Masters Degrees (Data Visualisation)

Why take this course?

As a Data Visualisation Designer you can contribute innovative solutions with the potential to transform societal challenges, by designing the human interface to increasingly complex problems.

On this course, you will learn how to create rich and meaningful stories with data. We will study digital content in any mode, whether it is in alphanumeric form, binary, vector, pixel, video, or others. The designer provides an important interface, that allows us to explore data and generates meaningful communication. This communication is predominantly visual, but with developments in Wearables and the Internet of Things, is also becoming increasingly physical, affective, networked and interactive. Data Visualisation Design spans traditional graphic and information design, interaction design, information architecture, computational design, design thinking and user-centred and user experience design.

What will I experience?

On this course you can:

Learn the theory and practice of data visualisation, data, interface/interaction design and user experience, and apply this to your own design
Critically question the role of data related to the social, political, economic and cultural through contextual research
Explore live data sets from real world scenarios, such as industry or charities like the digital humanitarian network
Develop independent research and project ideas to create innovative, forward thinking design solutions and experiences for a digital and data driven world

What opportunities might it lead to?

The course will prepare you to work in the design disciplines of the creative industries, with a focus on data visualisation, information design, computational design, digital content, interactivity and user experience. Data Visualisation designers are in demand in sectors including business, research, health, education, government/public service, the arts.

The skills gained on this course can also be applied to employment in UI (user interface) design, or focus on interaction as a UX (User experience) designer. The critical and contextual outlook allows you to position yourself as a strategist and operate in a consultative manner. The research aspect of the course would also suit a career in compulsory, further and higher education.

Careers include:

Data Visualisation Design
Information Design
Digital Graphic Design
UI (user interface) / UX (user experience) design
Interaction design

Module Details

The course is offered over one year (full-time) or two years (part-time).

You will study five units, one of which is shared with other MA courses in the School of Art and Design. There will be preparatory units delivering a grounding in practical skills, theoretical context and academic research (competencies and skills). You will also study units that allow more thematic engagement with interactive and data driven design in terms of theory such as critical design, affordances, experience and complexity. It will also provide a unit oriented towards employability, and incorporate live briefs and group work. These units work to catalyse your own ideas and research direction for the Major Project unit.

Core units currently comprise:

A Question of Research
Fundamentals of Data and Interaction Design
Digital Futures – Themes and Issues in Practice
Design Solutions for Enterprise, Society and Culture
Major Project

Programme Details

The teaching combines interactive lectures and group seminar discussions with support through one-to-one tutorials. You also receive feedback on your work through friendly but critical peer review in group sessions with other students, members of faculty and other experts as appropriate. One of the units includes working as a team. Your project work emphasises self-initiated learning which gives you the freedom to explore the specialist area of your interest, while being helpfully guided by your supervisor. The curriculum is very closely related to the research areas in the department, so the staff have cutting edge knowledge of the field and its potential for innovation.

Your learning is mostly assessed through the submission of practical course work, such as digital prototypes, and the documentation of the learning journey in sketchbooks, diaries, blogs or journals.

This will be documenting contextual research as well as stages in practical experimentation and annotation of reflection. There are some written elements to be submitted as well, mostly accompanying proposals/reports to contextualise your practice. The assessment also includes individual and group presentations, this mode is also used to give you formative feedback on your work throughout.

Here's how we assess your work:

Digital artefacts / prototypes
Learning journals
Proposals
Reports
Oral presentation

Student Destinations

This course is an opportunity to focus your creative design practice on the interactive, data driven, user centred and culturally contextualised. It also enhances your design career by upgrading your skills and widening your knowledge and thinking in the digital arena, allowing you to stay one step ahead of the rest. The independent research aspect of the course prepares you for further education in terms of a research degree and employment in R&D and/or education.

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Introduction

MA Data Visualisation is driven by the intelligent interrogation of data and intensive practice-led research. In a data-driven society, it is increasingly important for businesses, policymakers, governments and NGO’s to communicate data coherently using diverse visualisation tools and techniques. 

Content

You will develop the ability to translate statistics into creative narratives that a wide variety of audiences can understand using the most appropriate form of communication design for each project, including print, physical objects, environmental design or a screen based interaction. 

You'll be supported in developing an independent and critical view, producing visual narratives to bring about positive change, taking a collaborative and cross-disciplinary approach across sectors including science, health, education and underrepresented social groups through the use of data.

The course recognises the social, cultural and ethical dimensions of data visualisation practices and will support you in developing a highly informed understanding of the role and implications of an editorial voice. 

Structure

MA Data Visualisation is delivered across four terms, starting in September and finishing in December the year after. Incorporating a summer break, this is a one-year full-time course (45 taught weeks), delivered over 15 months. 

Terms One and Two

In the first and second terms, students will engage in a series of workshops developing data visualisation research and practices. You'll participate in skills focused workshops, use visualisation tools to analyse data sets and develop data stories.  

Terms Three and Four

In the third and final term, you'll develop a project proposal and work with our external partners on collaboration projects and research.

You'll produce a final major project and a critical visual report that will give you the opportunity to engage in a major research-led project. Here the emphasis will be on defining, analysing and developing an individual and focused approach to a data visualisation related problem, subject, question or issue.

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Parsons’ Master of Science in Data Visualization is a multidisciplinary program in which students develop skills bringing together visual design, computer science, statistical analysis, and ethical considerations of data analysis and representation. The presentation of data plays a critical role in the shaping of opinion, policy, and decision making in today’s increasingly global society. Giving students a competitive edge as they enter the field, the MS program responds to the increased demand for experts who can turn data into insight.

This program is part of Parsons' School of Art, Media, and Technology (AMT). Learn about the AMT community and explore our blog to see what students, faculty, and alumni are doing in NYC and around the world.

