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Masters Degrees (Applied Computer Science)

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Do you already have an academic bachelor’s degree in another scientific field and do you want to pursue your academic career in the field of Computer Science?… Read more
Do you already have an academic bachelor’s degree in another scientific field and do you want to pursue your academic career in the field of Computer Science? Then the master’s in Applied Computer Sciences is the programme you’re looking for! It is organised in such a way to accommodate your scientific background and future-oriented academic interests – developing the necessary Computer Science skills by complementing your primary field of expertise. Above all that, we offer a wide variety of highly specialised elective courses.

Approach

The first year of the programme focuses on developing knowledge of computer science concepts and techniques, with respect to the earlier studies. Lectures address both hardware and software. Elective courses in the second year allow applying the skills established in the first year, in a specialization, such as software development, telecommunications, multimedia, numeric engineering, bioinformatics, or robotics, as well as many other possibilities. Practical sessions and a master's thesis are also built into the study program.

 All areas of computer science are covered
The programme offers lectures in all traditional areas of the computer science and also in more specialized fields like software engineering, embedded systems, web design, telecommunications, multimedia, bioinformatics, robotics and many other subjects.

 Adaptable to your background and field of interest
Since Computer Science has become a diffuse area, we decided to organize this programme in a way that it can be adapted to the background and the field of interest of the student. Students start with a number of courses (depending on their background) summarizing the basic concepts in order to gain enough knowledge to be able to take elective courses and to make a master thesis in their field of interest. All this happens in an academic environment where research is done in all topics with great interaction among the researchers and large involvement of the students.

Joint organisation of two departments provides wide range of research topics
Two departments, the Department of Computer Science in the Faculty of Science and the Department of Electronics and Informatics in the Faculty of Engineering, jointly organise the Master programmes. Together, they have more than 200 researchers who cover a wide range of research topics.

Learning outcomes

During the two master years students are able to continue to build on the broad ranging basic scientific knowledge acquired as part of their Bachelor programme, complemented with the Information Technology profile, combining a multidisciplinary engineering training with an in-depth specialisation in Applied Computer Science.

The Master of Science in Applied Sciences and Engineering : Applied Computer Science programme is designed to train young people who are capable of making an effective contribution to the conception, realisation and coaching of projects of scientific and/or technological scope for the benefit of the fast-changing world we live in.

Curriculum

Available on http://www.vub.ac.be/en/study/applied-sciences-and-engineering-applied-computer-science/programme

Admission requirements

Applicants should have at least a bachelor degree in one of the following areas:
- Engineering
- Mathematics
- Geography/Geology
- Biology/ Biochemistry/ Biotechnology/ Chemsitry
- Economics
- Physics
Students holding a Bachelor’s or Master's degree in another field of the exact sciences or engineering can also apply.

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This new Master’s in Applied Computer Science and Society program is unique in North America. The program will foster critical reflection on how technology and society interact. Read more
This new Master’s in Applied Computer Science and Society program is unique in North America. The program will foster critical reflection on how technology and society interact. It will offer a pragmatic and applied curriculum, addressing globalization and cultural issues alongside technical instruction. The new program will fulfill the largest growing occupational needs in government, industry and non-profit sectors. An internationally educated faculty engaged in innovative computing research will offer the program.

This graduate program focuses on issues of technology and human/social aspects of computing in four core areas:
-Information representation
-Search and management
-Intelligent systems
-Systems development and social issues

This program builds upon existing strengths in the department and will facilitate research and development in the area of information and computing technologies (ICT) with Manitoba's 300 ICT companies.

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

The University of Helsinki will introduce annual tuition fees to foreign-language Master’s programmes starting on August 1, 2017 or later. The fee ranges from 13 000-18 000 euros. Citizens of non-EU/EEA countries, who do not have a permanent residence status in the area, are liable to these fees. You can check this FAQ at the Studyinfo website whether or not you are required to pay tuition fees: https://studyinfo.fi/wp2/en/higher-education/higher-education-institutions-will-introduce-tuition-fees-in-autumn-2017/am-i-required-to-pay-tuition-fees/

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 minor 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.

Minor studies give you a wider perspective of Data Science. Your minor subject 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).

Selection of the Major

You can specialise either in the core areas of data science -- algorithms, infrastructure and statistics -- or in its applications. This means that you can focus on the development of new models and methods in data science, supported by the data science research carried out at the University of Helsinki; or you can become a data science specialist in an application field by incorporating studies in another subject. In addition to mainstream data science topics, the programme offers two largely unique opportunities for specialisation: the data science computing environment and infrastructure, and data science in natural sciences, especially physics.

Programme Structure

You should be able to complete the MSc Programme in Data Science of 120 credits (ECTS) in two years of full-time study. The programme consists of:
-Common core studies of basic data science courses.
-Several modules on specific topics within data science algorithms, data science infrastructures and statistical data science, and on data science tools.
-Seminars and colloquia.
-Courses on academic skills and tools.
-Possibly an internship in a research group or company.
-Studies in an application domain.
-Master’s thesis (30 credits).

