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Masters Degrees (Computational Science)

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Our MSc in Applied Computational Science and Engineering will educate future domain-specialists in computational science. This course will expand your knowledge of numerical methods, computational science, and how to solve large scale problems by applying novel science and engineering approaches. Read more

Our MSc in Applied Computational Science and Engineering will educate future domain-specialists in computational science.

This course will expand your knowledge of numerical methods, computational science, and how to solve large scale problems by applying novel science and engineering approaches. It is suitable for graduates of disciplines including maths and physical sciences, geophysics and engineering, and computer science.

This immersive, hands-on MSc course will enable students to develop their skills and techniques for a range of science and engineering applications utilising High Performance Computing resources. Students will learn alongside world-class researchers in the Department of Earth Science and Engineering.

There will be a strong emphasis on high productivity problem solving using modern computational methods and technologies, including computer code development and parallel algorithms.

Applicants who want to pursue analytical careers in industry geoscience and engineering are a target for this course. Graduates will develop the skills necessary to enter the modern industrial workforce.

This MSc will also prepare for your PhD studies in fields such as computational techniques, numerical analysis, optimisation and inversion, fluid mechanics, heat transfer, and machine learning applications.

The Applied Computational Science and Engineering MSc programme will ensure that students are able to apply appropriate computational techniques to understand, define and develop solutions to a range of science and engineering problems.

Students will have the chance to participate in individual and group research projects as well as to write reports and present technical work, developing the project management and numerical skills that employers want.

Careers

Graduates of this course will fill the market demand for those with applied, hands-on computational experience who can solve real world problems.

Through the combination of programming, foundational domain knowledge and advanced numerical literacy that this course provides, graduates will be highly sought after to work as expert analysts in industry, for example oil and gas, mineral exploration and climate science.

Graduates will be in an ideal position to pursue academic careers in fields such as computational techniques, optimisation and inversion, fluid mechanics, and machine learning applications.

Further information

For full information on this course, including how to apply, see: http://www.imperial.ac.uk/study/pg/earth-science/computational-science/

If you have any enquiries you can contact our team at:



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The MSc in Data and Computational Science is designed for students from highly quantitative disciplines who wish to work in data analytics or computational science. Read more
The MSc in Data and Computational Science is designed for students from highly quantitative disciplines who wish to work in data analytics or computational science.

Computational science is at the crossroads between modern applied mathematics and statistics, and our programme recognizes this fact by combining aspects of both in a unique set of tailored modules including scientific computing, mathematical modelling, and data analytics.

- The programme will equip you to solve complex scientific problems and analyse large data sets using a range of theoretical tools, from deterministic mathematical modelling to Bayesian analysis.

- The intensive programming modules will allow you develop a range of sought-after skills in practical programming and data analytics, including applications in high-performance computing.

- Topical application areas are offered each year, including cryptography, numerical weather prediction, and financial mathematics. The dissertation will give you further hands-on experience in computational science and will allow you to apply the key theoretical and practical skills by working on a challenging research topic.

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The objective of this programme of study is to prepare professionals able to deal with complex systems using sophisticated mathematical tools, yet with an engineering attitude. Read more

Mission and goals

The objective of this programme of study is to prepare professionals able to deal with complex systems using sophisticated mathematical tools, yet with an engineering attitude. It harmonises a solid scientific background with a command of advanced methodologies and technologies. The programme is characterised by a continuous synergy between Applied Mathematics and Engineering disciplines- The students may choose among three specialisations:
- Computational Science and Engineering
- Applied Statistics
- Quantitative Finance

See the website http://www.polinternational.polimi.it/educational-offer/laurea-magistrale-equivalent-to-master-of-science-programmes/mathematical-engineering/

Career opportunities

The professional opportunities offered by this course are rather ample and varied: engineering consultancy companies that deal with complex computational problems; manufacturing or civil engineering companies where analyses based on the use of advanced mathematical tools are needed; banks, insurance companies and financial institutions making use of quantitative finance for risk analysis or forecast; companies that require statistical interpretation and the processing of complex data, or the simulation of different scenarios; public and private research institutes and laboratories.

