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.
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 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
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.
- 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
The Master of Science in Big Data Analytics for Business is a unique program that trains business professionals in the field of (online) marketing, finance, and operations.
Students are exposed to the leading-edge fundamentals in decision-making by extracting knowledge from Big Data, including social media data, customer web traffic data, Bloomberg’s financial data, and inventory process logs.
Students will learn to solve managerial problems by critically asking questions in the spirit of ‘What do we know?’ (Data driven) rather than ‘What do we think? (Gut feeling).
- Introduction of leading tools that convert data to knowledge
- Possibility to obtain business-relevant certificates
- Exposure to both academic and applied industry research
- Digital/Web Analyst
- Customer Analyst
- Data Scientist
- Credit Risk Analyst
This program is under the process of being accredited with the Université Catholique de Lille as diplôme universitaire and with the Conférence des Grandes Ecoles.
The Master of Science in Big Data Analytics for Business offers core modules in business, technology, and methodology as well as specialized modules in marketing, finance, and operations.
These modules will be covered over two semesters and the students will take their newfound knowledge and apply it in a professional environment during a 4 – 6 month internship.
Students acquire real-life experience through a 4 – 6 month internship in France, or anywhere in the world.
The objective is to provide an opportunity where MSc in Big Data Analytics for Business students learn how to approach assignments and working relationships in a professional environment. They can apply their newfound knowledge in real world situations while receiving guidance and feedback from managers and colleagues.
New contacts made during their internships help create their professional networks.
French language classes -
French language lessons are mandatory for non-Francophone international students. Francophone students may choose German, Italian, Chinese, or Spanish.
The MSc in Big Data and Analytics for Business is for students with a bachelor’s degree with a quantitative component or business administration interested in a new and expanding field.
Admission requirements -
The program is open to candidates with a bachelor’s degree from a recognized university with good academic performance and a good command of English.
Native English speakers or students who have had two years of courses taught in English are exempt. A GMAT score is optional, not mandatory.
No prior knowledge of French is needed; however French language classes are mandatory for non-French speakers as part of the program.
Application process -
The application process is based on students’ online application available at https://application.ieseg.fr/ and review of the required documents.
Rolling admission is offered from October 2017.
- Online application form
- Transcripts and diploma translated into English or French if necessary
- English proficiency test (IELTS 6.5 TOEFL IBT 85, TOEIC 800) if required
- CV / Resume
- Copy of passport
- 80€ application fee
- € 15,000 for domestic and international students
- International merit-based scholarships are available
Funding and scholarship-
IÉSEG has a merit-based International Scholarship Program with a tuition waiver of 15 to 50% per year. Selection is based on the applicant’s previous academic performance and overall application portfolio.
The scholarship application is automatic; students do not need to apply separately.
All international students are encouraged to check with Campus France and their own government to see if there are any scholarships available. For American students please check with Sallie Mae for private loan options.
IMF Business School, in collaboration with the Camilo José Cela University, launches the Master in Business Analytics and Big Data. This program aims to provide students with a global view of Big Data technologies and their use, as well as applied and practical training in Business Analytics. IMF is a member of the Association of Computer Technicians (ATI).
This Master's degree is aimed at both new graduates and experienced professionals who wish to focus on the new professions related to data analysis (Data Analyst, Data Scientists, Chief Data Officer, Data Engineer ...). The recommended access profiles are those related to ICTs, careers with a high qualitative component, and careers in business and economics.
The Master's Degree in Big Data of IMF, of an academic year of duration, is taught in online mode supported by an advanced technological platform that allows the student to access the study, regardless of geographical location or time availability.
With the IMF Student Centered methodology, the student is placed at the center of all training services and guides the institution towards academic and professional success. The student will be able to know his progress at all times, be attended when he needs to, access to all resources with total freedom and have a coaching service, headhunting and job placement.
All students who successfully complete this program will obtain a double Master's degree from the University Camilo José Cela and Master by MFI Business School. They will have at their disposal all the advantages of MFI:
Data Science is a rapidly developing field of study within both academia and industry. Its interdisciplinary nature ensures its wide application domain. This MSc Data Science aims to prepare students for a successful career as a data scientist or business analyst working in any profession where large amounts of data is collected, hence there is a need for skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation. These type of skills are typically in high demand in IT business, security and health sectors, intelligent transport, energy efficiency and the creative industries.
