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.
The MSc in Data Science will provide you with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science.
The rate at which we are able to create data is rapidly accelerating. According to IBM, globally, we currently produce over 2.5 quintillion bytes of data a day. This ranges from biomedical data to social media activity and climate monitoring to retail transactions. These enormous quantities of data hold the keys to success across many domains from business and marketing to treating cancer or mitigating climate change.
The pace at which we produce data is rapidly outstripping our ability to analyse and use it. Science and industry are crying out for a new generation of data scientists who combine the statistical skills of data analysis and the computational skills needed to carry out this analysis on a vast scale.
The MSc in Data Science provides you with these skills.
Studying this Masters, you will learn the mathematical foundations of statistics, data mining and machine learning, and apply these to practical, real world data.
As well as these statistical skills, you will learn the computational techniques needed to efficiently analyse very large data sets. You will apply these skills to a range of real world data, under the guidance of experts in that domain. You will analyse trends in social media, make financial predictions and extract musical information from audio files.
The degree will culminate in a final project in which you will you can apply your skills and follow your specialist interests. You will do a novel analysis of a real world data of your choice.
The programme includes:
You will study the following core modules:
You will also choose from an anually approved list of modules which may include:
Data Science is one of the fastest growing sectors of employment internationally. Big Data is an important part of modern finance, retail, marketing, science, social science, medicine and government.
The study of a combination of long established fields such as statistics, data mining, machine learning and databases with very modern and strongly related fields as big data management and analytics, sentiment analysis and social web mining, offers graduates an excellent opportunity for getting valuable skills in advanced data processing.
This could lead to a variety of potential jobs including:
Find out more about employability at Goldsmiths.
How do children learn to reason in increasingly abstract ways? How do they learn language with such remarkable speed and fluidity? How do children use their reasoning and language skills to help them explain and understand people’s behaviour and emotions? Why does the amount of information that we can hold in mind at once increase from early childhood to adulthood? Why does children’s ability to control their own thinking, attention and behaviour improve as they get older? How does the development of children’s brains affect their behaviour, memory and ability to learn?
In this taught programme on Developmental Cognitive Science, you will learn how questions like these can be addressed using research techniques from several inter-related disciplines (e.g., Developmental Psychology, Cognitive Psychology, Computational Science, Neuroscience, Linguistics).
This programme aims to enhance your understanding of key theoretical and practical issues about typical and atypical development in children and young people, from a cognitive science perspective. It also aims to equip you with the skills required to conduct independent scientific research that addresses key issues in developmental cognitive science.
The University of Edinburgh has a long tradition of research expertise in developmental psychology and in cognitive science. This programme brings these two strands together focusing on a developmental cognitive science approach to both typical and atypical development in children and young people.
You will benefit from the breadth and strength of the interdisciplinary academic community at Edinburgh, for example by having the opportunity to select option courses and attend research seminars across different disciplines.
You will undertake the following:
Core courses (worth 100 credits in total):
2 option courses worth 20 credits in total:
And a Dissertation in Developmental Cognitive Science (60 credits)
The overall aim of the proposed programme is to advance students’ understanding of how questions about developmental changes in children’s cognitive abilities can be addressed using scientific methods drawn from a range of fields, including developmental psychology, cognitive psychology, computational modelling, neuroscience and linguistics. More specifically, the programme aims to:
Students who successfully complete the programme will be able to:
Career opportunities for graduates from this programme include:
Find out more about scholarships and funding opportunities:
The MSc Statistics (Social Statistics) aims to provide high-level training in the theory and application of modern statistical methods, with a focus on methods commonly used in the social sciences.
You will gain insights into the design and analysis of social science studies, including large and complex datasets, study the latest developments in statistics, and learn how to apply advanced methods to investigate social science questions.
The programme includes two core courses which provide training in fundamental aspects of probability and statistical theory and methods, the theory and application of generalised linear models, and programming and data analysis using the R and Stata packages. These courses together provide the foundations for the optional courses on more advanced statistical modelling, computational methods and statistical computing. Options also include specialist courses from the Departments of Methodology, Economics, Geography and Social Policy.
The Research stream is similar to the nine-month programme, but will include a dissertation component, extending the programme to twelve months.
There is a high demand for graduates with advanced statistics training and an interest in social science applications, and students on this programme have excellent career prospects.
Potential employers include the public sector (the Office for National Statistics, government departments, universities), market research organisations, survey research organisations and NGOs. This programme would be ideal preparation for doctoral research in social statistics or quantitative social science.