Theory and Practice

Housed within Parsons’ School of Art, Media, and Technology, the MS Data Visualization program can be completed in one or two years. The 30-credit curriculum integrates theory and studio practice, so students acquire the creative, quantitative, and coding tools needed to analyze and depict data, gaining a holistic understanding of context, audience, and objectives. With the MS in Data Visualization, students obtain the diverse skill set needed for success in a range of careers related to data interpretation. Students graduate with portfolios demonstrating their ability to create databases and Web-based software tools that reflect an understanding of data analysis and information visualization for varied applications.

Opportunities with Local Industry

The program’s setting in New York City, a technological hub and pioneer of open-source culture, offers invaluable industry access. Students intern with industry leaders and external partners from the government, nonprofit, and commercial sectors. Faculty invite guest lecturers and critics to share their insights and expose students to new possibilities in data visualization and related career paths.

Future Opportunities

Graduates find success in a wide variety of fields including data analysis, digital design, advertising and branding, journalism, business consulting and analytics strategy, management, strategic planning, entrepreneurship, social enterprise, public policy, trend forecasting, and business intelligence.

You can request more information about all possible future opportunities here: http://www.newschool.edu/m/data-visualization?utm_source=find_a_masters&utm_medium=hyperlink_listing&utm_campaign=pm_parsons_grad&utm_term=data_visualization

Financial Aid Deadline

All applicants selected for admission into our program are considered for a merit scholarship award that is determined by the strength of their application. Scholarship award notification is communicated at the same time as the admission decision. International students are eligible only for merit scholarships. If you are a U.S. citizen or eligible noncitizen, we encourage you to complete the Free Application for Federal Student Aid (FAFSA), which can be found on the Web at http://www.fafsa.gov. The FAFSA is available each year on October 1. You do not need to wait for an admission decision to apply for federal aid; we recommend submitting by our FAFSA priority deadline of February 1 for fall applicants. (The New School’s federal school code is 002780.)

You can request more information on available scholarships here: http://www.newschool.edu/m/data-visualization?utm_source=find_a_masters&utm_medium=hyperlink_listing&utm_campaign=pm_parsons_grad&utm_term=data_visualization

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Developed to meet the demand for data science professionals, our postgraduate Data Analytics course enables you to effectively structure, analyse and gain insight from a wide range of complex data across different industries.

Designed in close consultation with industry partners including the NHS Business Services Authority, Teradata, BT, SAS, the Pensions Regulator and local Brighton companies, your learning is informed by current business developments through case studies looking at real-world data sets, research questions and scenarios. You have the opportunity to collaborate on projects with our industry partners, and can also use your own data, project ideas and industry links.

Guest lecturers will share their knowledge and expertise with you, such as Tom Khabaza who is a founding chairman of the Society of Data Miners, author of 9 Laws of Data Mining and was involved in designing the course.

You will develop a skill set in specialist data analytics and associated software, quantitative methods and techniques, and business intelligence. Our staff are experts in their field and you have the chance to develop your knowledge in specialist areas where we have ongoing research and expertise, such as sequential forecasting, natural language processing and image processing.

Whether you are a recent graduate or an experienced professional wanting to gain data analysis skills, this course is available on a full or part-time basis to help you manage your studies around other commitments. 

Course structure

The course covers three main areas:

• data management – structuring and manipulating data for analysis purposes
• data interpretation – statistical analysis using advanced features of industry-standard software such as SAS, SPSS and R
• project management – the business-specific and strategic aspects of analytics.

You will learn how to assess project viability, propose sound business cases and strategies for analysis, perform and oversee analysis and manage large data projects successfully as well as developing your critical appraisal and presenting techniques. 

Based at our Moulsecoomb campus, you will have access to computer and research labs equipped with specialist, sophisticated software including SAS, SPSS Statistics and SPSS Modeller. Affordable student licences for home use are also available. 

With a flexible timetable to suit full-time or part-time students and commuters, and lecturers available to support you in your module choices, there are different study routes available to you.

Syllabus

You will study five core modules. One of these involves a major project, potentially in collaboration with industry. You will also choose option modules, subject to availability, allowing you to focus on particular areas of interest.

Core modules

• Data Management – provides an understanding of contemporary database management systems. Explores a methodology for database design and development, and develops skills in searching, reporting and analysing the data. Topics covered include database implementation and administration, data modelling and business intelligence.
• Programming for Analytics – provides competencies in computer programming and algorithm design with emphasis on statistical programming and data analysis. The module covers both general issues of algorithm design and data structures and implementation issues in R and SAS.
• Data Visualisation and Analysis – covers principles of data visualisation and specialised tools for data visualisation and analysis such as SAS Visual Analytics and Qlikview. The module also explores the mathematical and statistical theory behind data analysis.
• Business Analytics Strategy and Practice – develops analytics-specific project planning concepts within this context, enabling students to design and manage analytics projects and present the business case to senior management.
• Industry project – substantial, independent project undertaken with the supervision of a member of the teaching team. Projects are normally industry-based using real data sets.

Option modules*

• Multivariate Analysis and Statistical Modelling – design statistical experiments, analyse multivariate data and apply classical and modern statistical modelling techniques. Enhances skills in the use of specialist software such as R, SPSS or SAS.
• Data Mining and Knowledge Discovery in Data – find useful and relevant patterns, trends and anomalies in data sets, and summarise them in a form which may be used to support enterprise decisions – one of the great challenges of the information age. Emphasis is on the big, real-world picture rather than inside-the-box systems design engineering details. 
• Stochastic Methods and Forecasting – an understanding of stochastic models and their applications in a business context. The module also covers forecasting methods with the emphasis on selecting the best forecasting method for a business problem and correct application of that method.
• Risk Analysis and Retail Finance – introduction to the statistical methods used to estimate risk and reward in retail credit. The focus is on retail finance especially the provision of credit and lending services.
• Medical Statistics – introduction to the methods originally designed for clinical trials and now being used in other contexts including sociology and marketing research. Topics include assessment of risk factors, comparing treatments and assessing survival data.

