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Programme Description. Read more

Programme Description

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.





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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- http://www.gold.ac.uk/pg/msc-data-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- http://www.gold.ac.uk/pg/msc-data-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

Contact the department

If you have specific questions about the degree, contact the Programme Director, Dr Daniel Stamate.

Modules & Structure

You will study the following:
Data Programming- 15 credits
Data Science Research- 15 credits

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

Funding

The Department of Computing offers a number of scholarships for students with remarkably good applications. The scholarships will be a one-off payment of £2,000. You don't need to submit a separate application to be considered for one of these awards. You can find out more from the department.

Funding

Please visit http://www.gold.ac.uk/pg/fees-funding/ for details.

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This programme provides students with thorough grounding in the theory, technical and practical skills in the increasingly critical field of Data Science. Read more
This programme provides students with thorough grounding in the theory, technical and practical skills in the increasingly critical field of Data Science. It includes optional modules in various application domains, drawing on the unique strengths of King's in health, law, the arts and humanities, and the social sciences. The programme will conclude with an individual project, focused either on analysing data from a particular domain, or on exploring computational or statistical methods.

Key benefits

- Unrivalled location in the heart of London giving access to major libraries and leading scientific societies, including the BCS Chartered Institute for IT, and the Institution of Engineering and Technology (IET).

- Equips graduates with an in-depth understanding of the general principles of the computational and statistical methodologies and methods used in data science, and their underlying assumptions and limitations

- Provides students with the skill set required to plan, undertake, manage, and critically assess a data science project.

- Access to speakers of international repute through seminars and external lectures, enabling students to keep abreast of emerging knowledge in advanced computing and related fields.

Visit the website: http://www.kcl.ac.uk/study/postgraduate/taught-courses/data-science-msc.aspx

Course detail

- Description -

An MSc in Data Science will provide you with the practical skills needed to effectively assemble, collate, store, manage and retrieve data required for data science projects and the critical judgement to decide the appropriate statistical and computational data exploration or analysis techniques to evaluate data science activities and projects.

- Course purpose -

The purpose of this degree programme is to train graduates from quantitative disciplines or with relevant quantitative work experience in current methods and techniques of data science, particularly the science of large-scale data collections. These methods and techniques include both computational techniques and methods from mathematical statistics. The MSc will also provide you with an appreciation for the professional, ethical and legal responsibilities of the data scientist, along with standard conceptual or scientific models in at least one domain of application of data science. Your individual project will typically aim to apply these methods to a problem in a specific application domain, and provide valuable preparation for a career in research or industry.

- Course format and assessment -

Lectures; tutorials; seminars; laboratory sessions; optional career planning workshops. Assessed through: coursework; written examinations; final project report.

Core modules:

- Data Science Individual Projects
- Data Mining & Machine Learning
- Simulation & Reseach Methodology

(without a Computer Science undergraduate degree):

- Computer Programming
- Databases, Data Warehousing & Information Retrieval

Career prospects

Via the Department’s Careers Programme, students are able to network with top employers and obtain advice on how to enhance career prospects.

How to apply: http://www.kcl.ac.uk/study/postgraduate/apply/taught-courses.aspx

About Postgraduate Study at King’s College London:

To study for a postgraduate degree at King’s College London is to study at the city’s most central university and at one of the top 20 universities worldwide (2015/16 QS World Rankings). Graduates will benefit from close connections with the UK’s professional, political, legal, commercial, scientific and cultural life, while the excellent reputation of our MA and MRes programmes ensures our postgraduate alumni are highly sought after by some of the world’s most prestigious employers. We provide graduates with skills that are highly valued in business, government, academia and the professions.

Scholarships & Funding:

All current PGT offer-holders and new PGT applicants are welcome to apply for the scholarships. For more information and to learn how to apply visit: http://www.kcl.ac.uk/study/pg/funding/sources

Free language tuition with the Modern Language Centre:

If you are studying for any postgraduate taught degree at King’s you can take a module from a choice of over 25 languages without any additional cost. Visit: http://www.kcl.ac.uk/mlc

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This Masters in Research Methods of Psychological Science will provide you with both theoretical instruction and practical experience in the methods appropriate for scientific research in psychology. Read more
This Masters in Research Methods of Psychological Science will provide you with both theoretical instruction and practical experience in the methods appropriate for scientific research in psychology.