Eligible students

Students holding a Bachelor degree in Mathematical Engineering, or in a related area with a solid background in the core disciplines of the programme, i.e. Applied Mathematics, Computer Science, Applied Physics or other Engineering disciplines are eligible for application. In particular, eligible students' past studies must include courses in different areas of Engineering (among Informatics, Economics & Business Organization, Electrotechnics, Automation, Electronics, Applied Physics, Civil Engineering) for at least 25% of the overall courses, as well as courses in different areas of Mathematics (Mathematical Analysis, Linear Algebra, Geometry, Probability, Statistics, Numerical Analysis, Optimization) for at least 33% of the overall courses.
The following tracks are available:
1. Computational Science and Engineering
2. Applied Statistics
3. Quantitative Finance

Eligible students must clearly specify the track they are applying for in their motivation letter.

Presentation

See http://www.polinternational.polimi.it/uploads/media/Mathematical_Engineering.pdf
The Master of Science in Mathematical Engineering (MSME) aims to form an innovative and flexible professional profile, endowed with a wide spectrum of basic scientific notions and engineering principles, together with a deep knowledge of modern pure and applied mathematical techniques. MSME is characterized by a continuous synergy between Mathematics and Engineering methods, oriented to the modelling, analysis and solution of complex planning, control and management problems, and provides the students with the possibility to face problems from various scientific, financial and/or technological areas. The MSME graduates can find employment in Engineering companies specialized in handling complex computational problems, requiring a multidisciplinary knowledge; in companies manufacturing industrial goods for which design analysis based on the use of advanced mathematical procedures are required; in service societies, banks, insurance companies, finance or consultant agencies for the statistical interpretation and the simulation of complex situations related to the analysis of large number of data (e.g. management and optimization of services, data mining, information retrieval) or for handling financial products and risk management; in public and private institutions. The programme is taught in English.

Subjects

Three main tracks available:
1. Computational Science for Engineering
Real and functional analysis; algorithms and parallel programming; numerical and theoretical analysis for partial differential equations; fluid mechanics; computational fluid dynamics advanced programming techniques for scientific computing;

2. Statistics
Real and functional analysis; algorithms and parallel programming; stochastic dynamical models; applied statistics, model identification and data analysis; Bayesian statistics

3. Mathematical Finance
Real and functional analysis; algorithms and parallel programming; stochastic differential equations; mathematical finance; financial engineering; model identification and data analysis.

In the motivation letter the student must clearly specify the track he/she is applying for.

See the website http://www.polinternational.polimi.it/educational-offer/laurea-magistrale-equivalent-to-master-of-science-programmes/mathematical-engineering/

For contact information see here http://www.polinternational.polimi.it/educational-offer/laurea-magistrale-equivalent-to-master-of-science-programmes/mathematical-engineering/

Find out how to apply here http://www.polinternational.polimi.it/how-to-apply/

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Graduate education in Computational Science and Engineering (CMSE) at Koç University is offered through an interdisciplinary program among the Departments of the College of Arts and Sciences and the College of Engineering. Read more
Graduate education in Computational Science and Engineering (CMSE) at Koç University is offered through an interdisciplinary program among the Departments of the College of Arts and Sciences and the College of Engineering. In this program graduate students are trained on modern computational science techniques and their applications to solve scientific and engineering problems. New technological problems and associated research challenges heavily depend on computational modeling and problem solving. Because of the availability of powerful and inexpensive computers model-based computational experimentation is now a standard approach to analysis and design of complex systems where real experiments can be expensive or infeasible. Graduates of the CMSE Program should be capable of formulating solutions to computational problems through the use of multidisciplinary knowledge gained from a combination of classroom and laboratory experiences in basic sciences and engineering. Individuals with B.S. degrees in biology, chemistry, physics, and related engineering disciplines should apply for graduate study in the CMSE Program.

Current faculty projects and research interests:

• Computational Biology & Bioinformatics
• Computational Chemistry
• Computational Physics
• Molecular Dynamics and Simulation
• Parallel and High Performance Computing
• Computational Fluid Dynamics
• Dynamical and Stochastic Systems
• Quantum Mechanics of Many Body Systems
• Electronic Design Automation
• Numerical Methods
• Simulation of Material Synthesis
• Structural Dynamics
• Biomedical Modeling and Simulation
• Virtual Environments

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Computer science has an ever-greater influence on our lives, and the technological breakthroughs of today shape the way we live tomorrow. Read more

Computer science has an ever-greater influence on our lives, and the technological breakthroughs of today shape the way we live tomorrow. Opportunities continue to grow for highly skilled practitioners, and the taught Masters course allows you to make great advancements on your existing skills and knowledge. By the end of the programme you will be equipped to thrive in one of the fastest moving industries in the world.