More generally data and analytics capabilities have developed rapidly in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. Most companies, however, are not capturing the full potential value from data and analytics because they do not have the required expertise. Consequently, the MSc Data Science aims to address these challenges by providing a firm grounding in the core disciplines of data analytics and information processing, partnered with a broad appreciation of aspects of other disciplines where data science can form natural synergistic relationships.
Ulster University academics are actively involved in both research and teaching and this ensures that the developments accrued through research can feed into the teaching of students. A high percentage of staff are members of the Higher Education Academy, and all staff are expected to have a Postgraduate Certificate in University Teaching or equivalent. All Computing courses are subject to periodic Faculty Review and University Revalidation.
The key message from employability and work-related learning initiatives is that enhancing opportunities to develop work-related learning and employability enhances the learning of the subject being studied. We understand the importance of including real industrial and commercial contexts to our student's experience, so this MSc Data Science will pursue opportunities for industrially linked teaching material and student project work. In this regard, we will utilise our business and industry links to facilitate an industrially relevant student project. Such projects create valuable experiences for the student, and additionally, they can also help to build new and ongoing collaborations with departments and companies, with the potential to tap into funding streams designed for industry-academic research and development.
A recent statement from Ulster University’s Careers Office indicates that Data analysts are in high demand across all sectors, such as finance, consulting, manufacturing, pharmaceuticals, government and education. Data analysts can work in large companies such as the ‘big four’ consultancies or financial services firms, or consumer retail firms, small and medium sized businesses such as marketing agencies’ or the public sector.
Do you enjoy problem solving? Are you looking for a demanding career with a salary to match? Southampton Solent University’s MSc Data Analytics Engineering programme teaches students to make sense of a world where every action and transaction we perform has some aspect of data attached to it. Data analysts use these data sets to make meaningful inferences that can support business decisions, governmental policy changes and system designs.
As a conversion course, this master’s degree is well suited to students from a wide range of academic backgrounds. The course will help you to develop sought-after skills within the technology and big data environment, fully preparing you for a range of careers after graduation.
The one-year master’s level conversion course is designed to prepare students from a range of academic backgrounds for work in data analytics engineering. Students are also able to tailor the course to their own personal career ambitions through a research project. Many use this piece of work to springboard the start of their career or a further research study.
Topic covered include databases, data management, web technologies, analysis and computing fundamentals. Students will also study academic research methods, which will then inform their final research project.
Students are also supported to gain a range of transferable skills throughout the course. These include project management, critical thinking, organisation and presentation skills. The professional issues and practise unit helps prepare students for the workplace by looking at the wider computing industry and the contexts in which big data can be used most effectively.
Graduates from this course would be well-suited for a range of data analytics and data systems design roles. If you are interested in research, the course offers opportunities to continue on to PhD study.
This conversion master’s course is ideally suited to students from a number of academic backgrounds who have a strong interest in learning to code and transform data.
The course is also suited to those with extensive industry experience in this area, and who wish to gain an academic qualification.
Core units with CATS points:
Students have access to high-spec computer labs and make use of the latest design and development programs.
Students will test applications in our new device laboratory - a special test area integrated within one of our existing software development spaces. This arrangement allows you to test your website designs and apps on real equipment, ensuring they perform as expected on the target platforms.
Suitable roles for graduates include:
Course content is developed with input from a variety of sources including an industrial liaison panel. This ensures that your studies include the latest technologies and working practices.
You’ll also have the chance to work directly with real-world companies on live briefs, events and projects, while regular BCS meetings hosted at the University help you to build professional connections and secure valuable work experience opportunities.
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.
The programme combines training in core statistical and machine learning methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical computing and modelling. Students choosing the statistics specialisation will take one compulsory module and up to two additional modules in computer science, with the remaining modules (including the research project) taken mainly from within UCL Statistical Science.
Students undertake modules to the value of 180 credits.
The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research dissertation/report (60 credits).
At least two from a choice of Statistical Science modules including:
Up to two from a choice of Computer Science modules including:
All students undertake an independent research project, culminating in a dissertation usually of 10,000-12,000 words. Workshops running during the teaching terms provide preparation for this project and cover the communication of statistics.
Teaching and learning
The programme is delivered through a combination of lectures, tutorials and classes, some of which are dedicated to practical work. Assessment is through written examination and coursework. The research project is assessed through the dissertation and a 15-minute presentation.