Why this programme

-With a 95% overall student satisfaction in the National Student Survey 2015, the School of Psychology continues to meet student expectations combining both teaching excellence and a supportive learning environment.
-This MSc complies with requirements for the PhD research training programme of the Economic & Social Research Council (ESRC) and can either be the first year of a ‘1+3’ funding package or qualify you for future ‘+3’ funding.
-The University of Glasgow’s School of Psychology is consistently ranked amongst the top 10 in the UK and top 5 in Scotland, recently achieving 1st in Scotland and 2nd in the UK (Guardian University Guide 2015).
-You will benefit from innovative assessment, including portfolio of professional skills, peer review and writing up a research project in the format of a journal article.
-You will benefit from access to the resources of the University’s Centre for Cognitive Neuroimaging (CCNi), including a 3 Tesla fMRI scanner, MEG system, two TMS labs, and several EEG labs, including fMRI compatible systems. Dedicated motion capture suites record precise 3D body movements and facial animation sequences. Eye movements can be followed remotely using our SR Research EyeLink systems.
-The programme has excellent career prospects and a very good track record of previous graduates.

Programme structure

Modes of delivery of the MSc in Research Methods of Psychological Science include lectures, seminars and tutorials and lab work.

Core courses
-Introduction to Matlab programming
-Professional skills
-Research methods in cognitive science
-Statistics and research design
-Advanced qualitative methods
-Designing a research project
-Research project

Optional courses (one chosen)
-Cognitive brain imaging methods
-Computational neuroscience
-Formal models and quantitative methods*
-Psychology of language*
-Sleep and circadian timing*
-Visual perception and cognition*

*Each of these options will only run if the minimum number of students (>3) enrol.

You will also attend Scottish universities’ psychology postgraduate meetings, research seminars and journal clubs.

Research excellence

Research across the subject of Psychology attempts to advance our understanding of behaviour and the underlying mental processes and brain functions at multiple levels of analysis. This effort entails the integration of diverse approaches and paradigms from experimental psychology, cognitive science and the cognitive neurosciences.

We are committed to producing basic and applied research of the highest quality with a focus on three main areas:
-An interdisciplinary effort to advance the understanding of the complex relationship between the brain, cognition and behaviour. This brings together researchers with an interest in cognitive neuroscience, functional neuroimaging, neuropsychology and computational modelling. The Centre for Cognitive Neuroscience (CCNi) aims to develop new methods for understanding brain mechanisms, and to train interdisciplinary scientists in the use of those methods and techniques.
-The new science of social interactions, a science that blends behavioural, computational and neuroimaging techniques to investigate human social function, communication and cooperation. Our research examines a range of mechanisms that underlie social interaction: from gestures and expressive signals, from the face, voice and body to language-based communication. We have special interest in how such local interactions affect the dynamics and structure of larger scale social networks.
-Further research areas include sleep, language, visual perception, computational methods, memory, thought and social interaction.

Career prospects

As this programme complies with ESRC requirements, successful graduates from the programme are eligible for +3 ESRC PhD studentships. The majority of our graduates have obtained PhD funding or secured a research or teaching position. Others have opted for further professional training in specialised fields of psychology. Some graduates have used the qualification and skills to advance in their current employment.

Graduates of this programme have gone on to positions such as: Assistant Psychologist at NHS and PhD studentships at Glasgow University or other HEIs in UK or abroad.

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Data is being collected at an unprecedented speed and scale – but 'big data' is of little use without 'big insight'. The skills required to develop such insight are in short supply and the shortage of skilled workers in the data analytics market is cited as a key barrier. Read more

About the course

Data is being collected at an unprecedented speed and scale – but 'big data' is of little use without 'big insight'. The skills required to develop such insight are in short supply and the shortage of skilled workers in the data analytics market is cited as a key barrier.

The Data Science and Analytics MSc programme provides these skills, combining a strong academic programme with hands-on experience of leading commercial technology – and the chance to gain industry certification.

You will develop both your critical awareness of the state-of-the-art in data science and the practical skills that help you apply data science more effectively in the business, science and social world.

The programme is run in conjunction with SAS, a market leader in business analytics software and services, and the largest independent vendor in the business intelligence market.

Brunel's programme is unique in being the only current MSc programme that is fully integrated with SAS, providing the SAS base certification.

Aims

The Harvard Business Review calls data science the “sexiest job of the 21st century” – with demand for graduates with SAS skills rapidly rising across financial, retail and government sectors. Data science is now in vogue.