You will learn specialist computer science techniques and fundamental theories, but you’ll also have the opportunity to put this knowledge into practice by developing applications and working with real software. We cover a range of topics, including real time graphics and computational science, and you can choose from options including artificial intelligence, robotics and visualisation – built upon a solid foundation of good programming skills.

The course begins each September each year and lasts twelve months. There are two trimesters of taught modules, followed by an individual dissertation project during the third trimester. There is an option to take an extra trimester – immediately before the dissertation, adding an extra three months of study but letting you gain insights into industry through a module based on commercial practice, typically working with commercial clients and software.

The Department of Computer Science creates an excellent experience for students, with a supportive student community. We have an international reputation for our research activities, with a strong record of industrial and public grant funding. We are also affiliated with Microsoft’s DreamSpark programme, which allows you to access the latest Microsoft operating systems and development software for home use. Once registered with the department, you can download the software free of charge.

Study information

This MSc programme is designed to support students with various levels of computing and programming practice experience. However, it is particularly suited to those with significant experience of writing code.

There is a focus on the transition to further postgraduate study, with suitable content on professional skills and the importance of ethics for practising computer scientists.

In lectures you’ll benefit from a range of techniques, from interpreting complex ideas through interactive discussions, to live programming or other problem-solving demonstrations.

Core modules

  • C++ Programming and Design
  • Real Time Graphics
  • Image Analysis
  • Simulation and Artificial Intelligence
  • Visualization
  • Development Project

Optional modules

  • The Industrial Experience optional 60-credit single trimester module extends the MSc to a 4-trimestr length degree, enabling you to gain meaningful commercial experience

Dissertation

  • Up to 20,000 words. There is the option of taking this in either trimester 3 or trimester 4, depending on whether you take the Industrial Experience module.

Teaching and learning

We place a strong emphasis on practical laboratory sessions. These will significantly develop your core computer science skills, and enhance your employability through exposure to commercial projects.

As teamwork plays a key role in commercial software development and has great value as an employable skill, group work is used in a number of modules.

Assessment

Practical coursework is the main form of assessment, and you will design, build and test software solutions to a variety of problems. Written coursework is used to complement this. The largest assessment is the dissertation.

* All modules are subject to availability.

Future prospects

The MSc Advanced Computer Science programme is designed to open pathways to postgraduate research, as well as a wide range of careers. The computational science module introduces concepts and ideas which will help prepare you for further study; the industrial placement option, meanwhile, will enable you to practise your skills and knowledge of computer science in situ, giving you a useful insight and advantage when it comes to starting your career.

We have a range of inspirational extra-curricular activities including the Three Thing Game, Imagine Cup Worldwide Software Development Challenge, Really Useful Seminars and Global Game Jam. They are designed to boost your CV and employability, and taking part costs very little.

Our MSc graduates typically gain roles including: applications developer; business systems analyst; computer analyst; computer programmer; computer operations manager; data management analyst; database assistant; developer support engineer; games programmer; games programmer (engine design); information manager; IT design; IT systems manager; IT technician; IT user support; mobile developer; operations director; software designer; software developer; software engineer; solutions developer; systems engineer; technical sales; technical specialist; and web developer.



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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… Read more

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.

Programme structure

You will undertake the following:

Core courses (worth 100 credits in total):

  • Univariate Statistics and Methodology using R (10 credits)
  • Multivariate Statistics and Methodology using R (10 credits)
  • Psychological Research Skills (20 credits)
  • Current Topics in Psychological Research (10 credits)
  • Introduction to Developmental Cognitive Science (10 credits)
  • Research Methods for Developmental Cognitive Science (10 credits)
  • Seminar in Developmental Cognitive Science (10 credits)
  • Current Topics in Developmental Cognitive Science (10 credits)
  • Research Internship in Developmental Cognitive Science (10 credits)

2 option courses worth 20 credits in total:

  • Chosen from a wide range of courses relevant to Developmental Cognitive Science from Psychology or other disciplines, as approved by Programme Director (20 credits in total)

And a Dissertation in Developmental Cognitive Science (60 credits)

Learning outcomes

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:

  • enhance students’ understanding of key theoretical and practical issues about typical and atypical development in children and young people, from a cognitive science perspective
  • teach students how to conduct independent scientific research that addresses key issues in developmental cognitive science
  • provide advanced training in critical thinking skills

Students who successfully complete the programme will be able to:

  • carry out high quality original research in developmental cognitive science
  • evaluate published research studies in developmental cognitive science
  • make well-informed contributions to discussions about the interplay between developmental research and real-world applications/implications

Career opportunities

Career opportunities for graduates from this programme include:

  • undertaking a PhD in Developmental Cognitive Science or in a related field
  • undertaking a Professional Doctorate in Clinical or Educational Psychology (applicable only to students who have an accredited undergraduate degree in Psychology)
  • wide variety of careers where it is valuable to be able to use research skills, critical thinking skills and understanding of developmental processes to develop and evaluate practices and policies relating to children and young people – e.g. teaching, speech & language therapy, policy development in education, health and social care.