Career Prospects

Industry and science are flooded with data and are struggling to make sense of it. There is urgent demand for individuals trained to analyse data, including massive and heterogeneous data. For this reason, the opportunities are expected to grow dramatically. The interdisciplinary Data Science MSc programme will train you to work in data-intensive areas of industry and science, with the skills and knowledge needed to construct solutions to complex data analysis problems.

If you are focusing on the core areas of data science, you will typically find employment as a researcher or consultant, sometimes after taking a PhD in Computer Science or Statistics to deepen your knowledge of the field and research methods. If your focus is on the use of data science for specific applications, you will typically find work in industry or in other fields of science such as physics, digital humanities, biology or medicine.

Internationalization

The Data Science MSc is an international programme, with students from around the world and an international research environment. All of the departments taking part in the programme are internationally recognised for their research and a significant fraction of the teaching and research staff come from abroad.

The departments participate in international student exchange programmes and offer you the chance to include international experience as part of your degree. Data Science itself is an international field, so once you graduate you can apply for jobs in any country.

In the programme, all courses are in English. Although the Helsinki area is quite cosmopolitan and English is widely spoken, you can also take courses to learn Finnish at the University of Helsinki Language Centre. The Language Centre also offers an extensive programme of foreign language courses for those interested in learning other languages.

Research Focus

The MSc programme in Data Science is offered jointly by three departments and two research institutes. Their research covers a wide spectrum of the many aspects of data science. At a very general level, the focal areas are:
-Machine learning and data mining
-Distributed computation and computational infrastructures
-Statistical modelling and analysis
-Studies in the programme are tightly connected to research carried out in the participating departments and institutes.

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The Department of Computer Science at Binghamton University aims to provide all graduates with a strong foundation in computer science while also offering the opportunity to pursue specific interests within computer science and/or interests in other disciplines. Read more
The Department of Computer Science at Binghamton University aims to provide all graduates with a strong foundation in computer science while also offering the opportunity to pursue specific interests within computer science and/or interests in other disciplines. The program provides students with an understanding of the theory and practice of automating the representation, storage and processing of information, while emphasizing experimental research to design and engineer a wide variety of computer and information systems.

The Master of Science in Computer Science (MSCS) is intended for students with a strong background in computer science and a desire to prepare for research studies or professional practice. If you have bachelor's degree in computer science or a related field, you're invited to apply for admission to our MSCS program.

The doctoral program leads to a PhD in Computer Science. Students admitted into the program typically have a master's degree in computer science or a closely related discipline. Students with a bachelor's degree and a strong academic record may also be directly admitted.

Recent doctoral graduates have gone on to careers in as software engineering at Intel, eBay, Cisco Systems, positions at Hewlett Packard, Microsoft, Twitter, Bloomberg, the Air Force Research Lab, and the U.S. Census. Academic placements include assistant professorships at California State University at Fullerton, Valdosta State University, and Harran University, Turkey.

The Master's program leads to a Master of Science in Computer Science. It is intended for students with a strong background in computer science and a desire to prepare for research studies or professional practice. Holders of the baccalaureate degree in computer science or a related field are invited to apply for admission to the MSCS program. Students whose undergraduate degree is not in computer science may be required to complete some preparatory work in addition to fulfilling the requirements listed below.
Program requirements include four core courses taken over the first two semesters of study. These courses are Computer Organization and Architecture, Operating Systems, Programming Languages and Design & Analysis of Computer Algorithms. Three graduating options are offered: a thesis option, a project option and a comprehensive exam. Beyond the 4 core courses, these options require students to complete 4, 5 and 6 elective courses, respectively, chosen from a broad set of courses offered by the Department.

Applicant Qualifications

- Undergraduate major in computer science or related field desirable for admission
- Applicants are additionally expected to have completed coursework in the following areas:
*Algorithms and data structures
*Computer organization and architecture
*Operating systems
*Programming languages
*Discrete mathematics

All applicants must submit the following:

- Online graduate degree application and application fee
- Transcripts from each college/university you have attended
- Two letters of recommendation (three letters of recommendation for PhD applicants)
- Personal statement (2-3 pages) describing your reasons for pursuing graduate study, your career aspirations, your special interests within your field, and any unusual features of your background that might need explanation or be of interest to your program's admissions committee.
- Resume or Curriculum Vitae (max. 2 pages)
- Official GRE scores

And, for international applicants:
- International Student Financial Statement form
- Official bank statement/proof of support
- Official TOEFL, IELTS, or PTE Academic scores

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One of a range of degrees from the taught Masters Programme at the School of Computer Science our course is especially designed for graduates of numerate subjects other than computer science. Read more
One of a range of degrees from the taught Masters Programme at the School of Computer Science our course is especially designed for graduates of numerate subjects other than computer science. It is mostly taught separately from the other courses. It intensively covers a broad range of the key principles and techniques of computer science.

About the course

There is an emphasis on software development, in particular when applied to solving problems in other disciplines. Depending on the modules chosen, it can lead to a career in areas such as systems development, IT management, or the deployment of advanced applications in specific disciplines.