The range of pathways reflects the interdisciplinary nature of the programme and we welcome applications from students with backgrounds in a wide range of disciplines including:
We provide training in core data science skills, embedded in a disciplinary context provided by the pathway, and expect students to develop:
This innovative master's in data science is an opportunity for graduates from a broad range of disciplines to develop data science skills. Our goal is to help you develop into an agile, skilled data scientist, adept at working in variety of settings and able to meet the challenges and reap the rewards of interdisciplinary team work.
The range of pathways reflects the interdisciplinary nature of the course and we welcome applications from students with backgrounds in a wide range of disciplines including:
We provide training in core data science skills, embedded in a disciplinary context provided by the pathway, and will expect you to develop:
Through a set of core units you will develop a set of key data science skills. The core units are:
In semester 2 you will be able to specialise, choosing one of our five pathways and then writing a final dissertation. The pathways are:
Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service. Email: [email protected]
There is an acute shortage of data scientists across the globe, so this qualification will provide a strong boost to your employability.
The Institute for Adaptive and Neural Computation (IANC) is a world-leading institute dedicated to the theoretical and empirical study of adaptive processes in both artificial and biological systems. We are one of the UK’s largest and most prestigious academic teams in these fields.
We foster world-class interdisciplinary and collaborative research bringing together a range of disciplines.
Our research falls into three areas:
In machine learning we develop probabilistic methods that find patterns and structure in data, and apply them to scientific and technological problems. Applications include areas as diverse as astronomy, health sciences and computing.
In computational neuroscience and neuroinformatics we study how the brain processes information, and analyse and interpret data from neuroscientific experiments
The focus in the computational biology area is to develop computational strategies to store, analyse and model a variety of biological data (from protein measurements to insect behavioural data).
You carry out your research within a research group under the guidance of a supervisor. You will be expected to attend seminars and meetings of relevant research groups and may also attend lectures that are relevant to your research topic. Periodic reviews of your progress will be conducted to assist with research planning.
A programme of transferable skills courses facilitates broader professional development in a wide range of topics, from writing and presentation skills to entrepreneurship and career strategies.
The School of Informatics holds a Silver Athena SWAN award, in recognition of our commitment to advance the representation of women in science, mathematics, engineering and technology. The School is deploying a range of strategies to help female staff and students of all stages in their careers and we seek regular feedback from our research community on our performance.
The award-winning Informatics Forum is an international research facility for computing and related areas. It houses more than 400 research staff and students, providing office, meeting and social spaces.
It also contains two robotics labs, an instrumented multimedia room, eye-tracking and motion capture systems, and a full recording studio amongst other research facilities. Its spectacular atrium plays host to many events, from industry showcases and student hackathons to major research conferences.
Nearby teaching facilities include computer and teaching labs with more than 250 machines, 24-hour access to IT facilities for students, and comprehensive support provided by dedicated computing staff.
Among our entrepreneurial initiatives is Informatics Ventures, set up in 2008 to support globally ambitious software companies in Scotland and nurture a technology cluster to rival Boston, Pittsburgh, Kyoto and Silicon Valley.
The research you will undertake at ANC is perfectly suited to a career in academia, where you’ll be able to use your knowledge to advance this important field. Some graduates take their skills into commercial research posts, and find success in creating systems that can be used in everyday applications.
This exciting and challenging programme studies how data can be utilised to solve major business and societal challenges. The programme provides students with the knowledge, technical ability and skills for leadership roles in the fields of business analytics and data science.
The programme is designed to give students multidisciplinary skills in computing (i.e. programming, big data), analytics (i.e. data mining, machine learning, computational statistics, complexity), and business analysis. Emphasis will be on business problem framing, leveraging data as a strategic asset, and communicating complex analytical results to stakeholders.
Students undertake modules to the value of 180 credits.
The programme consists of three core modules (45 credits), four or five optional modules (60 to 75 credits), up to one elective module (15 credits) and a dissertation (60 credits).
Students must choose a minimum of 60 and a maxuimum of 75 credits from Optional modules. A maximum of 15 credits may be taken from Electives.
Please note: the availability and delivery of optional modules may vary, depending on your selection.
A list of acceptable elective modules is available on the Departmental page.
During the summer students will undertake a work placement with a UCL industrial partner. The research and data analysis conducted during this placement will form the basis of a 10,000-word dissertation.
