• University of Edinburgh Featured Masters Courses
  • University of Derby Online Learning Featured Masters Courses
  • Northumbria University Featured Masters Courses
  • Aberystwyth University Featured Masters Courses
  • University of Leeds Featured Masters Courses
  • University of Bristol Featured Masters Courses
  • Jacobs University Bremen gGmbH Featured Masters Courses

Postgrad LIVE! Study Fair

Birmingham | Bristol | Sheffield | Liverpool | Edinburgh

Nottingham Trent University Featured Masters Courses
Nottingham Trent University Featured Masters Courses
FindA University Ltd Featured Masters Courses
University of Reading Featured Masters Courses
University of Leeds Featured Masters Courses
"agent-based" AND "modell…×
0 miles

Masters Degrees (Agent-Based Modelling)

We have 15 Masters Degrees (Agent-Based Modelling)

  • "agent-based" AND "modelling" ×
  • clear all
Showing 1 to 15 of 15
Order by 
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. Read more

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.

Key benefits

  • An understanding of modern financial technology (FinTech) including electronic trading and distributed-ledger technology.
  • Practical hands-on techniques for working with and analysing financial data, which draw on modern developments in Artificial Intelligence and Big Data technology.
  • The opportunity to understand the practical aspects of quantitative finance and FinTech from Industry experts located in the heart of one of the World’s financial centres.

Description

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.

Career prospects

Students are expected to go in to careers such as Investment Banking, Hedge Funds and Regulatory Bodies.  



Read less
Starting in 2016, and currently under development, Royal Holloway will offer a new Masters in 'The Internet of Things' (IOT). The Internet of Things is a new and fast expanding area in Computer Science, technology and engineering. Read more
Starting in 2016, and currently under development, Royal Holloway will offer a new Masters in 'The Internet of Things' (IOT).

The Internet of Things is a new and fast expanding area in Computer Science, technology and engineering: it concerns the systems of networked devices that are now sensing, transmitting and acting on data. A series of reports place the IoT as a new and transformative technology domain that will require millions of developers by 2020.

The course will educate and train you in the key areas required for operating the generation of networks of connected devices that are starting to proliferate (smart homes, smart cities, smart cars, and so on): data analysis, storage and processing; distrbuted and networked systems; and information security.

Facilities include a laboratory where you can experiment with physical devices that can be interconnected in a network, and a cluster facility for processing and analysing real data sets.

Please note this programme is subject to validation.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/msctheinternetofthings.aspx

Why choose this course?

The Masters in The Internet of Things at Royal Holloway will provide you with advanced knowledge and skills in three essential and critical areas:

- Data analytics is essential for getting value from the IOT. For example, in Formula One racing there are hundreds of sensors providing thousands of data points for analysis such as tyre pressure and fuel burn efficiency, which have to be collected in real-time for very quick analysis by race engineers onsite.

- Distributed computing and systems concern technical aspects such as algorithms for distributed coordination, time-synchronisation, scalable storage, virtualisation and cloud computing technologies, as well as methodological aspects such agent-based modelling and simulation.

- Cybersecurity is another essential aspect of the IOT. Recent news such as the safety recall issued by Fiat Chrysler of 1.4m vehicles in the US after tech magazine Wired reported that hackers had taken control of a Jeep Cherokee via its internet-connected entertainment system, are examples of how privacy, safety and security are major concerns for the IOT.

During your studies you will have 24/7-access to labs equipped with a state-of-the-art cloud computing testbed and a new generation of large-scale data processing platforms (such as Hadoop and MongoDB), which will give you the opportunity to gain hands-on experience working on real-life problems in areas as diverse as social network analytics, web data mining, and botnet detection.

Department research and industry highlights

Royal Holloway is recognised for its research excellence in Machine Learning, Information Security, and Global Ubiquitous Computing.

We work closely with companies such as Centrica (British Gas, Hive), Cognizant, Orange Labs (UK), the UK Cards Association, Transport for London and ITSO.

We host a Smart Card Centre and we are a GCHQ Academic Centre of Excellence in Cyber Security Research (ACE-CSR).

During your studies you will have 24/7-access to labs equipped with a state-of-the-art cloud computing testbed and a new generation of large-scale data processing platforms (such as Hadoop and MongoDB), which will give you the opportunity to gain hands-on experience working on real-life problems in areas as diverse as social network analytics, web data mining, and botnet detection.

Assessment

Assessment is carried out by a variety of methods including coursework, practical projects and a dissertation.

Employability & career opportunities

Our graduates are highly employable and, in recent years, have entered many different Computer Science-related areas, including This taught masters course equips postgraduate students with the subject knowledge and expertise required to pursue a successful career, or provides a solid foundation for continued PhD studies.

How to apply

Applications for entry to all our full-time postgraduate degrees can be made online https://www.royalholloway.ac.uk/studyhere/postgraduate/applying/howtoapply.aspx .

Read less
Starting in 2016, and currently under development, Royal Holloway will offer a new Masters in 'The Internet of Things' (IOT) with a Year in Industry. Read more
Starting in 2016, and currently under development, Royal Holloway will offer a new Masters in 'The Internet of Things' (IOT) with a Year in Industry.