Scholarships and funding

Find out more about scholarships and funding opportunities:



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How can we develop secure and robust software? How can we teach computers to see? How can we make our software easily adaptable to new tasks? How do we improve learning by computers?. Read more
How can we develop secure and robust software? How can we teach computers to see? How can we make our software easily adaptable to new tasks? How do we improve learning by computers?

If you find these questions fascinating, you should choose the Master's degree programme in Computing Science in Groningen. This programme deals with both the theoretical and the practical aspects of computing.

You will develop and increase your in-depth knowledge within the areas of Distributed Systems, Software Engineering, Intelligent Systems, Computational Science and Visualization. Most courses offer insightful lectures covering both theory and practice. Apart from acquiring core computer science skills, mathematics and research methodology are also important.

In student colloquia research and presentation skills are developed. For those aiming to go into business rather than academia, project management and related topics are also offered. All skills acquired are actively used in research projects, either within the university or during business and industrial internships.

Because computer science is pivotal in many areas of research, students can easily participate in one of many research collaborations with other fields, such as bioinformatics, medicine and astronomy. In addition, contacts with many businesses facilitate internships in a variety of fields, matching the interests of most students.

Why in Groningen?

- Excellent computing facilities: Virtual Reality Cube, 12000+ processor, STELLA Blue-Gene type supercomputer, world's biggest touch screen
- Also offers Computational Science and Visualisation, in particular 3-D medical imagining

Job perspectives

Having completed the Master's degree programme in Computing Science, you will be spoilt for choice regarding future employment. There are hardly any areas of business or research which do not require computer scientists, either software engineers and architects, or researchers. Usually our students are offered suitable jobs within one month of graduating (or even before graduating).

After graduating, most of our alumni move on to software companies (or found their own) or to academia, usually by entering into PhD programmes.

Those in industry are generally promoted to managerial roles after a few years and, for example, become project managers.In academia the usual path is from PhD via post-doctoral researcher to university staff member, though quite a few of our successful PhD students have moved on to industry as well. Many MSc and PhD students have gone into the field of medical imaging, either at various universities or in the research labs of large companies. More and more are moving into bioinformatics.

Job examples

- PhD research position
- Work for a software company
- Medical imaging
- Bioinformatics

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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Erasmus Mundus Computational Mechanics at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017). Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Erasmus Mundus Computational Mechanics at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

Swansea University has gained a significant international profile as one of the key international centres for research and training in computational mechanics and engineering. As a student on the Master's course in Erasmus Mundus Computational Mechanics, you will be provided with in-depth, multidisciplinary training in the application of the finite element method and related state-of-the-art numerical and computational techniques to the solution and simulation of highly challenging problems in engineering analysis and design.

Key Features of Erasmus Mundus Computational Mechanics MSc

The Zienkiewicz Centre for Computational Engineering is acknowledged internationally as the leading UK centre for computational engineering research. It represents an interdisciplinary group of researchers who are active in computational or applied mechanics. It is unrivalled concentration of knowledge and expertise in this field. Many numerical techniques currently in use in commercial simulation software have originated from Swansea University.

The Erasmus Mundus MSc Computational Mechanics course is a two-year postgraduate programme run by an international consortium of four leading European Universities, namely Swansea University, Universitat Politècnica de Catalunya (Spain), École Centrale de Nantes (France) and University of Stuttgart (Germany) in cooperation with the International Centre for Numerical Methods in Engineering (CIMNE, Spain).

As a student on the Erasmus Mundus MSc Computational Mechanics course, you will gain a general knowledge of the theory of computational mechanics, including the strengths and weaknesses of the approach, appreciate the worth of undertaking a computational simulation in an industrial context, and be provided with training in the development of new software for the improved simulation of current engineering problems.