*Option modules are indicative and may change, depending on timetabling and staff availability.  

Employability

A wide variety of organisations draw upon data analytics specialists to help produce valuable information for decision-making, for example commodity price forecasting, customer intelligence, clinical trials, R&D and many other areas utilising large amounts of data.

Graduates are able to choose from a range of private, governmental and academic roles, depending on their personal interests. Some of our full-time students find a full-time job and switch to part-time study in the middle of the course.

Graduate destinations include:

• government bodies such as the Pensions Regulator and local councils
• transnational corporations such as Capgemini
• local companies such as iCrossing.

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The MSc in Data Science will provide you with the technical and practical skills to analyse the big data that is the key to success in future business, digital media and science.

The rate at which we are able to create data is rapidly accelerating. According to IBM, globally, we currently produce over 2.5 quintillion bytes of data a day. This ranges from biomedical data to social media activity and climate monitoring to retail transactions. These enormous quantities of data hold the keys to success across many domains from business and marketing to treating cancer or mitigating climate change.

The pace at which we produce data is rapidly outstripping our ability to analyse and use it. Science and industry are crying out for a new generation of data scientists who combine the statistical skills of data analysis and the computational skills needed to carry out this analysis on a vast scale.

The MSc in Data Science provides you with these skills. 

Studying this Masters, you will learn the mathematical foundations of statistics, data mining and machine learning, and apply these to practical, real world data.

As well as these statistical skills, you will learn the computational techniques needed to efficiently analyse very large data sets. You will apply these skills to a range of real world data, under the guidance of experts in that domain. You will analyse trends in social media, make financial predictions and extract musical information from audio files. 

The degree will culminate in a final project in which you will you can apply your skills and follow your specialist interests. You will do a novel analysis of a real world data of your choice. 

The programme includes:

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

Modules & structure

You will study the following core modules:

• Machine Learning & Statistical Data Mining
• Big Data Applications15 credits Data Programming
• Data Science Research Topics
•  Final Project in Data Science

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

• Artificial Intelligence
• Interaction Design
• Neural Networks
• Interactive Data Visualisation
• Introduction to Natural Language Processing
• Geometric Data Analysis

Skills & careers

Data Science is one of the fastest growing sectors of employment internationally. Big Data is an important part of modern finance, retail, marketing, science, social science, medicine and government. 

The study of a combination of long established fields such as statistics, data mining, machine learning and databases with very modern and strongly related fields as big data management and analytics, sentiment analysis and social web mining, offers graduates an excellent opportunity for getting valuable skills in advanced data processing. 

This could lead to a variety of potential jobs including: 

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

Find out more about employability at Goldsmiths.

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

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

Key Features of the MSc Data Science

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

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

Modules

Modules for the MSc Data Science programme include:

- Visual Analytics

- Data Science Research Methods and Seminars

- Big Data and Data Mining

- Big Data and Machine Learning

- Mathematical Skills for Data Scientists

- Data Visualization

- Human Computer Interaction

- High Performance Computing in C/C++

- Graphics Processor Programming

- Computer Vision and Pattern Recognition

- Modelling and Verification Techniques

- Operating Systems and Architectures

Facilities

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

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

Career Destinations

- Data Analyst

- Data mining Developer

- Machine Learning Developer

- Visual Analytics Developer

- Visualisation Developer

- Visual Computing Software Developer

- Database Developer

- Data Science Researcher

- Computer Vision Developer

- Medical Computing Developer

- Informatics Developer

- Software Engineer

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Train to become a data analyst in a growing industry by studying techniques such as data mining, statistical modelling, business intelligence and data visualisation. Study on a course which has been developed with direct input from industry experts who will bring real life business case scenarios to you.

More about this course

This specialist advanced course will equip students with the theoretical, technical and practical data analytics competencies required in an area of economic growth. The course curriculum content has been developed with direct input from industry experts and utilises specialist software tools and techniques. Students’ experience of the course will be enriched with exposure to real life business case scenarios brought to them by skilled professionals in industry.

The specialist nature of the course will allow students to explore and experience advanced techniques in data science. Students will acquire practical skills, often first-hand from an external practitioners, preparing them for employment as data analysts. Students will also be trained in the use of software tools and environments currently used by the industry sector. For example, students on this course will have exposure to R and Python programming, IBM SPSS, SAS®, Tableau, Oracle and Hadoop.

A range of assessment methods are used on the course, including written reports, practical and research assignments, demonstrations, presentations, group work and examinations.

Modular structure

The modules listed below are for the academic year 2016/17 and represent the course modules at this time. Modules and module details (including, but not limited to, location and time) are subject to change over time.

Year 1 modules include:
-Data Analysis and Visualization (core, 20 credits)
-Data Mining for Business Intelligence (core, 20 credits)
-Data Modelling and OLAP Techniques for Data Analytics (core, 20 credits)
-MSc Project (core, 60 credits)
-Programming for Data Analytics (core, 20 credits)
-Statistical Modelling and Forecasting (core, 20 credits)
-Financial Mathematics (option, 20 credits)
-Work Related Learning (option, 20 credits)

After the course

On completion of the course graduates will be well equipped to work in some of the fastest growing sectors of the data science and big data industries. The course offers wide-ranging career opportunities in the commercial industry, public and financial services, especially in areas requiring big data analysis such as consumer, healthcare, scientific, financial, security intelligence, business and social sciences.