The Internet of Things is a new and fast expanding area in Computer Science, technology and engineering: it concerns the systems of networked devices that are now sensing, transmitting and acting on data. A series of reports place the IoT as a new and transformative technology domain that will require millions of developers by 2020.

The course will educate and train you in the key areas required for operating the generation of networks of connected devices that are starting to proliferate (smart homes, smart cities, smart cars, and so on): data analysis, storage and processing; distrbuted and networked systems; and information security.

Facilities include a laboratory where you can experiment with physical devices that can be interconnected in a network, and a cluster facility for processing and analysing real data sets.

Please note this programme is subject to validation.

See the website https://www.royalholloway.ac.uk/computerscience/coursefinder/msctheinternetofthingsyearinindustry.aspx

Why choose this course?

The Masters in The Internet of Things at Royal Holloway will provide you with advanced knowledge and skills in three essential and critical areas:

- Data analytics is essential for getting value from the IOT. For example, in Formula One racing there are hundreds of sensors providing thousands of data points for analysis such as tyre pressure and fuel burn efficiency, which have to be collected in real-time for very quick analysis by race engineers onsite.

- Distributed computing and systems concern technical aspects such as algorithms for distributed coordination, time-synchronisation, scalable storage, virtualisation and cloud computing technologies, as well as methodological aspects such agent-based modelling and simulation.

- Cybersecurity is another essential aspect of the IOT. Recent news such as the safety recall issued by Fiat Chrysler of 1.4m vehicles in the US after tech magazine Wired reported that hackers had taken control of a Jeep Cherokee via its internet-connected entertainment system, are examples of how privacy, safety and security are major concerns for the IOT.

During your studies you will have 24/7-access to labs equipped with a state-of-the-art cloud computing testbed and a new generation of large-scale data processing platforms (such as Hadoop and MongoDB), which will give you the opportunity to gain hands-on experience working on real-life problems in areas as diverse as social network analytics, web data mining, and botnet detection.

Department research and industry highlights

Royal Holloway is recognised for its research excellence in Machine Learning, Information Security, and Global Ubiquitous Computing.

We work closely with companies such as Centrica (British Gas, Hive), Cognizant, Orange Labs (UK), the UK Cards Association, Transport for London and ITSO.

We host a Smart Card Centre and we are a GCHQ Academic Centre of Excellence in Cyber Security Research (ACE-CSR).

Assessment

Assessment is carried out by a variety of methods including coursework, practical projects and a dissertation. The placement is assessed as part of the Year-in-Industry degree.

Employability & career opportunities

Our graduates are highly employable and, in recent years, have entered many different Computer Science-related areas, including This taught masters course equips postgraduate students with the subject knowledge and expertise required to pursue a successful career, or provides a solid foundation for continued PhD studies.

How to apply

Applications for entry to all our full-time postgraduate degrees can be made online https://www.royalholloway.ac.uk/studyhere/postgraduate/applying/howtoapply.aspx .

Read less
Our highly sought-after graduates benefit from a programme that integrates training in identifying, framing and effectively researching social problems with a leading computational approach to social science. Read more
Our highly sought-after graduates benefit from a programme that integrates training in identifying, framing and effectively researching social problems with a leading computational approach to social science.

Furthermore, we are home to the Centre for Research in Social Simulation (CRESS) and its world-leading expertise in agent-based modelling.

PROGRAMME OVERVIEW

Interest in simulation has grown rapidly in the social sciences. New methods have been developed to tackle this complexity. This programme will integrate traditional and new methods, to model complexity, evolution and the adaptation of social systems.

These new methods are having an increasing influence on policy research through a growing recognition that many social problems are insufficiently served by traditional policy modelling approaches.

The Masters in Social Science and Complexity will equip you to develop expertise in the methods necessary to tackle complex, policy-relevant, real-world social problems through a combination of traditional and computational social science methods, and with a particular focus on policy relevance.

PROGRAMME STRUCTURE

This programme is studied full-time over one academic year and part-time over two academic years. It consists of eight taught modules and a dissertation. The following modules are indicative, reflecting the information available at the time of publication. Please note that not all modules described are compulsory and may be subject to teaching availability and/or student demand.
-Data Analysis
-Field Methods
-Computational Modelling
-Theory Model Data
-Modelling the Complex World
-Policy Modelling
-Theory and Method
-Statistical Modelling
-Evaluation Research
-Dissertation

EDUCATIONAL AIMS OF THE PROGRAMME

The main aims of the programme are to:
-Provide an appropriate training for students preparing MPhil/PhD theses, or for 
 students going on to employment involving the use of social science and policy research
-Provide training that fully integrates social science, policy modelling and computational methodologies to a high standard
-Provide training resulting in students with high quality analytic, methodological, computational and communication skills