From government, social networks and ecommerce sites to sensors, smart meters and mobile networks, data is being collected at an unprecedented speed and scale – creating an expanding job market for qualified data analysts.

The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (e.g. SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). This experience is designed, in part, to develop skills in preparation for the SAS certification part of the programme.

By the end of the course you should be able to:

Comprehend the key concepts and nuances of the disciplines that need to be synthesised for effective data science.
Demonstrate a critical understanding of the challenges and issues arising from taking heterogeneous data at volume and scale, understanding what it represents and turning that understanding into insight for business, scientific or social innovation (i.e. data science).
Develop a practical understanding of the skills, tools and techniques necessary for the effective application of data science.
Apply a practical understanding of data science to problems in social, business and scientific domains.
Evaluate the effectiveness of applied data science in relation to the issues addressed.

Course Content

Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (e.g. SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). That experience is designed, in part, to develop skills for the SAS certification that partners the programme.

Typical Modules:

Digital Innovation
Quantitative Data Analysis
High Performance Computational Infrastructures
Systems Project Management
Big Data Analytics
Research Methods
Data Visualisation
Learning Development Project
Dissertation

Special Features

SAS Certification
As an integral part of the programme, you will gain hands-on experience of commercial SAS tools – SAS being the market leader in business analytics software and services, and the largest independent vendor in the business intelligence market.
You will have the opportunity to obtain SAS certification (e.g. SAS Base Programming) which is a recognised industry qualification, following a two week SAS certification ‘boot camp’ preparation course.

Women in Engineering and Computing Programme

Brunel’s Women in Engineering and Computing mentoring scheme provides our female students with invaluable help and support from their industry mentors.

Teaching

Module are typically presented in a mixture of lecture and seminar/lab format. However, where appropriate other teaching methods will also be incorporated. All our learning environments are supported by the market leader in Virtual Learning Environments (VLE), the BlackboardLearn system.

Assessment

Your learning will be evaluated through a combination of in module assessments and more traditional exams, with module specific assessments – for example, presentations within the Learning Development Project.

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The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses. Read more
The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses. Technology is growing and evolving at an incredible speed, and both the rate of growth of data we generate and the devices we use to process it can only increase.

Data science is a growing and important field of study with a fast-growing number of jobs and opportunities within the private and public sector. The application of theory and methods to real-world problems and applications is at the core of data science, which aims especially to use and to exploit big data.

If you are interested in solving real-world problems, you like to develop skills to use smart devices efficiently, you want to use and to foster your understanding of mathematics, and you are interested and keen to use statistical techniques and methods to interpret data, MSc Data Science at Essex is for you. You study a balance of solid theory and practical application including:
-Computer science
-Programming
-Statistics
-Data analysis
-Probability

Our Department of Mathematical Sciences has an international reputation in many areas including semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology.

You also benefit from being taught in our School of Computer Science and Electronic Engineering, who are ranked Top 10 in the UK in the 2015 Academic Ranking of World Universities, with more than two-thirds of their research rated ‘world-leading’ or ‘internationally excellent’ (REF 2014).

The collaborative work between our departments has resulted in well-known research in areas including artificial intelligence, data analysis, data analytics, data mining, data science, machine learning and operations research.

Our expert staff

Our Department of Mathematical Sciences is a small but influential department, so our students and staff know each other personally. You never need an appointment to see your tutors and supervisors, just knock on our office doors – we are one of the few places to have an open-door policy, and no issue is too big or small.

The academic staff in our School of Computer Science and Electronic Engineering 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 Paul Scott, who researches data mining, models of memory and attention, and artificial intelligence, and Professor Maria Fasli, who researches data exploration, analysis and modelling of complex, structured and unstructured data, big data, cognitive agents, and web search assistants.

Specialist facilities

-Unique to Essex is our renowned Maths Support Centre, which offers help to students, staff and local businesses on a range of mathematical problems. Throughout term-time, we can chat through mathematical problems either on a one-to-one or small group basis
-We have our own computer labs for the exclusive use of students in the Department of Mathematical Sciences – in addition to your core maths modules, you gain computing knowledge of software including Matlab and Maple
-We have six laboratories that are exclusively for computer science and electronic engineering students
-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
-You 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
-We host regular events and seminars throughout the year
-Collaborate with the Essex Institute of Data Analytics and Data Science (IADS) and the ESRC Business and Local Government (BLoG) Data Research Centre of the University of Essex
-The UK Data Archive and the Institute for Social and Economic Research (ISER) at Essex contribute to our internationally outstanding data science environment

Your future

With a predicted shortage of data scientists, now is the time to future-proof your career. Data scientists are required in every sector, carrying out statistical analysis or mining data on social media, so our course opens the door to almost any industry, from health, to government, to publishing.