Why choose this course?

-This MSc is available with an optional one year industry placement. The 'with placement' programmes give you additional industrial experience by applying the skills you have learned throughout your studies
-A flexible course, part of our postgraduate master's programme in Computer Science, with two different routes
-Our course is especially designed for graduates of numerate subjects other than computer science
-Taught by a highly-regarded and long-established computer science department with strong links to business
-Half the research outputs in Computer Science at the University of Hertfordshire have been rated as world-leading or internationally excellent in the Research Excellence Framework (REF) 2014

Careers

Our masters programme is designed to give Computer Science graduates the specialist, up-to-date skills and knowledge sought after by employers, whether in business, industry, government or research. This particular course will prepare you for a career such as a software engineer, developer or project manager.

Teaching methods

Classes consist of lectures, small group seminars, and practical work in our well-equipped laboratories. We use modern, industry-standard software wherever possible. There are specialist facilities for networking and multimedia and a project laboratory especially for masters students. In addition to scheduled classes, you will be expected a significant amount of time in self-study, taking advantage of the extensive and up-to-date facilities. These include the Learning Resource Centres, open 24x7, with 1,500 computer workstations and wifi access, Studynet our versatile online study environment usable on and off campus, and open access to our labs.

Work Placement

This MSc is available with an optional one year industry placement. The 'with placement' programmes give you additional industrial experience by applying the skills you have learned throughout your studies.

This offers you the opportunity to work for one year in a highly professional and stimulating environment. You will be a full time employee in a company earning a salary and will learn new skills that can't be taught at University. During the placement, you will be able to gain further insight into industrial practice that you can take forward into your individual project.

We will provide excellent academic and personal support during both your academic and placement periods together with comprehensive careers guidance from our very experienced dedicated Careers and Placements Service.

Although the responsibility for finding a placement is with you, our Careers and Placements Service maintains a wide variety of employers who offer placement opportunities and organise special training sessions to help you secure a placement, from job application to the interview. Optional one-to-one consultations are also available.

In order to qualify for the placement period you must maintain an overall average pass mark of not less than 60% across all modules studied in semester ‘A’.

Structure

Year 1
Core Modules
-Computer Architectures
-Computer Science Masters Project
-Operating Systems and Networks
-Preparation for Placement
-Professional Issues
-Professional Work Placement for MSc Computer Science
-Programming and Program Design
-Software Development Exercise
-Systems Modelling

Year 2
Core Modules
-Computer Science Masters Project

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Data Science brings together computational and statistical skills for data-driven problem solving. Read more
Data Science brings together computational and statistical skills for data-driven problem solving. This rapidly expanding area includes machine learning, deep learning, large-scale data analysis and has applications in e-commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence.

Degree information

The programme comprises core machine learning methodology and an introduction to statistical science, combined with a set of more specialised and advanced options covering computing and statistical modelling. Projects are offered both within UCL Computer Science and from a wide range of industry partners.

Students undertake modules to the value of 180 credits.

The programme consists of three compulsory modules (45 credits), five optional modules (75credits) and a dissertation/report (60 credits).

Core modules
-Applied Machine Learning
-Introduction to Supervised Learning
-Introduction to Statistical Data Science

Optional modules - students choose a minimum of 30 credits and a maximum of 60 credits from the following optional modules:
-Cloud Computing (Birkbeck)
-Machine Vision
-Information Retrieval & Data Mining
-Statistical Natural Language Processing
-Web Economics

Students choose a minimum of 0 credits and a maximum of 30 credits from these optional Statistics modules:
-Statistical Design of Investigations
-Applied Bayesian Methods
-Decision & Risk

Students choose a minimum of 15 credits and a maximum of 15 credits from these elective modules:
-Supervised Learning
-Graphical Models
-Bioinformatics
-Affective Computing and Human-Robot Interaction
-Computational Modelling for Biomedical Imaging
-Stochastic Systems
-Forecasting

Dissertation/report
All students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning
The programme is delivered though a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed through a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.

Careers

Data science professionals are increasingly sought after as the integration of statistical and computational analytical tools becomes more essential to organisations. A thorough understanding of the fundamentals required from the best practitioners, and this programme's broad base, assists data scientists to adapt to rapidly evolving goals. This is a new degree and information on graduate destinations is not currently available. However, MSc graduates from across the department frequently find roles with major tech and finance companies including:
-Google Deepmind
-Microsoft Research
-Dunnhumby
-Index Ventures
-Last.fm
-Cisco
-Deutsche Bank
-IBM
-Morgan Stanley

Why study this degree at UCL?

The 2014 Research Excellence Framework ranked UCL first in the UK for computer science. 61% of its research work is rated as world-leading and 96% as internationally excellent.

UCL Computer Science staff have research interests ranging from foundational machine learning and large-scale data analysis to commercial aspect of business intelligence. Our extensive links to companies provide students with opportunities to carry out the research project with an industry partner.

The department also enjoys strong collaborative relationships across UCL; and exposure to interdisciplinary research spanning UCL Computer Science and UCl Statistical Science will provide students with a broad perspective of the field. UCL is home to regular machine learning masterclasses and big data seminars.