PROGRAMME LEARNING OUTCOMES
The programme provides opportunities for students to develop and demonstrate knowledge and understanding, skills, qualities and other attributes in the following areas:
-Develop skills in tackling real world policy problems with creativity and sound methodological judgment
-Cover the principles of research design and strategy, including formulating research 
questions or hypotheses and translating these into practicable research designs and models
-Introduce students to the methodological and epistemological issues surrounding research in the social sciences in general and computational modelling in particular
-Develop skills in programming in NetLogo for the implementation of agent-based models for the modelling of social phenomena
-Develop skills in the acquisition and analysis of social science data
-Make students aware of the range of secondary data available and equip them to evaluate its utility for their research
-Develop skills in searching for and retrieving information, using library and Internet resources
-Develop skills in the use of SPSS, and in the main statistical techniques of data analysis, including multivariate analysis
-Develop skills in the use of CAQDAS software for the analysis of qualitative data
-Develop skills in writing, in the preparation of a research proposal, in the presentation ofresearch results and in verbal communication
-Help students to prepare their research results for wider dissemination, in the form of seminar papers, conference presentations, reports and publications, in a form suitable for a range of audiences, including academics, stakeholders, policy makers, professionals, service users and the general public

Knowledge and understanding
-Show advanced knowledge of qualitative, quantitative and computational methodologies in the social science
-Show advanced knowledge of modelling methodologies, model construction and analysis
-Show critical understanding of methodological and epistemological challenges of social science and computer modelling
-Show critical awareness and understanding of the methodological implications of a range of sociological theories and approaches
-Show understanding the use and value of a wide range of different research approaches across the quantitative and qualitative spectra
-Show advanced knowledge in data collection, analysis and data driven modelling
-Show advanced knowledge of policy relevant social science research and modelling
-Show advanced understanding of the policy process and the role of social science and modelling therein
-Show advanced knowledge of statistical modelling

Intellectual / cognitive skills
-Systematically formulate researchable problems; analyse and conceptualise issues; critically appreciate alternative approaches to research; report to a range of audiences
-Conceptual development of Social Science and Complexity models to creatively enhance the understanding of social phenomena
-Integration of qualitative, quantitative and computational data
-Judgement of problem-methodology match
-Analyse qualitative and quantitative data drawn both from ‘real world’ and ‘virtual world’ environments, using basic and more advanced techniques, and draw warranted conclusions
-Develop original insights, questions, analyses and interpretations in respect of research questions
-Critically evaluate the range of approaches to research

Professional practical skills
-Formulate, design, plan, carry out and report on a complete research project
-Use the range of traditional and computational techniques employed in sociological research
-Ability to produce well founded, data driven and validated computational models
-Generate both quantitative and qualitative data through an array of techniques, and select techniques of data generation on appropriate methodological bases
-Employ a quantitative (SPSS) and qualitative software package to manage and analyse data
-Plan, manage and execute research as part of a team and as a sole researcher
-Ability to communicate research findings models in social science and policy relevant ways
-Ability to manage independent research

Key / transferable skills
-Communicate complex ideas, principles and theories by oral, written and visual means
-Apply computational modelling methodology to complex social issues in appropriate ways
-Creativity in approaching complex problems and a the ability of communicating and justifying problem solutions
-Apply computing skills for computational modelling, research instrument design, data analysis, and report writing and presentation
-Work to deadlines and within work schedules
-Work independently or as part of a team
-Demonstrate experience of a work environment

PLACEMENTS

On the MSc Social Science and Complexity, we offer the opportunity to take a research placement during the Easter vacation. This will provide you with first-hand experience of real-life policy research in action.

Organisations in which placements might be possible are a number of consultancies (e.g. Sandtable), government departments (e.g. Defra) and academic research centres (e.g. Centre for Policy Modelling at Manchester).

CAREER OPPORTUNITIES

Computational methods and especially computer-based simulations, are becoming increasingly important in academic social science and policy making.

Graduates might find career opportunities in government departments, consultancies, government departments, consultancies, NGOs and academia.

GLOBAL OPPORTUNITIES

We often give our students the opportunity to acquire international experience during their degrees by taking advantage of our exchange agreements with overseas universities.

In addition to the hugely enjoyable and satisfying experience, time spent abroad adds a distinctive element to your CV.

Read less
The increasing integration of technology into our lives has created unprecedented volumes of data on everyday social behaviour. Read more

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.

Programme structure

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.



Read less
Effective information management is key to the success of any organisation. The MBA Information Management develops knowledgeable and capable executives who will become managers in the IT / computing industry, or in companies in other sectors. Read more
Effective information management is key to the success of any organisation. The MBA Information Management develops knowledgeable and capable executives who will become managers in the IT / computing industry, or in companies in other sectors. The aim of the programme is to provide graduates with a range of management knowledge and skills, together with a thorough foundation in information management, information technology, and its commercial applications. The programme includes topical case studies, and reflects contemporary developments within the sector. The course is suitable for graduates in a wide range of disciplines, including Engineering, Finance, Social Sciences and other subjects.