In the first year of the Erasmus Mundus MSc Computational Mechanics course, you will follow an agreed common set of core modules leading to common examinations in Swansea or Barcelona. In addition, an industrial placement will take place during this year, where you will have the opportunity to be exposed to the use of computational mechanics within an industrial context. For the second year of the Erasmus Mundus MSc Computational Mechanics, you will move to one of the other Universities, depending upon your preferred specialisation, to complete a series of taught modules and the research thesis. There will be a wide choice of specialisation areas (i.e. fluids, structures, aerospace, biomedical) by incorporating modules from the four Universities. This allows you to experience postgraduate education in more than one European institution.

Modules

Modules on the Erasmus Mundus MSc Computational Mechanics course can vary each year but you could expect to study the following core modules (together with elective modules):

Numerical Methods for Partial Differential Equations

Continuum Mechanics

Advanced Fluid Mechanics

Industrial Project

Finite Element Computational Analysis

Entrepreneurship for Engineers

Finite Element in Fluids

Computational Plasticity

Fluid-Structure Interaction

Nonlinear Continuum Mechanics

Computational Fluid Dynamics

Dynamics and Transient Analysis

Reservoir Modelling and Simulation

Accreditation

The Erasmus Mundus Computational Mechanics course is accredited by the Joint Board of Moderators (JBM).

The Joint Board of Moderators (JBM) is composed of the Institution of Civil Engineers (ICE), the Institution of Structural Engineers (IStructE), the Chartered Institution of Highways and Transportation (CIHT), and the Institute of Highway Engineers (IHE).

This degree is accredited as meeting the requirements for Further Learning for a Chartered Engineer (CEng) for candidates who have already acquired an Accredited CEng (Partial) BEng(Hons) or an Accredited IEng (Full) BEng/BSc (Hons) undergraduate first degree.

See http://www.jbm.org.uk for further information.

This degree has been accredited by the JBM under licence from the UK regulator, the Engineering Council.

Accreditation is a mark of assurance that the degree meets the standards set by the Engineering Council in the UK Standard for Professional Engineering Competence (UK-SPEC). An accredited degree will provide you with some or all of the underpinning knowledge, understanding and skills for eventual registration as an Incorporated (IEng) or Chartered Engineer (CEng). Some employers recruit preferentially from accredited degrees, and an accredited degree is likely to be recognised by other countries that are signatories to international accords.

Links with Industry

On the Erasmus Mundus MSc Computational Mechanics course, you will have the opportunity to apply your skills and knowledge in computational mechanics in an industrial context.

As a student on the Erasmus Mundus MSc Computational Mechanics course you will be placed in engineering industries, consultancies or research institutions that have an interest and expertise in computational mechanics. Typically, you will be trained by the relevant industry in the use of their in-house or commercial computational mechanics software.

You will also gain knowledge and expertise on the use of the particular range of commercial software used in the industry where you are placed.

Careers

The next decade will experience an explosive growth in the demand for accurate and reliable numerical simulation and optimisation of engineering systems.

Computational mechanics will become even more multidisciplinary than in the past and many technological tools will be, for instance, integrated to explore biological systems and submicron devices. This will have a major impact in our everyday lives.

Employment can be found in a broad range of engineering industries as this course provides the skills for the modelling, formulation, analysis and implementation of simulation tools for advanced engineering problems.

Student Quotes

“I gained immensely from the high quality coursework, extensive research support, confluence of cultures and unforgettable friendship.”

Prabhu Muthuganeisan, MSc Computational Mechanics



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Goal of the pro­gramme. Data science combines computer science and statistics to solve exciting data-intensive problems in industry and in many fields of science. Read more

Goal of the pro­gramme

Data science combines computer science and statistics to solve exciting data-intensive problems in industry and in many fields of science. Data scientists help organisations make sense of their data. As data is collected and analysed in all areas of society, demand for professional data scientists is high and will grow higher. The emerging Internet of Things, for instance, will produce a whole new range of problems and opportunities in data analysis.

In the Data Science master’s programme, you will gain a solid understanding of the methods used in data science. You will learn not only to apply data science: you will acquire insight into how and why methods work so you will be able to construct solutions to new challenges in data science. In the Data Science master’s programme, you will also be able to work on problems specific to a scientific discipline and to combine domain knowledge with the latest data analysis methods and tools. The teachers of the programme are themselves active data science researchers, and the programme is heavily based on first-hand research experience.