Job roles include data scientist, data analyst, digital analyst, big data consultant, statistical analyst and data modeller. Graduates will be eligible to work as data analysts or data scientists in a multitude of areas where skills such as R or Python programming, machine learning and statistical modelling, SAS® and SPSS experience, data visualisation and data-driven decision-making are required.

The course also provides an excellent basis for further study for those wishing to pursue a higher-level research degree or embark on an industry-based research career.

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

Healthcare, with an already established strong relationship with Information & Communication Technologies (ICT), is continuously expanding the knowledge forefront as new methods of acquiring data concerning the health of human beings are developed.

Processing this data to extract valuable information about a population (epidemiological applications) or the individual (personalised healthcare applications) is the work of health data scientists. Their work has the potential to improve quality of life on a large scale.

Swansea University is the first institution in the UK to offer this taught master's programme in Health Data Science designed to develop the essential skills and knowledge required of the Health Data Scientist.

Key Features of the Health Data Science Programme

- A one year full-time taught master's programme designed to develop the essential skills and knowledge required of the Health Data Scientist.

- The Health Data Science course is also available for three years part-time study.

- An integrated programme of studies tailored to the essential skill set required for Data Scientists operating within healthcare organisations covering key topics in computation, data modeling, visualisation, machine learning and key methodologies in the analysis of linked health data.

- Hands on experiential learning from the professionals behind the Secure Anonymised Information Linkage (SAIL) Databank, a UK-exemplar project for the large scale mining of healthcare data within a secure environment.

- Strong collaboration links with colleagues from the Centre for Health Services Research of the University of Western Australia, a group of leading experts in the analysis of linked health data.

- The Health Data Science course is based within the award winning Centres for Excellence for Administrative Data and eHealth Research of Swansea University, awarded by the Economic and Social Research Council (ESRC) and Medical Research Council (MRC), enhancing the quality of the course.

Who should study MSc Health Data Science?

The Health Data Science course is suitable for those working in healthcare with roles involving the analysis of health data and also computer scientists with experience in working with data from the healthcare domain, as well as biomedical engineers and other similar professions.

Course Structure

Students must complete 6 modules of 20 credits each and produce a 60 credits dissertation on a Health Data Science project. Each module of the programme requires a short period of attendance that is augmented by preparatory and reflective material supplied via the course website before and after attendance.

Attendance Pattern

Health Data Science students are required to attend the University for 1 week (5 consecutive days) for each module in Part One. Attendance during Part Two is negotiated with the supervisor.

Modules

Modules on the Health Data Science programme typically include:

Scientific Computing and Health Care

Health Data Modelling

Introductory Analysis of Linked Health Data

Machine Learning in Healthcare

Health Data Visualisation

Advanced Analysis of Linked Health Data

Professional Development

The College of Medicine offers the modules on the Health Data Science course as standalone opportunities for prospective students to undertake continued professional development (CPD) in the area of Health Data Science.

You can enroll on the individual modules for the Health Data Science programme as either an Associate Student (who will be required to complete the module(s) assessments) or as a Non-Associate Student (who can attend all teaching sessions but will not be required to complete any assessments).

For information and advice on applying for any of the continuing education opportunities, please contact the College directly at [email protected].

Employability

Postgraduate study has many benefits, including enhanced employability, career progression, intellectual reward and the opportunity to change direction with a conversion course.

From the moment you arrive in Swansea, specialist staff in Careers and Employability will help you plan and prepare for your future. They will help you identify and develop skills that will enable you to make the most of your postgraduate degree and enhance your career options. The services they offer will ensure that you have the best possible chance of success in the job market.

The student experience at Swansea University offers a wide range of opportunities for personal and professional development through involvement in many aspects of student life.

Co-curricular opportunities to develop employability skills include national and international work experience and study abroad programmes and volunteering, together with students' union and athletic union societies, social and leisure activities.

For the MSc Health Data Science course, we are in the process of identifying opportunities for our students to complete volunteering placements with a number of our collaborative partners.

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IN BRIEF:

• Study a course that is unique in the UK and has been specifically developed to meet the skills gap. Course content can be applied to very diverse fields- there are many job opportunities in this area.
• Gain SAS certification.
• Learn to tell a story from data. Become immersed in Big Data techniques and platforms, working with real-world messy data to gain experience across the data science stack.
• Part-time study option
• International students can apply

COURSE SUMMARY

Have you ever wanted to ‘Mung’ data? Apply Machine Learning techniques? Search for hidden patterns? Be part of Big Data?      

This course is your opportunity to specialize as a Data Scientist, one of the most in demand roles across all sectors including health, retail, and energy. Companies such as Google and Microsoft, and also public organisations such as the NHS are struggling to fill their vacancies in this field due to    a  lack of suitably qualified people. This course is unique in the UK in that it has been developed as a MSc conversion course – if you have a good honours degree in any discipline with a demonstrable mathematical aptitude, an enquiring mind, a practical and analytical approach to problem solving,    and  an ambition for a career in data science; then this course is for you.    

During your time with us, you will develop an awareness of the latest developments in the fields of Data Science and Big Data including advanced databases, data mining and big data tools such as Hadoop. You will also gain substantial knowledge and skills with the SAS business intelligence software suite  due  to    the  partnership of the University with the SAS Student Academy.  

"We are especially pleased to endorse the new MSc in Data Science. With the explosion of interest and investment in data science teams, our customers cannot get enough graduates with SAS-based analytical skills. Courses such as this new MSc are an important step forward by the University to addressing this skills shortage, especially amongst home students." - SAS

COURSE DETAILS

This course covers a very comprehensive range of topics split in to four large modules worth 30 credits each plus the MSc Project worth 60 credits. External speakers from blue-chip and local companies will give seminars to complement your learning, that will be real-world case studies related to the subjects you are studying in your modules. These are designed to improve the breadth of your learning and could lead to ideas that you can develop for your MSc Project.

