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Masters Degrees (Computer Science And Statistics)

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Research in Computer Science at York is carried out at the frontiers of knowledge in the discipline. This course gives you the chance to study a range of advanced topics in Computer Science, taught by researchers active in that area. Read more
Research in Computer Science at York is carried out at the frontiers of knowledge in the discipline. This course gives you the chance to study a range of advanced topics in Computer Science, taught by researchers active in that area. This means you will be learning current research results, keeping you at the forefront of these areas. You will also learn a range of theories, principles and practical methods.

The MSc in Advanced Computer Science is a full time, one year taught course, intended for students who already have a good first degree in Computer Science, and would like to develop a level of understanding and technical skill at the leading edge of Computer Science.

You can choose modules on a range of topics, including Cryptography, Functional Programming, Interactive Technologies, Natural Language Processing, Quantum Computation and Model-Driven Engineering.

Course aims
You will gain an in-depth knowledge of topics on the frontiers of Computer Science in order to engage in research or development and application of leading-edge research findings.

By undertaking an individual project, you will become a specialist in your selected area. You will be encouraged to produce research results of your own. This will prepare you to undertake a PhD in Computer Science should you wish to continue studying within the subject.

Learning outcomes
-A knowledge of several difference topics in Computer Science at an advanced level.
-An understanding of a body of research literature in Computer Science in your chosen topic, and the underlying principles and techniques of research in this area.
-An ability to engage in independent study at an advanced level, and develop skills in self-motivation and organisation.

Research Project

You will undertake your individual research project over the Summer term and Summer vacation. This will be a culmination of the taught modules you have taken during the course, which will allow you to focus on a specialist area of interest.

You will be allocated a personal supervisor, who will be an expert in your chosen area of research. You will be hosted by the research group of your supervisor, and you will benefit from the knowledge and resources of the whole group. Being attached to a research group also allows you to take part in their informal research seminars, and receive feedback and help from other members of the group.

You can choose from projects suggested by members of our academic staff. You also have the option of formulating your own project proposal, with the assistance from your personal supervisor.

All project proposals are rigorously vetted and must meet a number of requirements before these are made available to the students. The department uses an automated project allocation system for assigning projects to students that takes into account supervisor and student preferences.

The project aims to give you an introduction to independent research, as well as giving you the context of a research group working on topics that will be allied to your own. You will develop the skills and understanding in the methods and techniques of research in Computer Science.

As part of the assessment of the project, as well as your dissertation, you will give a talk about your work and submit a concise paper which we will encourage you to publish.

Information for Students

The MSc in Advanced Computer Science exposes you to several topics in Computer Science that are under active research at York. The material taught is preparatory to helping to continue that research, and perhaps continuing to a PhD. What we require from you are enthusiasm, hard work and enough background knowledge to take your chosen modules.

The modules on the MSc in Advanced Computer Science are mostly shared with our Stage 4 (Masters level) undergraduates. Your technical background will be different, and we acknowledge this.

During August we will send entrants a document describing the background knowledge needed for each module and, in many cases, references to where this knowledge is available (for example, widely available text books and web pages).

More generally, many of the modules expect a high level of mathematical sophistication. While the kind of mathematics used varies from module to module, you will find it useful to revise discrete mathematics (predicate and propositional calculi, set theory, relational and functional calculi, and some knowledge of formal logic), statistics and formal language theory. You should also be able to follow and produce proofs.

Careers

Here at York, we're really proud of the fact that more than 97% of our postgraduate students go on to employment or further study within six months of graduating from York. We think the reason for this is that our courses prepare our students for life in the workplace through our collaboration with industry to ensure that what we are teaching is useful for employers.

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

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

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

TRACKS

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

PROSPECTIVE CAREER

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

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

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

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Degree. Master of Science (two years) with a major in Computer Science and Engineering. Computer science and technology play a key role in every part of the modern world. Read more
Degree: Master of Science (two years) with a major in Computer Science and Engineering.