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The Master of Computer Science is a two-year (120 ECTS) advanced study in computer science organized by the Vrije Universiteit Brussel, a Flemish university located in Brussels, Belgium. Read more
The Master of Computer Science is a two-year (120 ECTS) advanced study in computer science organized by the Vrije Universiteit Brussel, a Flemish university located in Brussels, Belgium. This English-speaking programme is designed for students with a solid, basic academic background in computer science (Bachelor in Computer Science). The objectives of the programme are to provide a deeper understanding and knowledge of various specializations of computer science and to prepare the student for an active role in computer science research and development. The following specializations are offered: Artificial Intelligence, Multimedia, Software Engineering and Web & Information Systems.

Course outline

All students study a core programme of 30 credits; do a research training of 6 credits and a master thesis of 24 credits. Students should choose one out of four specializations: Artificial Intelligence, Multimedia, Software Engineering, or Web & Information Systems. 30 credits should be spent to mandatory and optional courses within the chosen specialization. The 24 remaining optional courses can be taken within the specialization or from another specialization. Finally, 6 credits can be chosen completely freely form any of the VUB courses. This brings the total to 120 ECTS credits. The master thesis as well as the research training needs to be related to the chosen specialization.

Specializations offered

 Artificial Intelligence
The focus in this specialization is on building intelligent software artifacts. The theories of complex dynamic systems and self-organization are emphasized starting from the theory of complex dynamic systems as developed in related fields such as mathematics, physics, and biology. Students will be exposed to current research in the areas of adaptive systems, multi-agent systems, and the origins of language.

 Multimedia
Students in this specialization will conduct in-depth exploration of techniques for signal processing and communication of multimedia content. The program is designed to build thorough technological and scientific knowledge of various multimedia domains such as digital television, telephony and video phony, computer animation, computer games, and the Internet. Students will gain experience with complex ICT architectures for the processing, distribution, and consumption of multimedia content.

 Software Engineering
In this specialization, students gain the skills needed to build complex software applications and software intensive systems. Students are also taught scientifically sound methods, as well as the newest techniques and tools for the developing of software. The curriculum also includes research topics in programming languages and integrated development environments.

 Web & Information Systems
This specialization is geared towards information system development and application development in the context of the Web. Students will learn about data and semantic representation techniques and acquire thorough technological and scientific knowledge related to the newest technological developments for the Web. Students can participate is research in the area of ontologies, new media systems (multimedia, Virtual Reality, games, social systems), and the "Internet of Things".

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This MSc programme takes two years of full-time study, covering a wide spectrum of fields in Computer Science and Information Technology. Read more
This MSc programme takes two years of full-time study, covering a wide spectrum of fields in Computer Science and Information Technology. It is suitable for students with diverse academic backgrounds, such as computer science, engineering, statistics, mathematics and related disciplines.
The programme has been awarded with the GRIN 2015 Quality Label.
GRIN is an Italian association that aims at promoting research and education in Computer Science.

The programme

The programme unfolds into three semesters of full-time lectures and lab experience. During the last semester, students work on an individual project and dissertation, supervised by a department member. The programme is organized around two curricula, which include both compulsory and elective courses, from which students have to build their study plan for qualification. The two curricula, which include a first semester of common courses on advanced topics in computer science and mathematics, are the following:

Data Management and Analytics (DMA)
This curriculum is designed to train a new generation of professionals specialized on data. Specifically, the study program of this curriculum allows students to acquire skills and key competences such as machine learning and artificial intelligence, advanced databases and information retrieval, statistics, data mining and visualization, cloud, distributed and parallel computing.

Software Dependability and Cyber Security (SDCS)
The curriculum aims at training specialists in software engineering with advanced skills in software correctness verification, in design of secure and privacy aware systems, and their performance evaluation. The study program for this profile allows students to acquire skills in system modelling, in evaluating and verifying software requirements in terms of correctness, scalability and performance, in secure programming and cyber security.

Applying to the programme

To enter the programme applicants need to have an equivalent of a three-year Italian undergraduate degree (Laurea) such as a BSc degree in Computer Science or related subject, with good background on fundamental topics in computer science and engineering, such as programming languages and software engineering, algorithms, computer architecture, operating systems, databases, and computer networks. Further requirements include basic knowledge of calculus, discrete mathematics, and probability and statistics, foundations of computer science.

When and how to apply

The classes start in September. Please note that it is best to apply as early as possible. Applications are made directly to the University of Venice. For full details visit How to apply, or contact the Head of Study ().

Graduate careers

Students graduating from the MSc in Computer Science may use their new computing skills to enhance their employment prospects in work related to their first degree. Graduates interested in foundational, experimental, and applied research, can join our PhD Programme in Computer Science.

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The computer science program is designed for students who have an undergraduate degree (or minor) in computer science, as well as those who have a strong background in a field in which computers are applied, such as engineering, science, or business. Read more

Program overview

The computer science program is designed for students who have an undergraduate degree (or minor) in computer science, as well as those who have a strong background in a field in which computers are applied, such as engineering, science, or business.