Compulsory Modules:

Organisations and People: This module examines key issues arising from contemporary research in organisational behaviour (OB) and human resource management (HRM). It provides an integrated analysis of management, organisations and people, developing the conceptual, strategic and practical skills necessary for managers in complex, global organisational contexts. Specific topics include:

The nature of organisations
Organisation structures: strategy, design and function, job design
Organisation cultures: values, ethics, norms of behaviour
Theories and models of management: classical and contemporary
Individual differences: perception, learning, motivation, equality and diversity
Groups and teams in the organisation
Managing relationships: power, conflict, communication, engagement
Managers as leaders, people developers, coaches
Managing job satisfaction and performance

International Strategic Management: This module analyses strategic decision-making within business. You will develop a critical understanding of the strategic processes of business management, the interconnections with the functional domains of marketing, human resource management and corporate finance, and the management of knowledge systems. Specific topics include:

Concepts of strategic management applicable to business
Prescriptive and emergent strategies
Strategy implementation through capacity building and resource allocation
Managing, monitoring and reviewing strategic change
Organisational designs for strategic advantage
Human resources strategy, marketing and corporate financial strategy
Organisational learning and knowledge management

Management Research: This module analyses the philosophical basis for research in the management sciences, and examines a number of key methodological issues and approaches. Research designs for both quantitative and qualitative research methodologies are developed, including interviews, case studies, focus groups, surveys and experiments. Specific topics include:

Research methodologies and philosophy: positivism and interpretivism
Qualitative research methods and the search for meaning
Selecting a research strategy and design
Data gathering, documentary records, triangulation and mixed methods
Content analysis, conversation analysis, discourse analysis, grounded theory
Quantitative research design and methodologies
Univariate and multivariate analysis, factor, cluster and discriminant analysis

Web Technologies: This module provides an understanding of the basic technologies and structures for developing web applications, including internet resource creation, search techniques and programming languages for creating web content. You will create and use multimedia content in web applications, and gain familiarity with technologies for creating secure web applications. Specific topics include:

Internet concepts; networks; ISO 7 layer model; basic network architecture; routing; domain names; email; ftp; telnet; HTTP
WWW concepts; Internet resources; URI, and URI resolution, URL, URN; relation to XML namespaces; search engines; search algorithms; search engine optimisation
JavaScript; PHP; CSS; programming tools and environments
Multimedia; WWW support for multimedia; file compression
Internet Security; Cryptography; standards for the Internet; public key systems; signatures; authentication; trust management; electronic cash; security issues; firewalls
Web programming; HTML; XML; form input; CGI scripting; Perl programming

Finance for Managers: This module is designed for those who aim to achieve a basic understanding of financial management and control, and who require an understanding of finance in order to manage an organisation effectively. Financial planning and control are central themes, as well as the appraisal techniques of investment projects. Specific topics include:

Principles underlying the preparation of accounting information
Recording business transactions
Preparation and analysis of financial statements
Preparation of budgets, financial planning and control
Costing methods, uses and interpretation of cost data
Investment appraisal techniques

Databases: This module shows how to design a database and intelligently query a database using SQL; and provides an introductory level of understanding in database systems. A mini project is carried out towards the end of the module. This project allows you to complete the entire development process, from informal user requirements, to ER/EER modelling, transformation into relations, normalisation, and finally to the SQL commands to create and query the database. Specific topics include:

Characteristics of a relational database
ER/EER modelling of simple applications
Relational model and relational algebra
Transformation of an ER/EER model into a relational database
Normalisation techniques
Uses of SQL language to create and query a database

Technologies for Internet Systems: This module introduces technologies and tools for Internet Systems and e-commerce systems. Technologies and structures for developing web applications are examined. Technical issues for implementing an e-system, and commonly-available technology components, are covered. You will implement a practical web based e-commerce system using relevant technologies, taking into account current market implementation. Specific topics include:

e-commerce ideas and concepts
Internet concepts; networks; basic network architecture; routing; domain names; email; telnet; HTTP
Architectures and technologies, e-payment, e-commerce software and hardware, e-security, auctions
Design and implementation: HTML, XML, CSS, JavaScript, DOM, SVG
Research awareness: agent-based e-commerce; web services; grid computing; virtual organisations

Information Systems: This module examines the major types and components of Information Systems, their functions, benefits and limitations. The theoretical underpinnings of Information Systems are analysed. You will study the main business and personal uses of Information Systems, and how such systems are developed, procured and deployed. Specific topics include:

Understanding the nature of organisations and the people within them, and their use of information for strategic business purposes
The influence of human and organisational factors on the successful introduction of information systems
Methods and techniques involved in project and programme management
The importance of business processes and techniques for process modelling

Part 2:

For MBA Information Management, you MUST:

Complete two of the following Applied Business Projects: Business Planning; e-Business and Chain Value; Human Resource Management; International Business; Operations Management; Investment and Private Banking.
Write a Computing project, Software Hut. Software Hut is a project in which students (in groups) analyse, design and implement a software product for an organisation.

Read less
MSc Financial Econometrics combines a practical approach to finance with a strong theoretical approach in econometrics, and is taught jointly between our Department of Economics and Essex Business School. Read more
MSc Financial Econometrics combines a practical approach to finance with a strong theoretical approach in econometrics, and is taught jointly between our Department of Economics and Essex Business School. This mix of approaches means that you graduate from our course as someone who is very attractive to a variety of financial institutions, from insurance companies to central banks.