TEACHING

The course is focused around the underpinning knowledge and practical skills needed for employment within the data sciences industry. There will be 22 hours of lectures; 11 hours of tutorials and 22 hours workshops; 2 hours of examination-based assessment; and 245 hours of independent study, assessed coursework and preparation for examination. This makes a total of 300 hours total learning experience.

• Lectures will be used to introduce ideas, and to stimulate group discussions.
• Tutorials will be used to develop problem solving strategies and to provide practice and feedback with scenarios to help with exam preparation.
• Workshops will be used to develop expertise in SAS tools, by analysing example datasets of increasing complexity.

ASSESSMENT

• 50% of the assessment will comprise a practical project where students will be given some data, will devise and carry out an analysis strategy and will present their interpretations and explain their strategy. 
• 50% will comprise an examination, which will assess more theoretical aspects of the course and will explore students’ immediate response to unseen scenarios or data.

CAREER PROSPECTS

A recent report by e-Skills and SAS (Big Data Analytics: An assessment of the demand for labour and skills, 2012-1017) indicates the demand forecast for staff with big data skills is predicted to ”rise by 92% between 2012 and 2017, and by 2017 there will be at least 28,000 job openings for big data staff in the UK each year…”

With this qualification, you’ll be equipped with the skill set and technical knowledge relevant for the data science and big data job market.

FURTHER STUDY

The Informatics Research Centre in the School of Computing, Science and Engineering at the University of Salford builds on the history, success and achievements of the research in Computer Science and Information Systems developed at the University of Salford over the last thirty years.

Evolving around Data and Information in all their types and usages, the Centre covers all phases and processes from data pre-processing to engineering and visualisation. The Centre is developing novel methods and systems for the analysis and recognition of various data sets, learning behaviours and causal models. The techniques and systems developed have a wide range of potential applications including digitisation of historical documents, medical diagnosis, semantic tagging, segmentation of types of viewers and their behaviours, text mining and retrieval and data visualisation.

Forensic computing, digital investigation and Cyber security is another area of expertise supported by the centre both at the theoretical and application levels.

Many students go on to further research in the fields of:

• Actionable Knowledge Discovery and Semantic Web
• Software Engineering and applications
• Big Data, Data Mining and Analytics
• Image and document processing and analysis
• Cyber Security and Forensics
• Information visualisation and virtual environments

FACILITIES

Facilities include a new Dell Cloud Computing platform with OpenStack and lab workstations, providing access to software platforms and languages specialized in Machine Learning, Data Mining, Statistical Analysis and Big Data including:

• R, SAS Enterprise Guide & Miner, Python, Apache Hadoop & Spark, RapidMiner
• NoSQL databases ie MongoDB

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Develop the skills you need for a career in this fast emerging field of data science.

This programme will deepen your understanding of advanced software development, systems for big data analytics, statistical data analysis, data mining, data privacy and security, data visualisation and exploration.

It is an interdisciplinary programme, designed for students with a first degree in subjects such as:

• Computer Science
• Electrical Engineering
• Mathematics
• Physics
• Economics

Our teaching is strongly linked to research within the Faculty of Engineering and Informatics, which includes aspects of applied computing, theoretical computer science, and communications and networks. Statistical analysis of data across various disciplines is a central theme of our research.

You can tailor your studies to particular areas of interest or career aspirations through our range of optional modules, and in the final dissertation (which is a significant piece of project work).

On graduation you’ll be ready and able to develop solutions to challenges in big data analytics and big data systems.

What you will study

The programme is intended to equip graduates with the cutting-edge knowledge and skills to work in the industry as a Data Scientist, Big Data Architect, or Big Data Analyst.

Modules

Core

• Software Development
• Big Data Systems and Analytics
• Statistical Data Analysis
• Data Mining
• Dissertation

Option

• Information Theory and Data Communication
• Security, Privacy and Data Protection
• Mobile Applications
• Concurrent and Distributed Systems
• Data Visualisation (PG)

Learning and assessment

You'll learn through a mixture of formal lectures, practical lab sessions, tutorials and seminars.

Some modules involve supervised group work, usually with an assigned academic staff member for each group.

Most modules are related to research in the school.

All modules require students to undertake independent study, supported through distance learning technologies such as our Virtual Learning Environment. Reading lists and suggested resources for independent study provide further direction for students to undertake this work, and regular contact hours and informal feedback throughout the courses provide opportunities for further guidance for learners.

Assessments for modules mostly take the form of practical coursework, lab tests and written exams, with all forms being well represented across all modules.

Facilities

Our facilities are impressive, with several laboratories filled with dual-screen, dual boot (Windows and Linux) systems packed with industry-standard software.

Our specialised labs, such as the Ethical Hacking lab and the Internet of Things lab, allow students to build their skills within these key areas of growth, in a structured way through taught modules.

We provide a range of online facilities to support independent learning, including our Virtual Learning Environment which gives you access to learning materials and collaborative learning tools 24/7, anywhere in the world. We also provide virtual server technology using in house hardware to allow students to use our operating systems remotely.

Career prospects

Big data is a major area for future growth and investment. As the global big data industry continues to grow year after year we continue to grow our programme, and continue to produce future leaders in an exciting and rewarding field.

The University is committed to helping students develop and enhance employability and this is an integral part of many programmes. Specialist support is available throughout the course from Career and Employability Services including help to find part-time work while studying, placements, vacation work and graduate vacancies. Students are encouraged to access this support at an early stage and to use the extensive resources on the Careers website.

Discussing options with specialist advisers helps to clarify plans through exploring options and refining skills of job-hunting. In most of our programmes there is direct input by Career Development Advisers into the curriculum or through specially arranged workshops.