The degree is offered on a full- or part-time basis. Courses are generally offered in the afternoons and evenings to accommodate part-time students. Full-time students take three or four courses per semester and may be able to complete the course work in three semesters. Full-time students who are required to take additional bridge courses may be able to complete the course work in four semesters. Part-time students take one or two courses per semester and may be able to complete the course work in four to five semesters. The time required to complete a master's project is one semester, but can vary according to the student and the scope of the topic. Two semesters is typical.

Plan of study

The program consists of 30 credit hours of course work, which includes either a thesis or a project. Students complete one core course, three courses in a cluster, four electives, and a thesis. For those choosing to complete a project in place of a thesis, students complete one additional elective.

Clusters

Students select three cluster courses from the following areas (see website for individual area information):
-Computer graphics and visualization
-Data management
-Distributed systems
-Intelligent systems
-Languages and tools
-Security
-Theory

Electives

Electives provide breadth of experience in computer science and applications areas. Students who wish to include courses from departments outside of computer science need prior approval from the graduate program director. Refer to the course descriptions in the departments of computer science, engineering, mathematical sciences, and imaging science for possible elective courses.

Master's thesis/project

Students may choose the thesis or project option as the capstone to the program. Students who choose the project option must register for the Project course (CSCI-788). Students participate in required in-class presentations that are critiqued. A summary project report and public presentation of the student's project (in poster form) occurs at the end of the semester.

Curriculum

Thesis/project options differ in course sequence, see the website for a particular option's modules and a particular cluster's modules.

Other admission requirements

-Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
-Submit scores from the Graduate Record Exam.
-Have a minimum grade point average of 3.0 (B), and complete a graduate application.
-International applicants, whose native language is not English, must submit scores from the Test of English as a Foreign Language. A minimum score of 570 (paper-based) or 88 (Internet-based) is required.
-Applicants must satisfy prerequisite requirements in mathematics (differential and integral calculus, probability and statistics, discrete mathematics, and computer science theory) and computing (experience with a modern high-level language [e.g., C++, Java], data structures, software design methodology, introductory computer architecture, operating systems, and programming language concepts).

Additional information

Bridge courses:
If an applicant lacks any prerequisites, bridge courses may be recommended to provide students with the required knowledge and skills needed for the program. If any bridge courses are indicated in a student's plan of study, the student may be admitted to the program on the condition that they successfully complete the recommended bridge courses with a grade of B (3.0) or better (courses with lower grades must be repeated). Generally, formal acceptance into the program is deferred until the applicant has made significant progress in this additional course work. Bridge program courses are not counted as part of the 30 credit hours required for the master's degree. During orientation, bridge exams are conducted. These exams are the equivalent to the finals of the bridge courses. Bridge courses will be waived if the exams are passed.

Faculty:
Faculty members in the department are actively engaged in research in the areas of artificial intelligence, computer networking, pattern recognition, computer vision, graphics, visualization, data management, theory, and distributed computing systems. There are many opportunities for graduate students to participate in these activities toward thesis or project work and independent study.

Facilities:
The computer science department provides extensive facilities that represent current technology, including:
-A graduate lab with more than 15 Mac’s and a graduate library.
-Specialized labs in graphics, computer vision, pattern recognition, security, database, and robotics.
-Six general purpose computing labs with more than 100 workstations running Linux, Windows, and OS X; plus campus-wide wireless access.

Maximum time limit:
University policy requires that graduate programs be completed within seven years of the student's initial registration for courses in the program. Bridge courses are excluded.

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Data science is an emerging new area of science. With City’s MSc in Data Science you can develop the skills and knowledge to analyse data in many forms and communicate insights. Read more
Data science is an emerging new area of science. With City’s MSc in Data Science you can develop the skills and knowledge to analyse data in many forms and communicate insights.

Who is it for?

This programme is for students who have a numerate first degree or can demonstrate numerate skills. Students are often at the early stages of their careers in diverse professions including economics, statistics and computer science.

Students will have a curiosity about data, and will want to learn new techniques to boost their career and be part of exciting current industry developments. The MSc in Data Science includes some complex programming tasks because of the applied nature of the course, so many students have a mathematics or statistics background and enjoy working with algorithms.

Objectives

The demand for data scientists in the UK has grown more than ten-fold in the past five years *. The amount of data in the world is growing exponentially. From analysing tyre performance to detecting problem gamblers, wherever data exists, there are opportunities to apply it.

City’s MSc Data Science programme covers the intersection of computer science and statistics, machine learning and practical applications. We explore areas such as visualisation because we believe that data science is about generating insight into data as well as its communication in practice.

The programme focuses on machine learning as the most exciting technology for data and we have learned from our own graduates that this is of high value when it comes to employment within the field. At City, we have excellent expertise in machine learning and the facilities students need to learn the technical aspects of data analysis. We also have a world-leading centre for data visualisation, where students get exposed to the latest developments on presenting and communicating their results – a highly sought after skill.