You develop a solid theoretical grounding in econometrics which complements the modules taught by Essex Business School; this structure gives you the opportunity to develop your skills in econometrics and apply them practically in a finance environment. You investigate topics including:
-Asset pricing theories and empirical findings
-Methods of estimation
-Modern econometric techniques
-Time series econometrics

We are top 5 in the UK for research, with over 90% rated as “world-leading” or “internationally excellent”. Much of this world-class research is related to policy, and we have particular strengths in the areas of:
-Game theory and strategic interactions
-Theoretical and applied econometrics
-Labour economics

The quality of our work is reflected in our stream of publications in high-profile academic journals, including American Economic Review, Econometrica, and Review of Economic Studies.

Professional accreditation

Our University is one of only 21 ESRC-accredited Doctoral Training Centres in the UK.

This means that our course can form part of a prestigious 1+3 funding opportunity worth up to £21,575.

Our expert staff

Study and work alongside some of the most prominent economists of our time.

Our researchers are at the forefront of their field and have even received MBEs, with students coming from across the globe to study, research or work with us.

Many of our researchers also provide consultancy services to businesses in London and other major financial centres, helping us to develop research for today's society as well as informing our teaching for the future.

Within Essex Business School, you are taught by a highly qualified, enthusiastic team with wide-ranging research interests and proven academic track record.

Our staff specialise in areas including: accounting and economic development in the public and third sectors; regulation and corporate social responsibility; finance and banking; accounting and finance in developing economies; and contemporary financial markets and their participants.

Specialist facilities

Take advantage of our wide range of learning resources to assist you in your studies:
-Extensive software for quantitative analysis is available in all computer labs across the university
-Access a variety of economics databases and multiple copies of textbooks and e-books in the Albert Sloman Library

Our landmark new Essex Business School building on our Colchester Campus is the first zero carbon business school in the UK. Set around a lush winter garden, the Eden-style dome gives the building its own micro-climate. Our new building provides you with a stunning new work environment, offering:
-A virtual trading floor with Bloomberg Terminals offering direct use of Bloomberg data, information and analytics
-A light and spacious lecture theatre, with seating for 250 students
-Study pods and innovation booths for group working
-Dedicated office space for student entrepreneurs
-Networking opportunities with visiting businesses
-A café with an adjacent sun terrace

Your future

After completing your masters, you may wish to extend your knowledge with a research degree – many Essex graduates decide to stay here for further study.

Alternatively, our course also prepares you for employment; recent surveys have shown that higher degree graduates are more likely to obtain jobs at professional or managerial level.

Our MSc Financial Econometrics will help you to develop a range of skills that will make you highly employable. These include modelling skills, statistical analysis, research skills and developing an understanding of asset pricing and financial markets.

Our graduates find employment in roles such as business and financial analysts, management consultants, government officials, and economists for banks and other financial organisations.

We also work with the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-Time Series Econometrics
-Estimation and Inference in Econometrics
-Dissertation (optional)
-Asset Pricing
-Financial Modelling
-MSc Finance and Investment: Dissertation (optional)
-Econometric Methods (optional)
-Mathematical Methods (optional)
-Monetary Economics (optional)
-Economic Development Theory (optional)
-Microeconomics (optional)
-Macroeconomics (optional)
-Political Economy (optional)
-Banking (optional)
-Economics of Financial Markets (optional)
-Topics in Financial Economics (optional)
-Behavioural Economics I: Individual Decision Making (optional)
-Computational Agent-Based Macro-Economics, Financial Markets and Policy Design (optional)
-Behavioural Economics II: Games and Markets (optional)
-Theory of Industrial Organisation (optional)
-International Trade (optional)
-International Finance (optional)
-Game Theory and Applications (optional)
-Economics of Incentives, Contracts and the Firm (optional)
-Microeconometrics (optional)
-Panel Data Methods (optional)
-Applications of Data Analysis (optional)
-Corporate Finance (optional)
-Derivative Securities (optional)
-Portfolio Management (optional)
-Exchange Rates and International Finance (optional)
-Behavioural Finance (optional)
-Risk Management (optional)
-Fixed Income Securities (optional)
-Trading Global Financial Markets (optional)
-Modern Banking (optional)
-Bank Strategy and Risk (optional)
-Industry Expert Lectures in Finance (optional)

Read less
This programme is designed for graduates in mathematics, engineering, computer science and finance/economics wishing to pursue careers in the financial services and banking industry. Read more
This programme is designed for graduates in mathematics, engineering, computer science and finance/economics wishing to pursue careers in the financial services and banking industry. The structure is of an interdisciplinary nature in which graduates coming from different disciplines collaborate to address the computational aspects of market risk. Our core philosophy is to equip our students with the appropriate knowledge in mathematical finance, focusing on a strong development of associated computational methods.

Our Royal Maritime (Heritage) London based campus, close to the financial district of Canary Wharf, enables the department to build ties with market practitioners permitting our students to become part of a wider financial group. Our seminar series, inviting both academics and practitioners, allows you to interact with our external links creating an advantageous learning experience. We provide the knowledge for you to build up your profile of understanding of current research practice in finance. You will be trained and equipped with the skills for derivatives pricing and make use of non-linear methods for quantitative analysis (programming in Matlab, R and VBA). Our classes include interactive applications that enhance your learning experience through innovative teaching. By utilising research expertise within the department you will graduate with a strong understanding of numerical methods. You will also develop an understanding for further applicability in relevant fields as in energy commodity markets, where part of our current research focuses on by combining the world leading Agent-Based research team with our Computational Finance applications for crude oil price modelling.