Computer science and technology play a key role in every part of the modern world. The Computer Science master's programme is centred on the need for computer scientists to master the theoretical foundations of the field and be able to apply and integrate them with other technologies.

The first semester of the programme comprises mandatory core courses in theoretical computer science and programming such as Theory of Computing and Database Technology.

The second and third semesters include elective courses based on students areas of specialisation such as: artificial intelligence, databases and data mining, internet computing, the design and programming of computer games, information security, language technology, human-computer interaction, theoretical computer science or the design and implementation of computer languages.

As the courses given during the programme address both theoretical and practical issues, applicants are expected to have an adequate background in computer science and good programming skills, see the specific requirements.

The Computer Science master's programme focuses on the acquisition of skills necessary for a career at the frontline of modern software technology such as operative system designer, Internet security specialists or game engine programmer. The programme also prepares students for a career in research or continued studies towards a doctoral degree.

The programme is taught at Linköping University, the home of one of the largest centres of computer science and engineering in Northern Europe with 175 employees, including 20 full professors, and internationally renowned for the high quality of its research and education. The research at the Department of Computer Science covers a broad spectrum of topics such as artificial intelligence, cognitive science, security, databases, distributed systems, embedded and real-time systems, human-computer interaction, software engineering.

Welcome to the Institute of Technology at Linköping University

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

Degree information

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

Students undertake modules to the value of 180 credits.

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

Core modules
-Introduction to Statistical Data Science
-Introduction to Supervised Learning
-Statistical Design of Investigations
-Statistical Computing

Optional modules - st least two from a choice of Statistical Science modules including:
-Applied Bayesian Methods
-Decision & Risk
-Factorial Experimentation
-Forecasting
-Quantitative Modelling of Operational Risk and Insurance Analytics
-Selected Topics in Statistics
-Stochastic Methods in Finance I
-Stochastic Methods in Finance II
-Stochastic Systems

Up to two from a choice of Computer Science modules including:
-Affective Computing and Human-Robot Interaction
-Graphical Models
-Statistical Natural Language Processing
-Information Retrieval & Data Mining

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

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

Careers

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

The Data Science MSc is a new programme with the first cohort of students due to graduate in 2017. Recent career destinations for graduates of the related Statistics MSc include:
-Towers Watson, Actuary Analyst
-Proctor & Gamble, Statistician
-Ernst & Young, Audit Associate
-Collinson Group, Insurance Analyst
-UCL, PhD Statistical Science

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

Why study this degree at UCL?

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

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

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

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Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study Data Science at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Read more
Take advantage of one of our 100 Master’s Scholarships or College of Science Postgraduate Scholarships to study Data Science at Swansea University, the Times Good University Guide’s Welsh University of the Year 2017. Postgraduate loans are also available to English and Welsh domiciled students. For more information on fees and funding please visit our website.

MSc in Data Science aims to equip students with a solid grounding in data science concepts and technologies for extracting information and constructing knowledge from data. Students of the MSc Data Science will study the computational principles, methods, and systems for a variety of real world applications that require mathematical foundations, programming skills, critical thinking, and ingenuity. Development of research skills will be an essential element of the Data Science programme so that students can bring a critical perspective to current data science discipline and apply this to future developments in a rapidly changing technological environment.

Key Features of the MSc Data Science

The MSc Data Science programme focuses on three core technical themes: data mining, machine learning, and visualisation. Data mining is fundamental to data science and the students will learn how to mine both structured data and unstructured data. Students will gain practical data mining experience and will gain a systematic understanding of the fundamental concepts of analysing complex and heterogeneous data. They will be able to manipulate large heterogeneous datasets, from storage to processing, be able to extract information from large datasets, gain experience of data mining algorithms and techniques, and be able to apply them in real world applications. Machine learning has proven to be an effective and exciting technology for data and it is of high value when it comes to employment. Students of the Data Science programme will learn the fundamentals of both conventional and state-of-the-art machine learning techniques, be able to apply the methods and techniques to synthesise solutions using machine learning, and will have the necessary practical skills to apply their understanding to big data problems. We will train students to explore a variety visualisation concepts and techniques for data analysis. Students will be able to apply important concepts in data visualisation, information visualisation, and visual analytics to support data process and knowledge discovery. The students of the Data Science programme also learn important mathematical concepts and methods required by a data scientist. A specifically designed module that is accessible to students with different background will cover the basics of algebra, optimisation techniques, statistics, and so on. More advanced mathematical concepts are integrated in individual modules where necessary.