Placements

There is the opportunity to do an internship as part of the programme. The final project, which is normally three months for a full-time student, can be extended to six months if you want to study within a specific organisation. When it comes to the big data and data science area, we have established relationships with organisations including the BBC, Microsoft and The British Library so you can be confident that with City, your access to professional experience is unparalleled. One recent student undertook an internship with Google and has since secured a job within the company.

Academic facilities

The School's computer science laboratories are equipped with the latest up-to-date hardware and software. From Oracle’s leading commercial object-relational database server to PCs with state-of-the-art NVidia GPUs for computer graphics, you will have access to an array of tools to support your learning.

The MSc Data Science programme offers two (three by mid 2016) dedicated computer servers for the Big Data module, which you can also use for your final project to analyse large data sets. We give you the opportunity to undertake training in MATLAB, the most popular numerical and technical programming environment, while you study.

Scholarships

A scholarship for the full fees of the MSc will be offered to an outstanding applicant. The scholarship is available to UK/EU and overseas students, studying full-time. To be considered for the scholarship, please include with your full application a one-page essay with your answer to the question:

'What are the challenges that Data Science faces and how would you address those challenges?'

The submission deadline for anyone wishing to be considered for the scholarship is: 1 MAY 2017

Teaching and learning

The teaching and learning methods we use mean that students’ specialist knowledge and autonomy increase as they progress through each module. Active researchers guide your progress in the areas of machine learning, data visualization, and high-performance computing, which culminates with an individual project. This is an original piece of research conducted with academic supervision, but largely independently and, where appropriate, in collaboration with industrial partners.

Taught modules are delivered through a series of 20 hours of lectures and 10 hours of tutorials/laboratory sessions. Lectures are normally used to:
-Present and exemplify the concepts underpinning a particular subject.
-Highlight the most significant aspects of the syllabus.
-Indicate additional topics and resources for private study.

Tutorials help you develop the skills to apply the concepts we have covered in the lectures. We normally achieve this through practical problem solving contexts.

Laboratory sessions give you the opportunity to apply concepts and techniques using state-of-the-art software, environments and development tools.

In addition to lectures, laboratory sessions and tutorial support, you also have access to a personal tutor. This is an academic member of staff from whom you can gain learning support throughout your degree. In addition, City’s online learning environment Moodle contains resources for each of the modules from lecture notes and lab materials, to coursework feedback, model answers, and an interactive discussion forum.

We expect you to study independently and complete coursework for each module. This should amount to approximately 120 hours per module if you are studying full time. Each module is assessed through a combination of written examination and coursework, where you will need to answer theoretical and practical questions to demonstrate that you can analyse and apply data science methods and techniques.

The individual project is a substantial task. It is your opportunity to develop a research-related topic under the supervision of an academic member of staff. This is the moment when you can apply what you have learnt to solve a real-world problem using large datasets from industry, academia or government and use your knowledge of collecting and processing real data, designing and implementing big data methods and applying and evaluating data analysis, visualisation and prediction techniques. At the end of the project you submit a substantial MSc project report, which becomes the mode of assessment for this part of the programme.

Course content

Data science is the area of study concerned with the extraction of insight from large collections of data.

The course covers the study, integration and application of advanced methods and techniques from:
-Data analysis and machine learning
-Data visualisation and visual analytics
-High-performance, parallel and distributed computing
-Knowledge representation and reasoning
-Neural computation
-Signal processing
-Data management and information retrieval.

It gives you the opportunity to specialise so, once you graduate, you can apply data science to any sector from health to retail. By engaging with researchers and industrial partners during the programme, you can develop your knowledge and skills within a real-world context in each of the above areas.

Core modules
-Principles of data science (15 credits)
-Machine learning (15 credits)
-Big Data (15 credits)
-Neural computing (15 credits)
-Visual analytics (15 credits)
-Research methods and professional issues (15 credits)

Elective modules
-Advanced programming: concurrency (15 credits)
-Readings in computer science (15 credits)
-Advanced databases (15 credits)
-Information retrieval (15 credits)
-Data visualisation (15 credits)
-Digital signal processing and audio programming (15 credits)
-Cloud computing (15 credits)
-Computer vision (15 credits)
-Software agents (15 credits)

Individual project - (60 credits)

Career prospects

From health to retail, and from the IT industry to government, the Data Science MSc will prepare you for a successful career as a data scientist. You will graduate with specialist skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation, which are in high demand.

City's unique internships, our emphasis on machine learning and visual analytics, together with our links with the industry and Tech City, should help you gain employment as a specialist in data analysis and visualization. Graduates starting a new business can benefit from City's London City Incubator and City's links with Tech City, providing support for start-up businesses.

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The MSc in Computer Science (Applied) offers students with degrees that include three years of Computer Science a personalised programme of advanced CS modules to enhance their knowledge and fit their strengths. Read more

Overview

The MSc in Computer Science (Applied) offers students with degrees that include three years of Computer Science a personalised programme of advanced CS modules to enhance their knowledge and fit their strengths. Furthermore, students deepen their research and practical skills through a project and dissertation plus an industrial work placement meaning they will get the opportunity to apply the skills from the lecture hall and the research laboratory in a real industrial environment. This will be a key part of their training and enhance their employment prospects following their graduation.