Upon graduating from the Data Science MSc programme, you will have solid knowledge of the central concepts, theories, and research methods of data science as well as applied skills. In particular, you will be able to

  • Understand the general computational and probabilistic principles underlying modern machine learning and data mining algorithms
  • Apply various computational and statistical methods to analyse scientific and business data
  • Assess the suitability of each method for the purpose of data collection and use
  • Implement state-of-the-art machine learning solutions efficiently using high-performance computing platforms
  • Undertake creative work, making systematic use of investigation or experimentation, to discover new knowledge
  • Report results in a clear and understandable manner
  • Analyse scientific and industrial data to devise new applications and support decision making.

The MSc programme is offered jointly by the Department of Computer Science, the Department of Mathematics and Statistics, and the Department of Physics, with support from the Helsinki Institute for Information Technology (HIIT) and the Helsinki Institute of Physics (HIP), all located on the Kumpula Science campus. In your applied data science studies you can also include multidisciplinary studies from other master's programmes, such as digital humanities, and natural and medical sciences.

Further information about the studies on the Master's programme website.

Pro­gramme con­tents

The Data Science MSc programme combines elements from computer science and mathematical sciences to provide you with skills in topics such as machine learning, distributed systems and statistical methods. You might also find that knowledge in a particular scientific field is useful for your future career. You can obtain this through elective studies in the MSc programme, or it might already be part of your bachelor-level degree.

Studies in the Data Science MSc programme include both theoretical and practical components, including a variety of study methods (lectures, exercises, projects, seminars; done both individually and in groups). Especially in applied data science, we also use problem-based learning methods, so that you can address real-world issues. You will also practise academic skills such as scientific writing and oral presentation throughout your studies. You are encouraged to include an internship in your degree in order to obtain practical experience in the field.

Elective studies give you a wider perspective of Data Science. Your elective studies can be an application area of Data Science (such as physics or the humanities), a discipline that supports application of Data Science (such as language technology), or a methodological subject needed for the development of new Data Science methods and models (such as computer science, statistics, or mathematics).



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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). Read more

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.

Key Features of the MSc Data Science

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

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

Facilities

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.

Career Destinations

- 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



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Technologies based on the intelligent use of data are leading to great changes in our everyday life. Data Science and Engineering refers to the know-how and competence required to effectively manage and analyse the massive amount of data available in a wide range of domains. Read more
Technologies based on the intelligent use of data are leading to great changes in our everyday life. Data Science and Engineering refers to the know-how and competence required to effectively manage and analyse the massive amount of data available in a wide range of domains.

We offer a two-year Master of Science in Computer Science centered on this emerging field. The backbone of the program is constituted by three core units on advanced data management, machine learning, and high performance computing. Leveraging on the expertise of our faculty, the rest of the program is organised in four tracks, Business Intelligence, Health & Life Sciences, Pervasive Computing, and Visual Computing, each providing a solid grounding in data science and engineering as well as a firm grasp of the domain of interest.

By blending standard classes with recitations and lab sessions our program ensures that each student masters the theoretical foundations and acquires hands-on experience in each subject. In most units credit is obtained by working on a final project. Additional credit is also gained through short-term internship in the industry or in a research lab. The master thesis is worth 25% of the total credit.

TRACKS

• Business Intelligence. This track builds on first hand knowledge of business management and fundamentals of data warehousing, and focuses on data mining, graph analytics, information visualisation, and issues related to data protection and privacy.
• Health & Life Sciences. Starting from core knowledge of signal and image processing, bioinformatics and computational biology, this track covers methods for biomedical image reconstruction, computational neuroengineering, well-being technologies and data protection and privacy.
• Pervasive Computing. Security and ubiquitous computing set the scene for this track which deals with data semantics, large scale software engineering, graph analytics and data protection and privacy.
• Visual Computing. This track lays the basics of signal & image processing and of computer graphics & augmented reality, and covers human computer interaction, computational vision, data visualisation, and computer games.

PROSPECTIVE CAREER

Senior expert in Data Science and Engineering. You will be at the forefront of the high-tech job market since all big companies are investing on data driven approaches for decision making and planning. The Business Intelligence area is highly regarded by consulting companies and large enterprises, while the Health and Life Sciences track is mainly oriented toward biomedical industry and research institutes. Both the Pervasive and the Visual Computing tracks are close to the interests of software companies. For all tracks a job in a start-up company or a career on your own are always in order.

Senior computer scientist.. By personalizing your plan of study you can keep open all the highly qualified job options in software companies.