Our graduates are highly sought after by a range of employers and find employment in financial services, scientific computation, decision making support and government, risk assessment, statistics, education and other sectors.

We also offer supervision for PhD, MPhil and MSc by Dissertation. We have an international reputation in many areas such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology, and our staff are strongly committed to research and to the promotion of graduate activities.

We additionally work with our Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-Dissertation (optional)
-MSc Project and Dissertation (optional)
-Applied Statistics
-Machine Learning and Data Mining
-Modelling Experimental Data
-Text Analytics
-Artificial Neural Networks (optional)
-Bayesian Computational Statistics (optional)
-Big-Data for Computational Finance (optional)
-Combinatorial Optimisation (optional)
-High Performance Computing (optional)
-Natural Language Engineering (optional)
-Nonlinear Programming (optional)
-Professional Practice and Research Methodology (optional)
-Programming in Python (optional)
-Information Retrieval (optional)
-Data Science and Decision Making (optional)
-Research Methods (optional)
-Statistical Methods (optional)
-Stochastic Processes (optional)

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The MSc Statistics (Social Statistics) aims to provide high-level training in the theory and application of modern statistical methods, with a focus on methods commonly used in the social sciences. Read more

About the MSc programme

The MSc Statistics (Social Statistics) aims to provide high-level training in the theory and application of modern statistical methods, with a focus on methods commonly used in the social sciences. You will gain insights into the design and analysis of social science studies, including large and complex datasets, study the latest developments in statistics, and learn how to apply advanced methods to investigate social science questions.

The programme includes two core courses which provide training in fundamental aspects of probability and statistical theory and methods, the theory and application of generalised linear models, and programming and data analysis using the R and Stata packages. These courses together provide the foundations for the optional courses on more advanced statistical modelling, computational methods and statistical computing. Options also include specialist courses from the Departments of Methodology, Economics, Geography and Social Policy. Students on the taught master’s programme will take optional courses to the value of two units, while those on the research track will substitute one unit with a dissertation.

Graduate destinations

The programme will prepare graduates for work within the public sector, market research organisations and survey research organisations, or for further study.

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The Data Science and Computational Intelligence MSc has been designed to provide an industry-relevant programme that meets the needs of individuals wishing to pursue a research and development career in data science and computational intelligence. Read more
The Data Science and Computational Intelligence MSc has been designed to provide an industry-relevant programme that meets the needs of individuals wishing to pursue a research and development career in data science and computational intelligence.

Students will acquire knowledge, skills and expertise required for the analysis, interpretation and visualisation of complex, high-volume, high-dimensional and structured/unstructured data from varying sources. The programme will be delivered through activity-led and problem-based learning in the context of current research or industrial consultancy projects conducted by the academics teaching on the course. Students will explore cutting-edge research topics and technologies in order to maximise their professional career prospects.

The course has a carefully designed set of options that allows the individual to customise their programme of study according to their preferences, strengths and future plans.

WHY CHOOSE THIS COURSE?

If you choose this course you will benefit from the excellent modern facilities including specialist computing labs with high-performance hardware and industry-standard software. There will be opportunities for joint projects with local companies, guest lectures and interacting with employers. You will also have the opportunity to get involved in projects pursued by our research groups in: Computational Intelligence; Intelligent Information Modelling and retrieval; Distributed Systems and Modelling; Interactive Worlds; Digital Security and Forensics and Biomedical Computing and Engineering Technologies. Every year our students present their best work at the yearly Computing Show which attracts abundance of potential employers. You will work in collaborative international environment, which reflects the globalised nature of the computing industries. You can also get involved in variety of extra curricula activities including social events, trips and computing.

WHAT WILL I LEARN?