The programme welcomes both recent graduates as well as experienced professional practitioners who wish to further their skills. Programme assessments are all 100% coursework with problems relating to current market practice. A supervised dissertation project takes place after the end of the last teaching term during the summer months. Projects are allocated in March and students are encouraged to work on projects that provide genuine insight in financial markets analysis. The programme is also available on a part-time basis. For those already at employment the flexible part-time mode of study, two years typically but can be flexible, allows students to be committed to both the MSc programme and employment.

Visit the website http://www2.gre.ac.uk/study/courses/pg/maths/compfinance

Mathematics

Postgraduate mathematics students benefit from award-winning teaching and great facilities. Our programmes are informed by world-renowned research and our links with industry ensure our students develop the academic and practical skills that will enhance their career prospects.

What you'll study

Full time
- Year 1:
Students are required to study the following compulsory courses.

English Language Support Course (for Postgraduate Students in the School of Computing and Mathematical Sciences)
Financial Markets (Dual Award) (15 credits)
Masters Project (Maths) (60 credits)
Advanced Finite Difference Methods for Derivatives Pricing (15 credits)
Computational Methods (15 credits)
Mathematical Approaches to Risk Management (15 credits)
Mathematical Finance (30 credits)

Students are required to choose 15 credits from this list of options.

Scientific Software Design and Development (15 credits)
Inverse Problems (15 credits)

Students are required to choose 15 credits from this list of options.

Enterprise Software Engineering Development (15 credits)
Software Tools and Techniques (15 credits)
Actuarial Mathematics and Risk Modelling (15 credits)
Financial Time Series (15 credits)

Part time
- Year 1:
Students are required to study the following compulsory courses.

Computational Methods (15 credits)
Inverse Problems (15 credits)
Mathematical Finance (30 credits)

- Year 2:
Students are required to study the following compulsory courses.

Scientific Software Design and Development (15 credits)
Financial Markets (Dual Award) (15 credits)
Masters Project (Maths) (60 credits)
Advanced Finite Difference Methods for Derivatives Pricing (15 credits)
Mathematical Approaches to Risk Management (15 credits)

Fees and finance

Your time at university should be enjoyable and rewarding, and it is important that it is not spoilt by unnecessary financial worries. We recommend that you spend time planning your finances, both before coming to university and while you are here. We can offer advice on living costs and budgeting, as well as on awards, allowances and loans.

Find out more about our fees and the support available to you at our:
- Postgraduate finance pages (http://www.gre.ac.uk/finance/pg)
- International students' finance pages (http://www.gre.ac.uk/finance/international)

Assessment

100% coursework. Coursework assessment at the postgraduate level allows for better elaboration of ideas and expansion of knowledge. A supervised dissertation project takes places at the end of the teaching terms during the summer months. The Department is very keen to tackle dissertation topics.

Career options

Graduates are equipped with the tools needed to become quantitative analysts, work in risk and portfolio management as well as in the insurance sector. Our expert seminar series gives you the opportunity to interact with leading figures from industry and academia and undertake projects relating to current industry practice. A postgraduate qualification is a major achievement and a milestone in your specialised career path leading to a professional career. The Department also offers a PhD programme.

Find out how to apply here - http://www2.gre.ac.uk/study/apply

Read less
Would you like to undertake advanced study in quantitative analysis in an environment of research excellence? To question how we understand and assess empirical findings reported within applied economics?. Read more
Would you like to undertake advanced study in quantitative analysis in an environment of research excellence? To question how we understand and assess empirical findings reported within applied economics?

Our popular course emphasises the foundations of econometrics and its application to a wide range of topics in economics. You explore topics including:
-The statistical foundations for a variety of estimating methods
-Ways of testing economic hypotheses
-The classical linear regression model
-The use of asymptotic methods in econometrics
-The analysis of stock markets and other financial data

We are top 5 in the UK for research, with over 90% of our research rated as “world-leading” or “internationally excellent”. Much of this world-class research is related to policy, and we have particular strengths in the areas of:
-Game theory and strategic interactions
-Theoretical and applied econometrics
-Labour economics

The quality of our work is reflected in our stream of publications in high-profile academic journals, including American Economic Review, Econometrica, and Review of Economic Studies.

Professional accreditation

Our University is one of only 21 ESRC-accredited Doctoral Training Centres in the UK.

This means that our course can form part of a prestigious 1+3 funding opportunity worth up to £21,575.

Our expert staff

Study and work alongside some of the most prominent economists of our time.

Our researchers are at the forefront of their field and have even received MBEs, with students coming from across the globe to study, research or work with us.

Many of our researchers also provide consultancy services to businesses in London and other major financial centres, helping us to develop research for today's society as well as informing our teaching for the future.

Specialist facilities

Take advantage of our wide range of learning resources to assist you in your studies:
-Extensive software for quantitative analysis is available in all computer labs across the university
-Access a variety of economics databases and multiple copies of textbooks and e-books in the Albert Sloman Library

Your future

After completing your masters, you may wish to extend your knowledge with a research degree – many Essex graduates decide to stay here for further study.