Further graduate studies.. In all cases, you will be fully qualified to pursue your graduate studies toward a PhD in Computer Science.

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Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. Read more

Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. This MSc will equip students with the analytical tools to design sophisticated technical solutions using modern computational methods and with an emphasis on rigorous statistical thinking.

About this degree

The programme combines training in core statistical and machine learning methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical computing and modelling. Students choosing the statistics specialisation will take one compulsory module and up to two additional modules in computer science, with the remaining modules (including the research project) taken mainly from within UCL Statistical Science.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research dissertation/report (60 credits).

Core modules

  • Introduction to Statistical Data Science
  • Introduction to Machine Learning
  • Statistical Design of Investigations
  • Statistical Computing

Optional modules

At least two from a choice of Statistical Science modules including:

  • Applied Bayesian Methods
  • Decision & Risk
  • Factorial Experimentation
  • Forecasting
  • Quantitative Modelling of Operational Risk and Insurance Analytics
  • Selected Topics in Statistics
  • Stochastic Methods in Finance I
  • Stochastic Methods in Finance II
  • Stochastic Systems

Up to two from a choice of Computer Science modules including:

  • Affective Computing and Human-Robot Interaction
  • Graphical Models
  • Statistical Natural Language Processing
  • Information Retrieval & Data Mining

Dissertation/report

All students undertake an independent research project, culminating in a dissertation usually of 10,000-12,000 words. Workshops running during the teaching terms provide preparation for this project and cover the communication of statistics.

Teaching and learning

The programme is delivered through a combination of lectures, tutorials and classes, some of which are dedicated to practical work. Assessment is through written examination and coursework. The research project is assessed through the dissertation and a 15-minute presentation.

Further information on modules and degree structure is available on the department website: Data Science MSc

Careers

Graduates from UCL Statistical Science typically enter professional employment across a broad range of industry sectors or pursue further academic study. 

The Data Science MSc is a new programme with the first cohort of students due to graduate in 2017. Recent career destinations for graduates of the related Statistics MSc include:

  • Management Associate, HSBC
  • Statistical Analyst, Nielsen
  • PhD in Statistics, UCL
  • Mortgage Specialist, Citibank
  • Research Assistant Statistician, Cambridge Institute of Public Health

Employability

Data science professionals are likely to be increasingly sought after as the integration of statistical and computational analytical tools becomes essential in all kinds of organisations and enterprises. A thorough understanding of the fundamentals is to be expected from the best practitioners. For instance, in applications in marketing, the healthcare industry and banking, computational skills should should be accompanied by statistical expertise at graduate level. Data scientists need a broad background knowledge so that they will be able to adapt to rapidly evolving challenges. Recent graduates from the related Statistics MSc have been offered positions as research analysts or consultants, and job opportunities in these areas are increasing.

Why study this degree at UCL?

UCL Statistical Science has a broad range of research interests, but has particular strengths in the area of computational statistics and in the interface between statistics and computer science.

UCL's Centre for Computational Statistics and Machine Learning, in which many members of the department are active, has a programme of seminars, masterclasses and other events. UCL's Centre for Data Science and Big Data Institute are newer developments, again with strong involvement of the department, where emphasis is on research into big data problems.

UCL is one of the founding members of the Alan Turing Institute, and both UCL Statistical Science and UCL Computer Science will be playing major roles in this exciting new development which will make London a major focus for big data research.

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: Statistical Science

82% 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.



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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Computer Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017). Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Computer Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

The MSc in Computer Science course is for you if you are a graduate from one of a wide range of disciplines and are looking to change direction or because of the needs of your chosen career, require a solid foundation in Computer Science.

As the use of computers and computer based systems continues to grow in all aspects of life, at home and at work, it is apparent that there will be for years to come a need for many people who can combine a knowledge of Computer Science, the discipline that underlies Information Technology, and degree level knowledge in a wide variety of other disciplines.

Over the duration of the MSc Computer Science course you will study a variety of modules taught by academic staff that are part of internationally renowned research groups. The course is also regularly updated to ensure that it keeps pace with the rapid developments in Computer Science.