Mandatory study topics
-Artificial Neural Networks (15 credits)
-Machine Learning and Data Mining (15 credits)
-Fuzzy Logic and Evolutionary Computing (15 credits)
-Intelligent Information retrieval (15 credits)
-Business Intelligence and Big Data Processing (15 credits)
-Cloud Computing and Distributed Technologies (15 credits)
-Project dissertation (60 credits)

The remainder of the programme is bespoke, made up of topics from areas such as:
-IT Project Management (15 credits)
-Internet Systems Development (15 credits)
-Open Systems Application Development (15 credits)

The Data Science and Computational Intelligence postgraduate programme includes the completion of an individual project. Guided by an expert tutor, the MSc project serves to provide a method of applying previous learning whilst further developing the skills necessary to carry out research and facilitate the acquisition of valuable professional experience integral to that of a computer professional.

The MSc project serves to integrate and apply the subjects studied. The project could be industry-based or undertaken in collaboration with one of the University research groups, within the cognate area of this MSc.

HOW WILL THIS COURSE ENHANCE MY CAREER PROSPECTS?

The course presents existing opportunities in pursuing careers as data scientists, data professionals and data analysts in variety of sectors including financial services, retail, marketing, customer and business intelligence. On Completion of the course, graduates should be equipped with sought after, specialist knowledge and skills by industries which deal with very large volumes of data.

GLOBAL LEADERS PROGRAMME

Centre for Global Engagement logoTo prepare students for the challenges of the global employment market and to strengthen and develop their broader personal and professional skills Coventry University has developed a unique Global Leaders Programme.

The objectives of the programme, in which postgraduate and eligible undergraduate students can participate, is to provide practical career workshops and enable participants to experience different business cultures.

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This intensive programme in data science and software engineering is designed for graduates who are new to computer science and provides an excellent grounding for working as a data scientist or analyst in industry. Read more
This intensive programme in data science and software engineering is designed for graduates who are new to computer science and provides an excellent grounding for working as a data scientist or analyst in industry. You will gain a broad knowledge of computing and acquire programming and data analysis skills, as well as comprehensive, practical problem-solving and analytical skills. You will also critically explore current research and methodologies and have the opportunity to investigate an area of current research in more depth via a project.

If you are new to computer science, this programme provides a solid foundation for a career in IT as a data scientist or analyst. For those already working in IT, the programme is an ideal opportunity to strengthen and update your knowledge and skills in the areas of data science and software engineering, while obtaining a formal Master's qualification.

This programme has been funded by the Higher Education Funding Council for England (HEFCE), as part of an innovative initiative to fund conversion courses in computing and engineering. This course uniquely enables students without any previous computer or data science experience at undergraduate level to study towards a Master's degree in this area of emerging importance. Crucially, the course covers both data science and software engineering, a combination of skills sought after in industry.

Why study this course at Birkbeck?

This programme is ideal if you are new to computer science and want to develop a career in IT as a data scientist or analyst.
Our Department of Computer Science and Information Systems is one of the longest-established in the world - we are celebrating our 60th anniversary in 2017.
We provide a stimulating teaching and research environment, with academic specialists in all fields, including information and knowledge management, web and pervasive technologies, computational intelligence, and information systems development, among others.
Our research dates back to the late 1940s, when one of the first electronic computers was developed at Birkbeck by Dr Andrew Booth. We now house the Computational Intelligence Research Group and the Information Management and Web Technologies Research Group, both of which collaborate with other research groups and with industry, in the UK and abroad, and undertake interdisciplinary research in the life, natural and social sciences, and the humanities.
We are also part of the London Knowledge Lab, a unique collaboration between Birkbeck and the UCL Institute of Education, which brings together computer and social scientists to explore how we learn, the role of technology in this process, and how technology relates to broader social, economic and cultural factors.
In the 2014 Research Excellence Framework (REF), more than 75% of our research outputs in Computer Science were ranked world-leading or internationally excellent.
You will have 24-hour access to several laboratories of networked PCs with a range of language compilers, database and other application software. We are connected, via the SuperJANET network, to the computers of other academic institutions in London, elsewhere in the UK and abroad.

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The Institute for Adaptive and Neural Computation (IANC) is a world-leading institute dedicated to the theoretical and empirical study of adaptive processes in both artificial and biological systems. Read more

Research profile

The Institute for Adaptive and Neural Computation (IANC) is a world-leading institute dedicated to the theoretical and empirical study of adaptive processes in both artificial and biological systems. We are one of the UK’s largest and most prestigious academic teams in these fields.

We foster world-class interdisciplinary and collaborative research bringing together a range of disciplines.