Course Structure

The course lasts two years. In year one, students will do 12 taught modules selected to complement their previous experience and knowledge. In year 2, semester 1 consists of an advanced module in programming plus a choice of optional modules. In addition, student undertake a major individual software project and preparation for work placement. In semester 2, students are placed within software companies to gain at least six months of relevant work experience.

Graduates of the MSc in Computer Science (Applied) will be well placed to obtain positions in the software industry as programmers, designers and developers. In addition they will have an all-round qualification in computer science that makes them eligible for a wide range of computer-related positions.

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The Higher Diploma in Applied Computing Technology is a CONVERSION COURSE open to graduates from non-computing disciplines. Read more
The Higher Diploma in Applied Computing Technology is a CONVERSION COURSE open to graduates from non-computing disciplines. The course provides you with an understanding of the principles of internet-based computer systems and will equip you with a range of core IT skills, including web design, web server configuration, managing and manipulating multimedia content, interfacing with databases and working with common office software.

Visit the website: http://www.ucc.ie/en/cko08/

Course Details

The Higher Diploma is offered as a one year full-time or a two years part-time.

This is a CONVERSION COURSE and is intended for graduates who do not have Computer Science Degree and would like to attain skills in demand by the IT sector.

Format

A typical five credit module includes:
• two lecture hours
• one to two hours of practicals per week
• outside these regular hours, you are required to study independently

Full-Time Mode

Full-Time students take 60 credits as follows: 30 credits in teaching period 1 and 30 credits in period 2.

CS1117 Introduction to Programming (15 credits) - Dr. Jospeh Manning
CS5002 Web Development 1 (5 credits) - Dr. Frank Boehme
CS5007 Computer Applications with Visual Basic (5 credits) - Dr. James Doherty
CS5008 Internet Computing (5 credits) - Mr. Adrian O'Riordan
CS5009 Multimedia (5 credits) - Prof. James Bowen
CS5018 Web Development 2 (5 credits) - Dr. Derek Bridge
CS5019 Systems Organization I (5 credits) - Prof. John Morrison
CS5020 Systems Organization II (5 credits) - Prof. John Morrison
CS5021 Introduction to Relational Databases (5 credits) - Dr. Kieran Herley
CS5022 Database Design and Administration (5 credits) - Mr. Humprey Sorensen

Part-Time mode

Part-Time students take 30 credits in each of the two academic years as follows:

- Year 1 -

CS1117 Introduction to Programming (15 credits) - Dr. Joseph Manning
CS5002 Web Development 1 (5 credits) - Dr. Frank Boehme
CS5018 Web Development 2 (5 credits) - Dr. Derek Bridge
CS5021 Introduction to Relational Databases (5 credits) - Dr. Kieran Herley

- Year 2 -

CS5007 Computer Application with Visual Basic (5 credits) - Dr. James Doherty
CS5008 Internet Computing (5 credits) - Mr. Adrian O'Doherty
CS5009 Multimedia (5 credits) - Prof. James Bowen
CS5019 Systems Organization I (5 credits) - Prof. John Morrison
CS5020 Systems Organization II (5 credits) - Prof. John Morrison
CS5022 Database Design and Administration (5 credits) - Mr. Humphrey Sorensen

Further details on the content and modules are available on the Postgraduate College Calendar - http://www.ucc.ie/calendar/postgraduate/Diploma/Science/page14.html

Assessment

The Higher Diploma in Applied Computing Technology will be examined through a combination of end-of-year exams and module assignments.

Careers

Companies actively recruiting Computer Science graduates in 2014-15 include:

Accenture, Aer Lingus, Amazon, Apple, Bank of America Merrill Lynch, Bank of Ireland, BT, Cisco, CiTi-Technology, Cloudreach, Dell, Digital Turbine Asia Pacific, EMC, Enterprise Ireland, Ericsson, First Derivatives, Guidewire, IBM, Intel, Open Text, Paddy Power, Pilz, PWC, SAP Galway Transverse Technologies, Trend Micro, Uniwink, Version 1 (Software).

How to apply: http://www.ucc.ie/en/study/postgrad/how/

Funding and Scholarships

Information regarding funding and available scholarships can be found here: https://www.ucc.ie/en/cblgradschool/current/fundingandfinance/fundingscholarships/

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Color science is broadly interdisciplinary, encompassing physics, chemistry, physiology, statistics, computer science, and psychology. Read more

Program overview

Color science is broadly interdisciplinary, encompassing physics, chemistry, physiology, statistics, computer science, and psychology. The curriculum, leading to a master of science degree in color science, educates students using a broad interdisciplinary approach. This is the only graduate program in the country devoted to this discipline and it is designed for students whose undergraduate majors are in physics, chemistry, imaging science, computer science, electrical engineering, experimental psychology, physiology, or any discipline pertaining to the quantitative description of color. Graduates are in high demand and have accepted industrial positions in electronic imaging, color instrumentation, colorant formulation, and basic and applied research. Companies that have hired graduates include Apple Inc., Benjamin Moore, Canon Corp., Dolby Laboratories, Eastman Kodak Co., Hallmark, Hewlett Packard Corp., Microsoft Corp., Pantone, Qualcomm Inc., Ricoh Innovations Inc., Samsung, and Xerox Corp.