The MSc Data Science programme delivers the practical components using a number of programming languages and software packages, such as Hadoop, Python, Matlab, C++, OpenGL, OpenCV, and Spark. Students will also be exposed to a range of closely related subject areas, including pattern recognition, high performance computing, GPU processing, computer vision, human computer interaction, and software validation and verification. The delivery of both core and optional modules leverage on the research strength and capacity in the department. The modules are delivered by lecturers who are actively engaged in world leading researches in this field. Students of the Data Science programme will benefit from state-of-the-art materials and contents, and will work on individual degree projects that can be research-led or application driven.

Modules

Modules for the MSc Data Science programme include:

- Visual Analytics
- Data Science Research Methods and Seminars
- Big Data and Data Mining
- Big Data and Machine Learning
- Mathematical Skills for Data Scientists
- Data Visualization
- Human Computer Interaction
- High Performance Computing in C/C++
- Graphics Processor Programming
- Computer Vision and Pattern Recognition
- Modelling and Verification Techniques
- Operating Systems and Architectures

Facilities

The Department of Computer Science is well equipped for teaching, and is continually upgrading its laboratories to ensure equipment is up-to-date – equipment is never more than three years old, and rarely more than two. Currently, our Computer Science students use three fully networked laboratories: one, running Windows; another running Linux; and a project laboratory, containing specialised equipment. These laboratories support a wide range of software, including the programming languages Java, C# and the .net framework, C, C++, Haskell and Prolog among many; integrated programme development environments such as Visual Studio and Netbeans; the widely-used Microsoft Office package; web access tools; and many special purpose software tools including graphical rendering and image manipulation tools; expert system production tools; concurrent system modelling tools; World Wide Web authoring tools; and databases.

As part of the expansion of the Department of Computer Science, we are building the Computational Foundry on our Bay Campus for computer science and mathematical science.

Career Destinations

- Data Analyst
- Data mining Developer
- Machine Learning Developer
- Visual Analytics Developer
- Visualisation Developer
- Visual Computing Software Developer
- Database Developer
- Data Science Researcher
- Computer Vision Developer
- Medical Computing Developer
- Informatics Developer
- Software Engineer

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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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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 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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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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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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Degree. Master of Science (120 credits) with a major in Statistics. Teaching language. English. The rapid growth of databases provides business people and scientists with vast new resources. Read more
Degree: Master of Science (120 credits) with a major in Statistics
Teaching language: English

The rapid growth of databases provides business people and scientists with vast new resources. This programme - on the interface between statistics and computer science - will help students discover patterns and trends in large and complex datasets, and turn data into information and knowledge.

Statistics and data mining is a programme with unique career opportunities. The methods to organise and analyse data are getting more and more powerful, and this makes the synergy between data mining and statistics immensely rewarding. Economic transactions, customer data, individual health records and environmental data are just a few examples of the contents of enormous databases awaiting thorough examination.

The programme prepares students for professional work with data analysis in private enterprises and government agencies. It can also provide an ideal background for students who aspire to pursue a career as researchers in statistics or computer science.

The programme is designed for students with a solid background in statistics, computer science or applied mathematics, and an aspiration to learn more about data analysis. Being at the interface between statistics and computer science, it provides skills that cannot be achieved in conventional discipline-oriented programmes. The focus is on applications of data analysis, but underlying theories are also given considerable space.