Our research falls into three areas:

-machine learning
-computational neuroscience
-computational biology

In machine learning we develop probabilistic methods that find patterns and structure in data, and apply them to scientific and technological problems. Applications include areas as diverse as astronomy, health sciences and computing.

In computational neuroscience and neuroinformatics we study how the brain processes information, and analyse and interpret data from neuroscientific experiments

The focus in the computational biology area is to develop computational strategies to store, analyse and model a variety of biological data (from protein measurements to insect behavioural data).

Training and support

You carry out your research within a research group under the guidance of a supervisor. You will be expected to attend seminars and meetings of relevant research groups and may also attend lectures that are relevant to your research topic. Periodic reviews of your progress will be conducted to assist with research planning.

A programme of transferable skills courses facilitates broader professional development in a wide range of topics, from writing and presentation skills to entrepreneurship and career strategies.

The School of Informatics holds a Silver Athena SWAN award, in recognition of our commitment to advance the representation of women in science, mathematics, engineering and technology. The School is deploying a range of strategies to help female staff and students of all stages in their careers and we seek regular feedback from our research community on our performance.

Facilities

The award-winning Informatics Forum is an international research facility for computing and related areas. It houses more than 400 research staff and students, providing office, meeting and social spaces.

It also contains two robotics labs, an instrumented multimedia room, eye-tracking and motion capture systems, and a full recording studio amongst other research facilities. Its spectacular atrium plays host to many events, from industry showcases and student hackathons to major research conferences.

Nearby teaching facilities include computer and teaching labs with more than 250 machines, 24-hour access to IT facilities for students, and comprehensive support provided by dedicated computing staff.

Among our entrepreneurial initiatives is Informatics Ventures, set up in 2008 to support globally ambitious software companies in Scotland and nurture a technology cluster to rival Boston, Pittsburgh, Kyoto and Silicon Valley.

Career opportunities

The research you will undertake at IANC is perfectly suited to a career in academia, where you’ll be able to use your knowledge to advance this important field. Some graduates take their skills into commercial research posts, and find success in creating systems that can be used in everyday applications.

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Data science is the study of the computational principles, methods, and systems for extracting and structuring knowledge from data; and the application and use of those principles. Read more

Programme description

Data science is the study of the computational principles, methods, and systems for extracting and structuring knowledge from data; and the application and use of those principles. Large data sets are now generated by almost every activity in science, society, and commerce - ranging from molecular biology to social media, from sustainable energy to health care.

Data science asks: how can we efficiently find patterns in these vast streams of data? Many research areas have tackled parts of this problem: machine learning focuses on finding patterns and making predictions from data; ideas from algorithms and databases are required to build systems that scale to big data streams; and separate research areas have grown around different types of unstructured data such as text, images, sensor data, video, and speech. Recently, these distinct disciplines have begun to converge into a single field called data science.

Programme structure

You follow two taught semesters of lectures, tutorials, project work and written assignments, after which you will learn research methods before individual supervision for your project and dissertation.

Compulsory courses

Informatics Research Review
Informatics Research Proposal
Dissertation

You are also required to take a breadth of courses in data science, with at least one in each of the following areas:

Machine Learning, Statistics and Optimization
Databases and Data Management
Applications

You can take up to two courses from other schools.

Learning outcomes

The School of Informatics' MSc in Data Science is designed to attract students who want to establish a career as a data scientist in industry or the public sector, as well as students who want to explore the area prior to further training such as in our CDT in Data Science.

The learning objectives of the degree are to foster:

A breadth of knowledge across the data science areas
An advanced technical background in at least one of the data science areas
An appreciation for real-world problems involving the use of data in industry, science, and the public sector
Research experience in one of the data science areas.

Career opportunities

Through this programme you will develop specialist, advanced skills in the development, construction and management of advanced computer systems.

You will gain practical experience and a thorough theoretical understanding of the field making you attractive to a wide range of employers or preparing you for further academic study.

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Our Computer Science MPhil and PhD programme gives you an opportunity to make a unique contribution to computer science research. Read more
Our Computer Science MPhil and PhD programme gives you an opportunity to make a unique contribution to computer science research. Your research will be supported by an experienced computer scientist within a research group and with the support of a team of advisers.

Research supervision is available under our six research areas, reflecting our strengths, capabilities and critical mass.