Further information on modules and degree structure is available on the department website: Data Science MSc
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:
Data science professionals are likely to be increasingly sought after as the integration of statistical and computational analytical tools becomes essential in all kinds of organisations and enterprises. A thorough understanding of the fundamentals is to be expected from the best practitioners. For instance, in applications in marketing, the healthcare industry and banking, computational skills should should be accompanied by statistical expertise at graduate level. Data scientists need a broad background knowledge so that they will be able to adapt to rapidly evolving challenges. Recent graduates from the related Statistics MSc have been offered positions as research analysts or consultants, and job opportunities in these areas are increasing.
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.
The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.
The following REF score was awarded to the department: Statistical Science
82% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)
Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.
Data is becoming an ever increasing part of modern life, yet the talent to extract information and value from complex data is scarce. This Masters will provide you with a thorough grounding in state-of-the art methods for learning from data, both in terms of statistical modelling and computation. You will also gain practical hands-on experience in carrying out various data-driven analytical projects. Previous study of Statistics or Computing Science is not required.
One Course is optional for students with sufficient background in Linear Algebra and Calculus.
Two students who have already completed an equivalent course can substitute this course by any other optional course, including optional courses offered as part of the MRes in Advanced Statistics (see the website for details).
In your project (60 credits) you will model data collected from research in environmental science, assessed by a dissertation.
Students choose at least two courses from group 1 and at least one course from group 2.
In your project (60 credits) you will tackle a complex data analytical problem or develop novel approaches to solving data analytical challenges.
There is a massive shortage of data-analytical skills in the workforce. Statistician is projected to be one of the fastest-growing occupations. There is a massive shortage of data-analytical skills in the workforce. Statistician is projected to be one of the fastest-growing occupations. Statistical Analysis and Data Mining was listed by LinkedIn as the hottest skill in 2014 and came second in 2015.
Our graduates have an excellent track record of gaining employment in many sectors including medical research, the pharmaceutical industry, finance and government statistical services, while others have continued to a PhD. Our recent graduates have taken up positions as Statisticians with the Scottish Government, as Advanced Analytics Analyst at Deloitte Ireland, as Consultant at the World Bank and as Research Officer at Kenya Medical Research Institute (KEMRI).
Understanding data is becoming increasingly important for us all. This is especially true for the intelligence analyst working for a police intelligence unit or business analytics department. The MSc Crime Intelligence and Data Analytics (with Advanced Practice) course helps you develop the necessary skills to work in these sectors.
The work boundaries of the traditional police intelligence analyst and digital forensic investigator are becoming blurred – today’s analysts need to be cyber aware, understanding how communication records and web search histories can be extracted and analysed.
This course covers these areas as well as theories that provide a better sense of the causes of crime and the prevention measures that can be put in place to stabilise and reverse these trends. Analysts shouldn’t be phased by data simply because of its size, complexity or format. This course provides you with the skills to work effectively with large datasets, allowing you to make more informed decisions in relation to criminal investigations. Key features include writing code to quickly clean up data and packaging it so it’s suitable for analysis and visualisation. You will discover that the world constantly presents data in data frames or spreadsheets – our daily activities are invariably logged by a time, date, geolocation. You develop these skills along with your confidence in applying them to make more sense of the data – analysing Twitter downloads, searched words and images, geolocation points or big data. This course also explores strategies employed in forensic investigation. It gives you the space and opportunity to develop your own area of interest in a 60-credit research project where your supervisor enables you to maximise your skillsets from academic writing to data analytics.The two-year MSc Crime Intelligence and Data Analytics (with Advanced Practice) is an opportunity to enhance your qualification by spending one year completing an internship, research or study abroad experience. Although we can’t guarantee an internship, we can provide you with practical support and advice on how to find and secure your own internship position. A vocational internship is a great way to gain work experience and give your CV a competitive edge. Alternatively, a research internship develops your research and academic skills as you work as part of a research team in an academic setting – ideal if you are interested in a career in research or academia. A third option is to study abroad in an academic exchange with one of our partner universities. This option does incur additional costs such as travel and accommodation. You must also take responsibility for ensuring you have the appropriate visa to study outside the UK, where relevant.
For the MSc award you must successfully complete 120 credits of taught modules and a 60-credit master's research project.
Advanced Practice options
Modules offered may vary.