Data mining courses, covering prediction, classification, clustering and association analysis constitute the core of the programme. Visualisation, survey sampling and theory of science are also mandatory. Supporting courses and profile courses (in statistics or computer science) and the master’s thesis project enables students to tailor the programme towards specific applications.

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1. Big Challenges being addressed by this programme – motivation. Globally, there is a reported shortage of data analytics talent, particularly of individuals with the required deep technical and analytical skills. Read more

About the Course

1. Big Challenges being addressed by this programme – motivation

• Globally, there is a reported shortage of data analytics talent, particularly of individuals with the required deep technical and analytical skills.
• Accenture, Gartner and McKinsey have all identified Data Analytics as one of the fastest growing employment areas in computing and one most likely to make an impact in the future.
• The Irish Government’s policy is for Ireland to become a leading country in Europe for big data and analytics, which would result in 21,000 potential new employment opportunities in Ireland alone.
• CNN has listed jobs in this area in their Top 10 best new jobs in America.

2. Programme objectives & purpose

This is an advanced programme that provides Computing graduates with advanced knowledge and skills in the emerging growth area of Data Analytics. It includes advanced topics such as Large-Scale Data Analytics, Information Retrieval, Advanced Topics in Machine Learning and Data Mining, Natural Language Processing, Data Visualisation and Web-Mining. It also includes foundational modules in topics such as Statistics, Regression Analysis and Programming for Data Analytics. Students on the programme further deepen their knowledge of Data Analytics by working on a project either in conjunction with a research group or with an industry partner.

Graduates will be excellently qualified to pursue careers in national and multinational industries in a wide range of areas. Our graduates currently work for companies as diverse as IBM, SAP, Cisco, Avaya, Google, Fujitsu and Merck Pharmaceuticals as well as many specialised companies and startups. Opportunities will be found in:
• Multinational companies, in Ireland and elsewhere, that provide services and solutions for analytics and big data or whose business depend on analytics and big data technologies;
• Innovative small to medium-sized companies and leading-edge start-ups who provide analytics solutions, services and products or use data analytics to develop competitive advantage
• Companies looking to extend their research and development units with highly trained data analytic specialists
• PhD-level research in NUI Galway, elsewhere in Ireland, or abroad

3. What’s special about CoEI/NUIG in this area:

• The MSc in Computer Science (Data Analytics) is being delivered by the Discipline of Information Technology in collaboration with the Insight Centre for Data Analytics (http://insight-centre.org) and with input from the School of Mathematics, Statistics and Applied Mathematics in NUI Galway
• The Discipline of Information Technology at NUI Galway has 25-year track record of education, academic research, and industry collaboration in the field of Computer Science
• The Insight centre at NUI Galway is Europe’s largest research centre for Data Analytics

4. Programme Structure – ECTS weights and split over semester; core/elective, etc.:

• 90ECTS programme
• one full year in duration, beginning September and finishing August
• comprises:
- Foundational taught modules (20 ECTS)
- Advanced taught modules (40 ECTS)
- Research/Industry Project (30 ECTS).

5. Programme Content – module names

Sample Foundational Modules:

• Tools and Techniques for Large Scale Data Analytics
• Programming for Data Analytics
• Machine Learning and Data Mining
• Modern Information Management
• Probability and Statistics
• Discrete Mathematics
• Applied Regression Models
• Digital Signal Processing

Sample Advanced Modules:

• Advanced Topics in Machine Learning and Information Retrieval
• Web Mining and Analytics
• Systems Modelling and Simulation
• Natural Language Processing
• Data Visualisation
• Linked Data Analytics
• Case Studies in Data Analytics
• Embedded Signal Analysis and Processing

6. Testimonials

Ms. Gofran Shukair, MSc, Research Engineer at ZenDesk, Ireland

After graduating with an MSc at NUI Galway, Gofran worked with Fujitsu’s Irish Research Lab as a research engineer before moving to a software engineering position at Zendesk, Ireland.