Advanced Model-Based Engineering and Reasoning (AMBER)

The AMBER group aims to equip systems and software engineering practitioners with effective methods and tools for developing the most demanding computer systems. We do this by means of models with well-founded semantics. Such model-based engineering can help to detect optimal, or defective, designs long before commitment is made to implementations on real hardware.

Digital Interaction Group (DIG)

The Digital Interaction Group (DIG) is the leading academic research centre for human-computer interaction (HCI) and ubiquitous computing (Ubicomp) research outside of the USA. The group conducts research across a wide range of fundamental topics in HCI and Ubicomp, including:
-Interaction design methods, eg experience-centred and participatory design methods
-Interaction techniques and technologies
-Mobile and social computing
-Wearable computing
-Media computing
-Context-aware interaction
-Computational behaviour analysis

Applied research is conducted in partnership with the DIG’s many collaborators in domains including technology-enhanced learning, digital health, creative industries and sustainability. The group also hosts Newcastle University's cross-disciplinary EPSRC Centre for Doctoral Training in Digital Civics, which focusses on the use of digital technologies for innovation and delivery of community driven services. Each year the Centre awards 11 fully-funded four-year doctoral training studentships to Home/EU students.

Interdisciplinary Computing and Complex BioSystems (ICOS)

ICOS carries out research at the interface of computing science and complex biological systems. We seek to create the next generation of algorithms that provide innovative solutions to problems arising in natural or synthetic systems. We do this by leveraging our interdisciplinary expertise in machine intelligence, complex systems and computational biology and pursue collaborative activities with relevant stakeholders.

Scalable Computing

The Scalable Systems Group creates the enabling technology we need to deliver tomorrow's large-scale services. This includes work on:
-Scalable cloud computing
-Big data analytics
-Distributed algorithms
-Stochastic modelling
-Performance analysis
-Data provenance
-Concurrency
-Real-time simulation
-Video game technologies
-Green computing

Secure and Resilient Systems

The Secure and Resilient Systems group investigates fundamental concepts, development techniques, models, architectures and mechanisms that directly contribute to creating dependable and secure information systems, networks and infrastructures. We aim to target real-world challenges to the dependability and security of the next generation information systems, cyber-physical systems and critical infrastructures.

Teaching Innovation Group

The Teaching Innovation Group focusses on encouraging, fostering and pursuing innovation in teaching computing science. Through this group, your research will focus on pedagogy and you will apply your research to maximising the impact of innovative teaching practices, programmes and curricula in the School. Examples of innovation work within the group include:
-Teacher training and the national Computing at School initiative
-Outreach activities including visits to schools and hosting visits by schools
-Participation in national fora for teaching innovation
-Market research for new degree programmes
-Review of existing degree programmes
-Developing employability skills
-Maintaining links with industry
-Establishing teaching requirements for the move to Science Central

Research Excellence

Our research excellence in the School of Computing Science has been widely recognised through awards of large research grants. Recent examples include:
-Engineering and Physical Sciences Research Council (EPSRC), Centre for Doctoral Training in Cloud Computing for Big Data Doctoral Training Centre
-Engineering and Physical Sciences Research Council (EPSRC), Centre for Doctoral Training in Digital Civics
-Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC) Research Grant: a £10m project to look at novel treatment for epilepsy, confirming our track record in Systems Neuroscience and Neuroinformatics.

Accreditation

The School of Computing Science at Newcastle University is an accredited and a recognised Partner in the Network of Teaching Excellence in Computer Science.

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Computer Science is one of the drivers of technological progress in all economic and social spheres. Those graduating with an M.Sc. Read more

About Computer Science

Computer Science is one of the drivers of technological progress in all economic and social spheres. Those graduating with an M.Sc. in Computer Science are specialists in at least one field of computer science who have wide-ranging science-based methodological expertise.
Graduates are able to define, autonomously and comprehensively, computer science problems and their applications, structure them and build abstract models. Moreover, they are able to define and implement solutions that are at the state of the art of technology and science.