How you learn
You learn through a range of lectures, seminars, tutorials and IT laboratories, using a variety of software. Simulated problems and scenarios are posed in much the same way that analysts would face in the real world. You have the opportunity to use software that is found in real-world intelligence analysis and digital forensic units and data science. Engaging and learning from your peers will help you to achieve solutions. Much of the software you use in class can be downloaded for home use.
How you are assessed
You are assessed through a formal exam as well as through structured coursework.
You can expect to apply for an intelligence researcher and intelligence analyst role in a wide variety of career opportunities ranging from security, policing and business.
The digital revolution has led to an unprecedented volume of information about consumers, which progressive organisations are eager to understand and use. This innovative masters degree will give you the practical skills to analyse consumer data and provide insights for successful marketing strategies.
Taught by leading academics from Leeds University Business School and School of Geography, you’ll explore a range of analytical techniques including applied Geographic Information Systems (GIS) and retail modelling, consumer and predictive analytics and data visualisation. You’ll also develop the softer skills to use the results of these analyses to inform decisions about marketing strategy.
Thanks to our connections with businesses worldwide, you’ll have access to emerging trends in topics such as consumer behaviour, decision science and digital and interactive marketing. You’ll further develop your practical skills with the opportunity to work on a live data project provided by a company.
This courseoffers you a rare combination of teaching expertise; the Business School’s academic excellence in Marketing alongside world-class teaching from the School of Geography, which draws on the knowledge of the Centre for Spatial Analysis and Policy.
The University of Leeds is a major centre for big data analytics and you’ll benefit from affiliation with the UK’s Consumer Data Research Centre. The centre aims to make data that are routinely collected by businesses and organisations accessible for academic purposes. Coordinating and analysing this large and complex data has the potential to increase productivity and innovation in business, as well as to inform public policy and drive development.
Read an interview with the academic team to learn more about our expertise and the growing importance of this emerging subject area.
Core modules will introduce you to a range of analytical methods, ensuring you develop a solid foundation in the essential skills for consumer analytics and marketing strategy.
You’ll learn how to analyse geographic data using GIS software and understand the application of this in retail modelling, to evaluate new markets and locations. You’ll study predictive analytics, big data and consumer analytics, business analytics and decision science, and learn how to communicate results through data visualisations.
Alongside this, you’ll learn how to deploy data to inform decisions about marketing strategy. Marketing modules include marketing strategy, consumer behavior and direct, digital and interactive marketing. You’ll also deliver your own data-driven marketing research project for a company.
Optional modules allow you to further your knowledge in a related area of interest, either corporate social responsibility, internal communications and managing change, or applied population and demographic analysis.
By the end of the course, you’ll submit an independent project. You can either research a topic in-depth and submit a dissertation, or gain practical experience through a consultancy project working with an external organisation.
You’ll take the nine compulsory modules below, plus your dissertation, which can be a choice of either a research dissertation or marketing consultancy project.
You'll take one further optional module.
We use a range of teaching methods so you can benefit from the expertise of our academics, including lectures, workshops, seminars, simulations and tutorials. Company case studies provide an opportunity to put your learning into practice.
Independent study is also vital for this course, allowing you to prepare for taught classes and sharpen your own research and critical skills.
Assessment methods emphasise not just knowledge, but essential skills development too. You’ll be assessed using a range of techniques including exams, group projects, written assignments and essays, in-course assessment, group and individual presentations and reports.
As a graduate of this course you will be equipped with advanced skills in consumer analytics and marketing strategy, ideal for those wishing to pursue a career in consumer data analytics, marketing and/or management.
Due to the digital revolution, companies from around the world and in many industrial sectors have access to greater amounts of data.
The most progressive companies in the world are particularly interested in marketing graduates with strong analytical skills, and typical roles could include marketing or consumer data analyst, direct marketing manager, marketing manager, retail manager, or marketing or management consultant.
As a masters student you will be able to access careers and professional development support, which will help you develop key skills including networking and negotiating, and put you in touch with potential employers.
Our dedicated Professional Development Tutor provides tailored academic and careers support to marketing students. They work in partnership with our academics to help you translate theory into practice and develop your interpersonal and professional business skills.
You can expect support and guidance on career choices, help in identifying and applying for jobs, as well as one-to-one coaching on interpersonal and communication skills.
Read more about careers support at the Business School.