Study support

We have a commitment to strong pastoral care for all of our students, which includes a Personal Tutor for all students, regular contact hours for tutor groups and our supportive student service teams who are always ready to help with any questions and provide the advice that you need.

In addition to standard study support through taught sessions, our Virtual Learning Environment allows students to access resources, participate in group work and submit work from anywhere in the world 24/7.

University central services are rich with support teams to assist students with every aspect of their journey through our degree programmes. From our Career and Employability Service, through our strong Students' Union, to our professional and efficient Student Finance team, there are always friendly faces ready to support you and provide you with the answers that you need.

Research

There is much research taking place at the Faculty of Engineering and Informatics related to this Master's programme. This includes aspects of applied computing, theoretical computer science, and communications and networks. Statistical analysis of data across various disciplines is a central theme of our research.

Teaching informed by research is at the core of this programme. Graduates leave us well prepared to pursue academic research, or industry based research and development positions.

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The Spatial Data Science and Visualisation MSc teaches cutting-edge data analysis, mining, modelling and visualisation techniques for spatial systems. Students carry out their own research project, supported by academics, researchers and other students in one of the most exciting, interdisciplinary research teams in the field. The programme takes place within The Bartlett, UCL's Faculty of the Built Environment.

About this degree

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

The programme consists of four core modules (60 credits), a group mini-project (30 credits), two elective modules (30 credits), and a dissertation (60 credits).

Core modules

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

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

Elective modules

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

Dissertation/report

All students submit a dissertation of 10-12,000 words.

Teaching and learning

The programme is delivered through a combination of lectures, seminars, tutorials and practical-based workshops and classes. The interlinked laboratory research-based mini project with data collection focuses on ‘remote data mining’ rather than fieldwork in the traditional planning/geographical/architectural sense. Assessment is through group and individual projects and the dissertation.

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

Funding

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

Careers

Recent graduates of our related Spatial Data Science and Visualisation MRes have gone on to work as developers, in spatial analysis, and a number have continued to PhDs. Through our PhD partners, Knowledge Transfer Partnerships and substantial outreach, graduates will be able to take advantage of CASA's links to the world outside academia.

Employability

The Spatial Data Science and Visualisation MSc provides a unique skill set in computation mapping, visualisation and spatial research. Research-led skills are increasingly a key element in our understanding of complex spatial functions, particularly as vast amounts of previously unused data are becoming available either from changes in accessibility regulation or more widely as a result of new mass data collection methodologies.

Why study this degree at UCL?

The Centre for Advanced Spatial Analysis (CASA) is a research centre specialising in computational and mathematical approaches, with cutting-edge research in GIS, urban simulation, mapping, data visualisation, and 3D environments in cities and space.

Students on this programme will be exposed to a range of programming languages (Java/Processing, R, Python and MySQL), 3D visualisation packages, and be given a substantive grounding in GIS, programming structure, mathematical methods and data design.

The combination of skills involved in this programme is unique – graduates will be able to lead institutions and companies in new directions and be involved in changing cultures across the sector.

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There has been a recent upsurge in commercial interest in the new role of "data scientist". A data scientist is a person who excels at manipulating and analysing data, particularly large data sets that don't fit easily into tabular structures (so-called "Big Data").

Why study Data Science at Dundee?

The School of Computing has been working on 'big data' and data analysis for at least five years; not only working with data but also developing new algorithms and techniques for data scientists. The School already runs the most successful Business Intelligence Masters course in the UK.

This course will be led by Professor Mark Whitehorn and Andy Cobley. Mark is an emeritus professor at the University of Dundee and also runs a successful consultancy company that specialises in BI, Data Sciences and analytics. Andy is the course organiser for both the existing BI course and the new Data Science course.

This course will enhance your employability by providing you with knowledge, skills and understanding of data science research and implementation. You will also acquire skills in the professional procedures necessary to ensure that data science research and implementation is both valid and actionable and engage with contemporary debate about the role, ethics and utility of data science in commercial and other settings.

What is the difference between Data Science and Business Intelligence?

There is clearly a huge overlap with Business Intelligence. A BI specialist will need to understand data and data analytics. However there is a bias towards understanding how data is stored in the current operational systems within an enterprise the design and the implementation of an analytical system such as a data warehouse. A data scientist will be less concerned with the construction of a data warehouse and more interested in the message the specific sets of data can deliver.

However, without some understanding of data warehouses the data scientist will find it difficult to interrogate the data for its secrets. For this reason there is overlap between the two courses.

If you already have a strong grounding in Business Intelligence and would like to upgrade your knowledge to include topics from the Data Science MSc, we offer the relevant Data Science modules either on a stand alone basis or as a PGCert.

What's so good about Data Science at Dundee?

Our facilities will give you 24-hour access to our award winning and purpose-built Queen Mother Building. It has an unusual mixture of lab space and breakout areas, with a range of conventional and special equipment for you to use. It's also easy to work on your own laptop as there is wireless access throughout the building. Our close ties to industry allows us access to facilities such as Windows Azure and Teradata, and university and industry standard software such as Tableau for you to evaluate and use.

A booming Postgraduate culture where the School of Computing maintains a friendly, intimate and supportive atmosphere, and we take pride in the fact that we know all of our students - you're far more than just a matriculation number to us. We have a thriving postgraduate department with regular seminars and guest speakers.

Duncan Ross (Director of Data Sciences at Teradata) has said that: "The first and most important trait is curiosity. Insane curiosity. In many walks of life evolution selects against the kind of person who decides to find out what happens 'if I push that button'. Data Science selects for it."