Features

– A broad, international and relevant selection of courses
– As a student, you will work on cutting-edge research projects
– Individual guidance in small learning groups
– Excellent enterprise relations maintained by the chairs and institutes
– Numerous partnerships with universities throughout the world, including a double degree programme with the Institut national des sciences appliquées de Lyon (INSA)

Syllabus

The programme offers the following five focus modules:
1) Algorithms and Mathematical Modelling
2) Programming and Software Systems
3) Information and Communication Systems
4) Intelligent Technical Systems
5) IT Security and Reliability
1) Algorithms and Mathematical Modelling: This module teaches you about determinstic and stochastic algorithms, their implementation, evaluation and optimisation. You will acquire advanced knowledge of computer-based mathematical methods – particularly in the areas of algorithmic algebra and computational stochastics – as well as developing an in-depth expertise in mathematical modelling and complexity analysis of discrete and continuous problems.
2) Programming and Software Systems: This module imparts modern methods for constructing large-scale software systems, as well as creating and using software authoring, analysis and optimisation tools. In this module you will consolidate your knowledge of the various programming paradigms and languages, the structure of language processing systems, and learn to deal with parallelism in program procedures.
3) Information and Communication Systems: In this module you will study the interactions of the classic computer science areas of information systems and computer networks. This focus area represents an answer to the problem of increasing volume and complexity of worldwide information distribution and networks, and for the growing requirements on quality and performance of computer communication. Additionally, you will learn to transfer database results to multimedia data.
4) Intelligent Technical Systems: In this module you are acquainted with digital image and signal processing, embedded systems and applications of intelligent technical systems in industrial and assistance systems, which are necessary for production automation and process control, traffic control, medical and building technology. You will learn to develop complex applications using computer systems and deal with topics such as image reconstruction, camera calibration, sensor data fusion and optical measurement technology.
5) IT Security and Reliability: This module group is concerned with security and reliability of IT systems, e.g. in hardware circuitry and communication protocols, as well as complex, networked application systems. To ensure the secure operation of these systems you will learn design methodology, secure architectures and technical implementation of the underlying components.

Language requirements

Unless English is your native language or the language of your secondary or undergraduate education, you should provide an English language certificate at level B2 CEFR, e.g. TOEFL with a minimum score of 567 PBT, 87 iBT or ITP 543 (silver); IELTS starting from 5.5; or an equivalent language certificate.

To facilitate daily life in Germany, it would be beneficial for you to have German language skills at level A1 CEFR (beginner’s level). If you do not have any German skills when starting out on the programme, you will complete a compulsory beginner’s German course during your first year of study.

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Computer science drives the fundamental technologies of today’s connected world. Read more

Course Summary

Computer science drives the fundamental technologies of today’s connected world. Suited to candidates with significant programming experience, this umbrella programme covers the foundations of a range of specialisms as well as providing the opportunity to deepen your understanding of one or more of these areas through a range of optional modules.

Modules

Semester one: Computer Networks; Computer Vision; Designing Usable and Accessible Technologies; Evolution of Complexity; Foundations of Artificial Intelligence; Foundations of Cyber Security; Foundations of Data Science; Foundations of Web Science; Implementing Cyber Security; Intelligent Agents; Machine Learning; Robotic Systems; Software Engineering and Cyber Security; Software Modelling Tools and Techniques for Critical Systems; Software Project Management and Development; Topics in Computer Science; Web Development

Semester two: Advanced Computer Networks; Advanced Computer Vision; Advanced Databases; Advanced Intelligent Agents; Advanced Machine Learning; Automated Code Generation; Automated Software Verification; Biological Inspired Robotics; Biometrics; Computational Biology; Computational Finance; Cryptography; Data Mining; Data Visualisation; E-Business Strategy; Further Web Science; Game Design and Development; Image Processing; Open Data Innovation; Secure Systems; Semantic Web Technologies; Simulation Modelling for Computer Science; The Science of Online Social Networks

Plus three month independent research project culminating in a dissertation

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The MSc Web Science explores the impact of the Web on the digital economy and all aspects of human society, from the individual right through to national and global scales. Read more

Course Summary

The MSc Web Science explores the impact of the Web on the digital economy and all aspects of human society, from the individual right through to national and global scales. Pioneered by ECS in the UK and MIT in the US, Web Science analyses the Web at a systems level; on the one hand investigating the technical capabilities of its distributed information infrastructure whilst also scrutinising the public policy and social practices that have made it a transformative global phenomenon. This course develops a multidisciplinary understanding of the Web in society and is open to graduates of Computer Science, IT, Social Sciences and the Humanities.

Modules

Semester one: Foundations of Web Science—Impact of Web on Society; Web Architecture (Web 1.0); Interdisciplinary Thinking; Research Methods

Semester two: Further Web Science—Innovating and Evaluating Policy; Social Networks (Web 2.0); Semantic Web (Web 3.0); Computational Thinking

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