Key Features of Computer Science MSc

• We are top in the UK for career prospects*

• We are 3rd in the UK for teaching quality**

• 5th in the UK overall*

• 7th in the UK for student satisfaction with 98% [National Student Survey 2016]

• 7th in the UK overall and Top in Wales*

• High employability prospects - we are 8th in the UK for graduate prospects*

• 92% in graduate employment or further study six months after leaving University [HESA data 2014/15]

• UK TOP 20 for Research Excellence [Research Excellence Framework 2014]

• Our Project Fair allows students to present their work to local industry

• Strong links with industry

• £31m Computational Foundry for computer and mathematical sciences will provide the most up-to-date and high quality teaching facilities featuring world-leading experimental set-ups, devices and prototypes to accelerate innovation and ensure students will be ready for exciting and successful careers. (From September 2018)

*Guardian University Guide 2017

**Times & Sunday Times University Guide 2016

Modules of Computer Science MSc

Modules for the MSc in Computer Science include Computer Science Project Research Methods but please visit our course page for more information.

Facilities

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.

Careers

All Computer Science courses will provide you the transferable skills and knowledge to help you take advantage of the excellent employment and career development prospects in an ever growing and changing computing and ICT industry.

94% of our Postgraduate Taught Graduates of Computer Science were in professional level work or study [DLHE 14/15].

Student Profile

“I chose the MSc Computer Science as a conversion from my previous War and Society degree, primarily employment opportunities. The course was by no means easy for me coming from an arts background, and the first few weeks I felt a little over my head, but thanks to the truly stimulating content from the syllabus and the high quality of the teaching within the department I soon caught up and began to thrive on the course. My project revolved around a comparative study of the Haskell Web-Framework Yesod and ASP.NET. During the completion of this I picked up many of the skills that I now use on an everyday basis in my role at Kinspeed (A Sheffield based Software House). Since starting work I have been able to apply many of the skills I obtained during my time at Swansea and have no doubt that choosing to study the MSc Computer Science at Swansea was one of the better decisions of my life.”

Chris Swires

Research

The results of the Research Excellence Framework (REF) 2014 show that Swansea Computer Science ranked 11th in the UK for percentage of world-leading research, and 1st in Wales for research excellence. 40% of our submitted research assessed as world-leading quality (4*).



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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Theoretical Computer Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017). Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Theoretical Computer Science at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

Computer Science is at the cutting edge of modern technology, is developing rapidly, and Swansea Computer Science graduates enjoy excellent employment prospects.

Computer Science now plays a part in almost every aspect of our lives - science, engineering, the media, entertainment, travel, commerce and industry, public services and the home.

The MSc by Research Theoretical Computer Science enables students to pursue a one year individual programme of research. The

Theoretical Computer Science programme would normally terminate after a year. However, under appropriate circumstances, this first year of research can also be used in a progression to Year 2 of a PhD degree.

As a student of the Theoretical Computer Science MSc by Research programme, you will be fully integrated into one of our established computer science research groups and participate in research activities such as seminars, workshops, laboratories, and field work.

Key Features of Theoretical Computer Science

The Department of Computer Science is amongst the top 25 in the UK, with a growing reputation in research both nationally and internationally in computer science. It is home to world class researchers, excellent teaching programmes and fine laboratory facilities.

All postgraduate Computer Science programmes will provide you the transferable skills and knowledge to help you take advantage of the excellent employment and career development prospects in an ever growing and changing computing and ICT industry.

Facilities

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.

Research

The results of the Research Excellence Framework (REF) 2014 show that we lead Wales in the field of Computer Science and are in the UK Top 20.

We are ranked 11th in the UK for percentage of world-leading research, and 1st in Wales for research excellence. 40% of our submitted research assessed as world-leading quality (4*).

Links with Industry

Each spring, Computer Science students prepare and present a poster about their project at a project fair – usually together with a system or software demonstration. The Department of Computer Science also strongly encourages students to create CVs and business cards to take along to the fair, as businesses and employers visit to view the range of projects and make contact with the graduating students.



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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. Read more

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:

  • A firm grounding in the theory of data mining, statistics and machine learning
  • Hands-on practical real world applications such as social media, biomedical data and financial data with Hadoop (used by Yahoo!, Facebook, Google, Twitter, LinkedIn, IBM, Amazon, and many others), R and other specialised software
  • The opportunity to work with real-world software such as Apache

Modules & structure

You will study the following core modules:

You will also choose from an anually approved list of modules which may include:

Skills & careers

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: 

  • Data Scientist
  • Data Mining Analyst
  • Big Data Analyst
  • Hadoop Developer
  • NoSQL Database Developer
  • R Programmer
  • Python Programmer
  • Researcher in Data Science and Data Mining

Find out more about employability at Goldsmiths.



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