Alternatively, our course also prepares you for employment; recent surveys have shown that higher degree graduates are more likely to obtain jobs at professional or managerial level.

On our course you will develop key employability skills including statistical analysis, mathematical techniques, research, analytical reasoning and modelling.

Our graduates find employment in roles such as business and financial analysts, management consultants, government officials, and economists for banks and other financial organisations.

We also work with the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-Dissertation
-Microeconomics
-Macroeconomics
-Time Series Econometrics
-Estimation and Inference in Econometrics
-Economic Development Theory (optional)
-Economics of Financial Markets (optional)
-Economics of Incentives, Contracts and the Firm (optional)
-Game Theory and Applications (optional)
-International Finance (optional)
-International Trade (optional)
-Mathematical Methods (optional)
-Microeconometrics (optional)
-Monetary Economics (optional)
-Panel Data Methods (optional)
-Political Economy (optional)
-Theory of Industrial Organisation (optional)
-Topics in Financial Economics (optional)
-Banking (optional)
-Behavioural Economics I: Individual Decision Making (optional)
-Computational Agent-Based Macro-Economics, Financial Markets and Policy Design (optional)
-Behavioural Economics II: Games and Markets (optional)
-Applications of Data Analysis (optional)

Read less
This innovative course in the growing area of behavioural science and behavioural economics combines multidisciplinary expertise from the Departments of Economics, Psychology and Warwick Business School. Read more
This innovative course in the growing area of behavioural science and behavioural economics combines multidisciplinary expertise from the Departments of Economics, Psychology and Warwick Business School. Warwick is one of the strongest places in the world to study behavioural science (flagged for excellence in the 2014 Research Excellence Framework), and one of the few to offer a truly interdisciplinary research and teaching team.

During the course you’ll focus on behavioural, experimental and neuroeconomics, decision-making and the principles of cognition. Methods explored include mathematical modelling of choice, agent-based simulation, econometrics and process-tracing methods (e.g. eye-tracking and brain-imaging).

You’ll also undertake a project, giving you the opportunity to collaborate with a team of researchers on live research projects. Past projects have included analysis of big data sets (e.g. Facebook profiles to large UK/US panel studies), large online experiments with thousands of participants, field experiments on consumer and economic behaviour, and laboratory studies of groups using economic games.

Our graduates continue to PhD research, or to work in the public and private sectors, applying behavioural science to public policy and business.

Science Track

The Science Track is intended for those with an undergraduate degree in science, or another quantitative subject. Students take a module in Behavioural Microeconomics in Term 1, which introduces classic microeconomics and the relationship to the new behavioural approach.

Read less
The Master in Data Science for Complex Economic Systems is a one year full-time postgraduate program (II level Master Degree) taught in English and welcomes students from different disciplines (e.g. Read more
The Master in Data Science for Complex Economic Systems is a one year full-time postgraduate program (II level Master Degree) taught in English and welcomes students from different disciplines (e.g. economics, physics, computer science and political science).
The Master provides theoretical and methodological tools to understand and analyse complex systems which typically exhibit nonlinear behaviour resulting from the interaction of heterogeneous agents, hierarchy and continuous innovation. These properties require exploring new visions and expanding education beyond the frontier of traditional economic studies. In addition to the standard toolkit of economics and econometrics, the program takes a path-breaking perspective on advanced analytics, including big data, machine-learning network analysis, and agent-based simulation. The Master applies this pioneering approach to modelling, management, forecasting and policymaking in innovation, urban and consumption systems.
The teaching method combines a thorough theoretical training with hands-on laboratories also in collaboration with top research centres and corporations. Our faculty is recruited from top schools (e.g. Écoles Normale Supérieure de Lyon, GREQUAM), on the international academic job market. The faculty also includes scholars from the University of Turin and the Collegio Carlo Alberto.
In addition to their coursework, students interact with the faculty, fellows, and researchers of the University of Turin and of the Collegio.
Master’s graduates can apply with full recognition of course credits for admission to the Vilfredo Pareto Doctorate in Economics (curriculum in Complexity) of the University of Turin.

APPLICATION DEADLINE:
Early applications (most welcome): March 31, 2016 - Regular applications: July 15, 2016

COURSES:
http://madas.carloalberto.org

Read less
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. Read more

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.

About this degree

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).

Core modules

  • Archaeological Data Science
  • Complexity, Space and Human History

Optional modules

  • Exploratory Data Analysis in Archaeology
  • GIS Approaches to Past Landscapes
  • GIS in Archaeology and History
  • Remote Sensing in Archaeology
  • Spatial Statistics, Network Analysis and Human History
  • The Archaeology of Complex Urban Sites: Analytical and Interpretative Technology
  • Web and Mobile GIS (by arrangement with the UCL Department of Civil and Geomatic Engineering
  • Other options available within the UCL Institute of Archaeology

Dissertation/report

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

Careers

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.

Employability

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.

Why study this degree at UCL?

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.

Research Excellence Framework (REF)

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.



Read less
Our modern world is witnessing a growth of online data in a variety of forms, including web documents, blogs, social networks, digital libraries and medical records. Read more
Our modern world is witnessing a growth of online data in a variety of forms, including web documents, blogs, social networks, digital libraries and medical records. Much of this data contains valuable information, such as emerging opinions in social networks, search trends from search engines, consumer purchase behaviour, and patterns that emerge from these huge data sources.