How you will be taught

The programme will be delivered by Prof. Mark Whitehorn with input from Andy Cobley, Yasmeen Ahmad, Chris Hillman and other specialists from within the School of Computing in an innovative blend of live co-presented master-classes, video seminars and recorded materials. A series of guest speakers from industry will provide case studies across both semesters.

The programme will be provided predominantly on-campus, with two intensive study weeks in each of the semesters. Other classes may be taken off-campus using the university’s VLE, remote desktop, Adobe Connect and video conferencing systems along with telephone conferencing.

What you will study

Semester 1
Big Data - 20 Credits
Business Intelligent Systems - 20 Credits
Data Analysis and Visualisation - 20 Credits

Semester 2
Analytical Database Models and Design - 20 Credits
Advanced statistics and data mining - 20 credits
MDX - 20 Credits

Semester 3
Data Science Mini Project - 20 credits (for Certificate)
Data Science Research Project - 60 credits

PGCert:
The PGCert is intended for students who have a strong grounding in Business Intelligence and would like to upgrade their knowledge to include topics from the Data Science MSc. The modules are available stand alone for those who want to take their time studying the material and perhaps build up to a PGCert.

The three modules that make up the PGCert are:
Big Data
Advanced Anlaysis
Mini Project

For more information about the content of the course, please visit the course webpage on the School of Computing website.

How you will be assessed

Assessment will be by examination, practical coursework and research project.

Careers

Various job sites now report an increase in jobs carrying the title of data scientist. Other career opportunities are in intelligence analysis, data management/database maintenance, data processing manager, database development and research, business intelligence consultant and more.

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Introduction

We offer a suite of Masters programmes at Stirling.
This is a one year, full time taught MSc. designed to lead to a job in data science or analytics.
Big Data skills are in high demand and they attract high salaries. The MSc Big Data at the University of Stirling is a taught advanced Master's degree covering the technology of Big Data and the science of data analytics.
The course is taught in the beautiful Stirling campus in the heart of Scotland with support from companies who recruit data scientists.
The course covers Big Data technology, advanced analytics and industrial and scientific applications. The syllabus includes:
- Mathematics for Big Data
- Python scripting
- Big Data theory and computing foundations
- Big databases and NoSQL
- Analytics, machine learning and data visualisation
- Optimisation and heuristics for big problems
- Hadoop and MapReduce
- Scientific and commercial applications
- Student projects

Key information

- Degree type: MSc
- Duration: One year
- Start date: September
- Course Director: Kevin Swingler

Course objectives

- An understanding of the issues of scalability of databases, data analysis, search and optimisation
- The ability to choose the right solution for a commercial task involving big data, including databases, architectures and cloud services
- An understanding of the analysis of big data including methods to visualise and automatically learn from vast quantities of data
- An appreciation of the size of search spaces in large problems and the ability to choose an appropriate heuristic to find a near optimal solution
- The programming skills to build simple solutions using big data technologies such as MapReduce and scripting for NoSQL, and the ability to write parallel algorithms for multi processor execution.

English language requirements

If English is not your first language you must have one of the following qualifications as evidence of your English language skills:
- IELTS: 6.0 with 5.5 minimum in each skill
- Cambridge Certificate of Proficiency in English (CPE): Grade C
- Cambridge Certificate of Advanced English (CAE): Grade C
- Pearson Test of English (Academic): 54 with 51 in each component
- IBT TOEFL: 80 with no subtest less than 17

For more information go to English language requirements https://www.stir.ac.uk/study-in-the-uk/entry-requirements/english/

If you don’t meet the required score you may be able to register for one of our pre-sessional English courses. To register you must hold a conditional offer for your course and have an IELTS score 0.5 or 1.0 below the required standard. View the range of pre-sessional courses http://www.intohigher.com/uk/en-gb/our-centres/into-university-of-stirling/studying/our-courses/course-list/pre-sessional-english.aspx .

Structure and content

Our Big Data MSc is a mix of practical technology such as Hadoop, NoSQL, and Map-Reduce, important maths and computing theory, and advanced computational techniques. The course will teach you what you need to know to collect, manage and analyse big, fast moving data for science or commerce

REF2014

In REF2014 Stirling was placed 6th in Scotland and 45th in the UK with almost three quarters of research activity rated either world-leading or internationally excellent.

Strengths

Stirling is a member of The Data Lab, which is an Innovation Centre with the aim of developing the data science talent and skills required by industry in Scotland. The data lab with facilitate industry involvement and collaboration and provide funding and resources for students.
The Stirling MSc in Big Data has been developed in partnership with global and local companies who employ data scientists. HSBC have a development centre in Stirling and have provided some very interesting Big Data projects to our students. Amazon’s development centre in Scotland is close by in Edinburgh. The course features a long summer project, generally in partnership with a company or technology provider, that provides students with a showcase of their skills to take to employers or launch online.
We also have a programme of invited speakers from industry who give the students a chance to ask questions of people who are doing data science every day. Recent companies have included MongoDB, SkyScanner and HSBC.

Career opportunities

Demand for people with big data skills is projected to grow rapidly in the coming years. Average salaries are higher in Big Data jobs than the IT average and the skills shortage will make that gap bigger.
The Stirling Big Data MSc is run in partnership with industry and is designed to produce graduates with the skills that companies need.
e-Skills UK estimate that:
- The number of Big Data jobs in the UK rose by 41% from 2012 - 2013
- By 2020 there will be 56,000 Big Data jobs in the UK alone
- Big Data professionals earn on average 31% more than other IT professionals
- 77% of companies say it is difficult to recruit people with the Big Data skill they need

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The Spatial Data Science and Visualisation MRes teaches cutting-edge data analysis, mining, modelling and visualisation techniques for spatial systems. Students carry out their own research project, supported by academics, researchers and students in one of the most exciting, interdisciplinary research teams in the field, within The Bartlett, UCL's Faculty of the Built Environment.

