machine Learning Developer
- Visual Analytics Developer
- Visualisation Developer
- Visual Computing Software Developer
- Database Developer
- Data Science Researcher
- Computer Vision Developer
- Medical Computing Developer
- Informatics Developer
- Software Engineer