The sheer volume of this information means that traditional stand-alone applications are no longer suitable to process and analyse this data. Our course equips you with the knowledge to contribute to this rapidly emerging area.

We give you hands-on experience with various types of large-scale data and information handling, and start by providing you with a solid understanding of the underlying technologies, in particular cloud computing and high-performance computing. You explore areas including:
-Mobile and social application programming
-Human-computer interaction
-Computer vision
-Computer networking
-Computer security

You also obtain practical knowledge of processing textual data on a large scale in order to turn this data into meaningful information, and have the chance to work on projects that are derived from actual industry needs proposed by our industrial partners.

We are ranked Top 10 in the UK in the 2015 Academic Ranking of World Universities, with more than two-thirds of our research rated ‘world-leading’ or ‘internationally excellent (REF 2014).

This degree is accredited by the Institution of Engineering and Technology (IET).This accreditation is increasingly sought by employers, and provides the first stage towards eventual professional registration as a Chartered Engineer (CEng).

Our expert staff

Today’s computer scientists are creative people who are focused and committed, yet restless and experimental. We are home to many of the world’s top scientists, and our staff are driven by creativity and imagination as well as technical excellence. We are conducting world-leading research in areas such as evolutionary computation, brain-computer interfacing, intelligent inhabited environments and financial forecasting.

Specialist staff working on data analytics include:
-Dr Luca Citi – machine learning, learning from biological signals and data (EEG, etc)
-Dr Adrian Clark – automatic construction of vision systems using machine learning and evaluation of algorithms, data visualisation and augmented reality
-Professor Maria Fasli – analysis of structured/unstructured data, machine learning, adaptation, semantic information extraction, ontologies, data exploration, recommendation technologies
-Professor John Gan – machine learning for data modelling and analysis, dimensionality reduction and feature selection in high-dimensional data space
-Dr Udo Kruschwitz – natural language processing, analysis textual/unstructured data, information retrieval
-Professor Massimo Poesio – cognitive science of language, text mining, computational linguistics
-Professor Edward Tsang – applied AI, constraint satisfaction, computational finance and economics, agent-based simulations

Specialist facilities

We are one of the largest and best resourced computer science and electronic engineering schools in the UK. Our work is supported by extensive networked computer facilities and software aids, together with a wide range of test and instrumentation equipment.
-We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
-All computers run either Windows 7 or are dual boot with Linux
-Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
-Students have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
-We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors

Your future

Demand for skilled graduates in the areas of big data and data science is growing rapidly in both the public and private sector, and there is a predicted shortage of data scientists with the skills to understand and make commercial decisions based on the analysis of big data.

Our recent graduates have progressed to a variety of senior positions in industry and academia. Some of the companies and organisations where our former graduates are now employed include:
-Electronic Data Systems
-Pfizer Pharmaceuticals
-Bank of Mexico
-Visa International
-Hyperknowledge (Cambridge)
-Hellenic Air Force
-ICSS (Beijing)
-United Microelectronic Corporation (Taiwan)

We also work with the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

Postgraduate study is the chance to take your education to the next level. The combination of compulsory and optional modules means our courses help you develop extensive knowledge in your chosen discipline, whilst providing plenty of freedom to pursue your own interests. Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore to ensure your course is as relevant and up-to-date as possible your core module structure may be subject to change.

Big Data and Text Analytics - MSc
-MSc Project and Dissertation
-Information Retrieval
-Cloud Technologies and Systems (optional)
-Group Project
-High Performance Computing
-Machine Learning and Data Mining
-Natural Language Engineering
-Professional Practice and Research Methodology
-Text Analytics
-Advanced Web Technologies (optional)
-Data Science and Decision Making (optional)
-Big-Data for Computational Finance (optional)
-Computer Security (optional)
-Computer Vision (optional)
-Creating and Growing a New Business Venture (optional)
-Mobile & Social Application Programming (optional)

Read less
The Spatial Data Science and Visualisation MSc teaches cutting-edge data analysis, mining, modelling and visualisation techniques for spatial systems. Read more

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.

About this degree

Students gain a grounding in the principles and skills of spatial research, data analysis and visualisation, agent-based models and virtual environments, and develop an understanding of research methodology for data collection and analysis. Subject-specific modules provide students with the opportunity to develop skills in spatial analysis and to contribute to current debates in the field. They will learn programming skills in Java/Processing, Python, R, JavaScript and SQL, and the ability to use a range of interactive geospatial and visualisation tools (ArcGIS, Unity, Mapbox and CityEngine).

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).

Core modules

The core modules focus on technical skills, leading to applications in mapping, visualising and analysing spatial data.

  • Data Science for Spatial Systems
  • Geographic Information Systems and Science
  • Introduction to Programming
  • Quantitative Methods
  • Group Mini Project: Digital Visualisation

Elective modules

Students select two elective modules from a wide range available at UCL, subject to approval.

Dissertation/report

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

Funding

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.

Careers

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.

Employability

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.

Why study this degree at UCL?

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.



Read less

  • 1
Show 10 15 30 per page



Cookie Policy    X