The color science degree provides graduate-level study in both theory and practical application. The program gives students a broad exposure to the field of color and affords them the unique opportunity of specializing in an area appropriate for their background and interest. This objective will be accomplished through the program’s core courses, selection of electives, and completion of a thesis or graduate project.The program revolves around the activities of the Munsell Color Science Laboratory within the College of Science. The Munsell Laboratory is the pre-eminent academic laboratory in the country devoted to color science. Research is currently under way in color appearance models, lighting, image-quality, color-tolerance psychophysics, spectral-based image capture, archiving, reproduction of artwork, color management, computer graphics; and material appearance. The Munsell Laboratory has many contacts that provide students with summer and full-time job opportunities across the United States and abroad.

Plan of study

Students must earn 30 semester credit hours as a graduate student to earn the master of science degree. For full-time students, the program requires three to four semesters of study. Part-time students generally require two to four years of study. The curriculum is a combination of required courses in color science, elective courses appropriate for the candidate’s background, and either a research thesis or graduate project. Students require approval of the program director if they wish to complete a graduate project, rather than a research thesis, at the conclusion of their degree.

Prerequisites: The foundation program

The color science program is designed for the candidate with an undergraduate degree in a scientific or other technical discipline. Candidates with adequate undergraduate work in related sciences start the program as matriculated graduate students. Candidates without adequate undergraduate work in related sciences must take foundation courses prior to matriculation into the graduate program. A written agreement between the candidate and the program coordinator will identify the required foundation courses. Foundation courses must be completed with an overall B average before a student can matriculate into the graduate program. A maximum of 9 graduate-level credit hours may be taken prior to matriculation into the graduate program. The foundation courses, representative of those often required, are as follows: one year of calculus, one year of college physics (with laboratory), one course in computer programming, one course in matrix algebra, one course in statistics, and one course in introductory psychology. Other science courses (with laboratory) might be substituted for physics.

Curriculum

Color science, MS degree, typical course sequence:
First Year
-Principles of Color Science
-Computational Vision Science
-Historical Research Perspectives
-Color Physics and Applications
-Modeling Visual Perception
-Research and Publication Methods
-Electives
Second Year
-Research
-Electives

Other admission requirements

-Submit scores from the Graduate Record Examination (GRE).
-Submit official transcripts (in English) for all previously completed undergraduate and graduate course work.
-Submit two professional recommendations.
-Complete an on-campus interview (when possible).
-Have an average GPA of 3.0 or higher.
-Have completed foundation course work with GPA of 3.0 or higher (if required), and complete a graduate application.
-International applicants who native language is not English must submit scores from the Test of English as a Foreign Language. Minimum scores of 94 (internet-based) are required. International English Language Testing System (IELTS) scores will be accepted in place of the TOEFL exam. Minimum scores will vary; however, the absolute minimum score required for unconditional acceptance is 7.0. For additional information about the IELTS, please visit http://www.ielts.org.

Additional information

Scholarships and assistantships:
Students seeking RIT-funded scholarships and assistantships should apply to the Color Science Ph.D. program (which is identical to the MS program in the first two years). Currently, assistantships are only available for qualified color science applicants to the Ph.D. program. Applicants seeking financial assistance from RIT must submit all application documents to the Office of Graduate Enrollment Services by January 15 for the next academic year.

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The Department of Computer Science offers a four-term graduate program leading to a Master of Science degree in Computer Science. Read more
The Department of Computer Science offers a four-term graduate program leading to a Master of Science degree in Computer Science. This program provides a high quality, leading-edge education on Computer Science that produces highly capable and sought after professionals able to excel in today’s ever changing technological world.

This program is very flexible, allowing the students the opportunity to select courses on their chosen area of specialization. Thesis and project based MSc students have the opportunity of conducting state of the art research with internationally renowned researchers. Course work based MSc students get a solid background in core and applied areas of Computer Science, giving them the edge they need to compete in any marketplace.

Visit the website: http://grad.uwo.ca/prospective_students/programs/program_NEW.cfm?p=37

Fields of Research

• Artificial Intelligence and Computer-Based Games
• Bioinformatics and Biocomputing
• Computer Algebra
• Distributed Systems
• Graphics, Image Processing, and Computer Vision
• Software Engineering and Human Computer Interaction
• Theoretical Computer Science

How to apply

For information on how to apply, please see: http://grad.uwo.ca/prospective_students/applying/index.html

Financing your studies

As one of Canada's leading research institutions, we place great importance on helping you finance your education. It is crucial that you devote your full energy to the successful completion of your studies, so we want to ensure that stable funding is available to you.
For information please see: http://grad.uwo.ca/current_students/student_finances/index.html

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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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