“The mix of technical and soft skills I gained through my Masters studies at NUI Galway is invaluable. I had the chance to work with great people and to apply my work on real world problems. With the data management and analysis skills I gained, I am currently pursuing my research in an international research project with one of the leading IT companies. I will be always thankful for studying at NUI Galway, a great historic place based in a culturally-rich vibrant city with an international mix of young and ambitious students that made me eager to learn and contribute back the moment I graduated.”

For further details

visit http://www.nuigalway.ie/courses/taught-postgraduate-courses/msc-in-computer-science-data-analytics.html

How to Apply:

Applications are made online via the Postgraduate Applications Centre (PAC) https://www.pac.ie
Please use the following PAC application code for your programme:

M.Sc. Computer Science – Data Analytics - PAC code GYE06

Scholarships :

Please visit our website for more information on scholarships: http://www.nuigalway.ie/engineering-informatics/internationalpostgraduatestudents/feesandscholarships/

Visit the M.Sc. Computer Science – Data Analytics page on the National University of Ireland, Galway web site for more details!

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The Department of Computer Science at Aberystwyth has a strong research focus on techniques and applications of intelligent systems, working with many major companies. Read more
The Department of Computer Science at Aberystwyth has a strong research focus on techniques and applications of intelligent systems, working with many major companies. Our taught Masters degrees draw on this focus, and link to the expertise and interests of the Department. They are designed to meet the needs of both students wanting a foundation for a career in research, and those wanting to expand on their skills to accelerate their industrial career.

Contemporary software is frequently developed to function in distributed systems. Applications are deployed across multiple computers, interacting to provide services and to solve problems in a distributed way. This Masters course is suitable for students intending to pursue a career in the software industry, and is a qualifying Masters Degree for Chartered Engineer status. It can also lead to a career in research.

See the website http://courses.aber.ac.uk/postgraduate/software-engineering-integrated-industrial-year/

Course detail

The course in Software Engineering is a two year full-time programme. This degree is the same as the one year MSc in Computer Science (Software Engineering) - G493, with the addition that the student spends a year working in industry after the taught part of the course.

Year one of the course is divided into two parts over three semesters. In part one, you will establish a breadth of necessary skills in a number of core modules whilst directing your own study by choosing specialist modules, worth a total of 120 credits. In part two, you will apply your learning in the individual dissertation worth an additional 60 credits.

Previous study topics have included: Transmission of MIDI music over internet connection, Designing a network intrusion detection system, Online results and statistics using web service technology, Supply chain management system applications and Prototype railway track measurement system.

Whatever your own previous experience or future aspiration, with this course you will benefit from the marvellous integration of cutting-edge theory and practical application, within a world-class department. The most recent Research Excellence Framework (2014) assessment found that 100% of the impact research the department of Computer Science undertakes is world leading.

Format

Industrial Year:

This degree is the same as the one year MSc in Computer Science (Software Engineering), with the addition that the student spends a year working in industry after the taught part of the course.

• Students study at Aberystwyth University from September to May, and are supported in applying for suitable jobs in the software industry.
• They work in the UK from June to the following May.
• They return to Aberystwyth to complete their dissertation from June to September

The work in industry is paid employment, not just work experience. Typical annual salaries for an industrial year are between £11,000 and £15,000.

Students wishing to do the industrial year are assisted in finding a place in industry. There is assistance with preparing an appropriate CV, training in what to expect at an interview, and practice in being interviewed by experienced industry interviewers. The Department of Computer Science sends about 70 students each year for a year's experience in industry, and has many contacts in companies enthusiastic to take good students from Aberystwyth University.

As these are paid jobs for companies, we cannot guarantee any student a job - the companies select the employees they want. Students that are unable to find a job can complete the Masters degree without an industrial year.

There is an additional but much reduced fee for the year in industry (presently £800 for the year), and members of staff stay in touch electronically and by visiting students during the year.

Assessment

The taught part of the course is delivered and assessed through lectures, student seminars, practical exercises, case studies, course work and formal examinations. The subsequent successful submission of your research dissertation leads to the award of an MSc.