Teaching and learning
The programme is delivered through a combination of lectures by world-class academics and industry leaders, seminars, workshops, tutorials and project work. The programme comprises two terms of taught material, followed by examinations and then a project. Assessment is through unseen written examinations, coursework and the dissertation.
Further details are available on UCL Computer Science website.
Further information on modules and degree structure is available on the department website: Business Analytics (with specialisation in Computer Science) MSc
Graduates of UCL Computer Science are particularly valued due to the department's international status and strong reputation for leading research. Recent graduate destinations include such companies as: IBM, Samsung, Microsoft, Price Waterhouse Coopers, Citibank.
This programme is designed to satisfy the need, both nationally and internationally, for exceptional data scientists and analysts. Graduates will be highly employable in global companies and high-growth businesses, finance and banking organisations, major retail and service companies, and consulting firms. They will be equipped to influence strategy and decision-making, and be able to drive business performance by transforming data into a powerful and predictive strategic asset. We expect our graduates to progress to leading and influential positions in industry.
UCL Computer Science is a global leader in research in experimental computer science. The department scored highest among UK universities for the quality of research in Computer Science and Informatics in the Research Excellence Framework (REF2014), with 96% regarded as 'world-leading' or 'internationally excellent'.
The department consists of a team of world-class academics specialising in big data, computational statistics, machine learning and complexity.
The programme aims to create the next generation of outstanding academics and industry pioneers, who will use data analysis to deliver real social and business impact.
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: Computer Science
96% 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.
Data science is the study of the computational principles, methods, and systems for extracting and structuring knowledge from data; and the application and use of those principles. Large data sets are now generated by almost every activity in science, society, and commerce - ranging from molecular biology to social media, from sustainable energy to health care.
As an MSc Data Science student you will explore how to efficiently find patterns in these vast streams of data. Many research areas have tackled parts of this problem. Machine learning focuses on finding patterns and making predictions from data; ideas from algorithms and databases are required to build systems that scale to big data streams; and separate research areas have grown around different types of unstructured data such as text, images, sensor data, video, and speech.
You follow two taught semesters of lectures, tutorials, project work and written assignments, after which you will learn research methods before individual supervision for your project and dissertation.
You are also required to take a breadth of courses in data science, with at least one in each of the following areas:
You can take up to two courses from other schools.
The School of Informatics' MSc in Data Science is designed to attract students who want to establish a career as a data scientist in industry or the public sector, as well as students who want to explore the area prior to further training such as in our CDT in Data Science.
The learning objectives of the degree are to foster:
You will develop specialist, advanced skills in data science methods and their applications. You will gain practical experience and a thorough theoretical understanding of the field, making you attractive to a wide range of employers or preparing you for further academic study.
MSc Data Science with Business combines core Data Science modules, taken from MSc Data Science, with modules in Management, Strategy, Marketing and Accounting. This course is taught in partnership between the College of Engineering, Mathematics and Physical Sciences and the University of Exeter Business School. This blend of business and science is designed for students wanting management or leadership roles working with data.
Teaching, taking place over 12 months, covers the fundamental mathematical and computational techniques used to deliver insights and understand phenomena extracted from data sources. Building upon your existing coding skills you will handle complex data sets, learning multiple methods of analysis and efficient approaches to visualisation and presentation. In addition, you will study optional modules on subjects such as governance and ethics, social networks, text analysis and computer vision.
Your project, which forms a major part of the MSc, will maintain a business focus as you explore data science in a commercial environment.
Constituent modules and pathways may be updated, deleted or replaced in future years as a consequence of programme development. Details at any time may be obtained from the programme website.
The compulsory modules can include;
Optional modules can include;
Teaching is mainly delivered by lectures, workshops and online materials. Each module references core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures.
We believe every student benefits from being taught by experts active in research and practice. All our academic staff are active in internationally-recognised scientific research across a wide range of topics. You will discuss the very latest ideas, research discoveries and new technologies, becoming actively involved in a research project yourself.
We aim to provide a supportive environment where students and staff work together in an informal and friendly atmosphere. The department has a student-focused approach to teaching, whereby all members of staff deal with questions on an individual basis. We operate an open door policy, so it is easy to consult individual members of staff or to fix appointments with them via email. As a friendly group of staff, you will get to know us well during your time here.
The assessment strategy for each module is explicitly stated in the full module descriptions given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills. Assessment methods include essays, closed book tests, exercises in problem solving, use of the web for tool-based analysis and investigation, mini-projects, extended essays on specialized topics, and individual and group presentations.