About this degree

Students gain a grounding in the principles and skills of spatial research, data analysis and visualisation and virtual environments, and develop an understanding of research methodology and methods of data collection and analysis. Subject-specific modules provide students with the opportunity to develop skills in spatial analysis and to contribute to current debates in the field.

The programme consists of four core modules (60 credits), a group mini-project (30 credits) and a research dissertation (90 credits).

Core modules

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

Optional modules

There are no optional modules on this programme.

Dissertation/report

All students submit a research dissertation of 10-12,000 words and 5,000-words in the form of a paper for publication.

Teaching and learning

The programme is delivered through a combination of lectures, seminars, tutorials and practical-based workshops and classes. The interlinked laboratory research-based mini project with data collection focuses on ‘remote data mining’ rather than fieldwork in the traditional planning/geographical/architectural sense. Assessment is through group and individual projects and the dissertation.

Further information on modules and degree structure is available on the department website: Spatial Data Science and Visualisation MRes

Funding

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

Careers

The Spatial Data Science and Visualisation MRes offers a unique skillset in computation mapping, visualisation and spatial research, with recent graduates working at Ordnance Survey and the BBC, as well as a number continuing to PhDs. Through our PhD partners, Knowledge Transfer Partnerships and substantial outreach, CASA is well-connected to the world outside academia.

Employability

Research-led skills are becoming increasingly key in shaping our understanding of complex spatial functions. Vast amounts of previously unused data are becoming available either from changes in accessibility, the nature of network and cloud-based computing, changing national data policies or more widely as a result of new mass data collection methodologies.

Why study this degree at UCL?

The Centre for Advanced Spatial Analysis (CASA) is a research centre specialising in computer-based methods such as GIS, urban simulation, mapping, data visualisation, and 3D environments in cities and space.

Graduates from our programme will have been exposed to a range of programming languages (Processing, R, Python and MySQL), 3D visualisation packages, and a substantive grounding in GIS, programming structure, mathematical methods and data design.

This combination of skills is unique – graduates from this programme will be leading institutions and companies in new directions and changing cultures across the sector.

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Course content

We have designed this MSc course in consultation with industry partners.

This has enabled us to understand their needs for Data Scientists, what skills will be required and on successful completion of this course individuals will be highly employable within businesses.

Having this close understanding of what industry needs makes this course relatively unique and the very best suited to these looking for a career in the Data Sciences. 

The course will be of specific interest to :

• A mathematics graduate wishing to use your skills in a vocational business based environment
• A computer science graduate wishing to follow a vocational route
• Individuals currently working in Business and looking to grow their career through gaining Data Science and Business Analytics skills

Six modules go to make up this MSc:

• Data Science Foundation
• Managing Data
• Data Exploration and Analysis
• Mathematics
• Machine Learning & Cognitive Computing
• Data Visualisation and Presentation

The 1 year full time MSc course will be stimulating and interactive, making use of lectures, self-learning, workshops and hands-on projects. 

You will be assigned a Personal Tutor from the start of your course who will work with you throughout your studies to help you achieve your academic best. 

The knowledge we provide you with in these areas will give you all of the essential know-how on methods, tools and techniques to deliver in your career as a Data Scientist.

We believe Data Science is very much an intellectual ‘contact sport’ and through this course we provide you with every opportunity to put your theoretical knowledge into practice.

The project work we have imbedded within the course has been chosen and developed based on real-world scenarios across a range of industry and government sectors and is specifically designed to:

• Provide an essential link between your theoretical learning and real-world challenges
• Create an environment where you decide the methods and tools best suited to the challenge based on what you have learnt
• Recreate some of the challenges facing industry and Government today and those very similar to what you will encounter in the workplace as a Data Scientist
• Be adaptable to reflect new methods / tools and scenarios in this fast developing discipline
• Be able upon completion of the projects to reference your experience in working with such challenges

Our facilities

You will undertake your workshops in training rooms that are bang up-to-date with design features, touch screen electronic white boards and high speed wifi; housed across three stunning Georgian mansions.

All of our current students love the learning environment, the culture, camaraderie and the fact that tutors know them by name so they are more than just a ‘face in the crowd’. 

You will have access to the very best IT facilities in order to support your studies. These range from computer labs to access to cloud analytics from the leading providers.

We will use software from the academic programs of the major enterprise I.T. vendors such as IBM and Amazon as well as commonly used open source programs and frameworks. 

From September 2018, many of the teaching sessions will take place in the purpose-built Engineering and Digital Technology building in the Bognor Regis campus.

What's more, you have lots of other facilities on this dedicated university campus including latest books, journals and online data in a truly modern library, an IT centre, a student zone complete with Costa Coffee, a gym and much more.

Where this can take you

The course has been designed to provide you with a very practical understanding of the issues associated with sourcing, curating, analysing and presenting data in business and other public sector and not-for-profit organisations. 

On completion of your MSc studies and successful graduation, you will have very transferable skills and can choose to move directly in to the workplace perhaps in retail, banking, government or transport.

Indicative modules

• Data Science Foundation (20 Credits)
• Managing Data (20 Credits)
• Data Exploration and Analysis (20 Credits)
• Mathematics (20 Credits)
• Machine Learning & Cognitive Computing (20 Credits)
• Data Visualisation and Presentation (20 Credits)
• Dissertation/Project (60 Credits)

Teaching and assessment

Our approach to supporting your learning, and how your learning is assessed, is designed to mirror the workplace environment. With this in mind, key features of our approach to learning and assessment include the following: 

• We place a lot of emphasis on course work related activity.
• Opportunities to work with organisations on current commercial/business problems and projects. These experiences are used to provide the basis for assessments that enable you to apply your learning within authentic commercial situations.

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