Find out how to apply here https://www.aber.ac.uk/en/postgrad/howtoapply/

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Students may be registered for an inter-disciplinary M.Sc. or Ph.D. in two departments of the University. This requires the agreement of both departments and joint supervision from the two Departments. Read more
Students may be registered for an inter-disciplinary M.Sc. or Ph.D. in two departments of the University. This requires the agreement of both departments and joint supervision from the two Departments.

Students have enrolled in interdisciplinary programs in Computer Science and Mechanical Engineering, Computer Science and Electrical and Computer Engineering, Computer Science and Mathematics & Statistics, Computer Science and Philosophy, and Computer Science and Art.

The interdisciplinary program is intended for exceptionally well-qualified students who have a research program that necessarily bridges two disciplines. Those applicants with an interest in this possibility should indicate this on their application forms. It is important to to have already contacted potential supervisors in the departments involved before submitting the program application.

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This exciting and challenging programme studies how data can be utilised to solve major business and societal challenges. The programme provides students with the knowledge, technical ability and skills for leadership roles in the fields of business analytics and data science. Read more
This exciting and challenging programme studies how data can be utilised to solve major business and societal challenges. The programme provides students with the knowledge, technical ability and skills for leadership roles in the fields of business analytics and data science.

Degree information

The programme is designed to give students multidisciplinary skills in computing (i.e. programming, big data), analytics (i.e. data mining, machine learning, computational statistics, complexity), and business analysis. Emphasis will be on business problem framing, leveraging data as a strategic asset, and communicating complex analytical results to stakeholders.

Students undertake modules to the value of 180 credits. The programme consists of five core modules (90 credits), two optional modules (30 credits) and a dissertation (60 credits).

Core modules
-Programming for Business Analytics
-Data Analytics
-Information Retrieval and Data Mining
-Introduction to Supervised Learning
-Statistical NLP

Please note: the availability and delivery of modules may vary.

Optional modules
-Applied Machine Learning
-Graphical Models
-Web Economics
-Statistical Models and Data Analysis
-Statistical Design of Investigations
-Decision and Risk
-Consumer Behaviour and Behavioural Change
-Consulting Psychology
-Talent Management
-Data Science for Spatial Systems
-Group Mini Project: Digital Visualisation
-Urban Simulation
-Mastering Entrepreneurship
-Decision and Risk Analysis
-Managing Hi-Tech Organisations

Please note: the availability and delivery of modules may vary.

Dissertation/report
During the summer students will undertake a work placement with a UCL industrial partner. The research and data analysis conducted during this placement will form the basis of a 10,000-word dissertation.

Teaching and learning
The programme is delivered through a combination of lectures by world-class academics and industry leaders, seminars, workshops, tutorials and project work. The programme comprises two terms of taught material, followed by examinations and then a project. Assessment is through unseen written examinations, coursework and the dissertation.

Careers

Graduates of UCL Computer Science are particularly valued due to the department's international status, and strong reputation for leading research. Recent graduate destinations include: IBM, Samsung, Microsoft, Price Waterhouse Coopers, Citibank.

Employability
This programme is designed to satisfy the need, both nationally and internationally, for exceptional data scientists and analysts. Graduates will be highly employable in global companies and high-growth businesses, finance and banking organisations, major retail and service companies, and consulting firms. They will be equipped to influence strategy and decision-making, and be able to drive business performance by transforming data into a powerful and predictive strategic asset. We expect our graduates to progress to leading and influential positions in industry.

Why study this degree at UCL?

UCL Computer Science is a global leader in research in experimental computer science. The 2014 Research Excellence Framework (REF) ranked the department as first in the UK for research, with 96% regarded as internationally excellent.

The department consists of a team of world-class academics specialising in big data, computational statistics, machine learning and complexity.

The programme aims to create the next generation of outstanding academics and industry pioneers, who will use data analysis to deliver real social and business impact.

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