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
Looking for high-quality training in statistics for research or for professional life? If so, consider KU Leuven's MSc in Statistics, an interdisciplinary programme whose teaching is grounded in internationally-recognised research. Choose from a number of approaches: biometrics, social behavioural and educational statistics, business statistics, industrial statistics, general statistical methodology, or an all-round statistics profile.
This master’s programme is offered by the Leuven Statistics Research Centre (LStat) of KU Leuven. It is accredited by the Royal Statistical Society. You’ll be trained intensively in both the theoretical and practical aspects of statistics. The programme will also help you develop a problem-solving attitude and teach you how to apply statistical methodology.
This is an initial Master's programme and can be followed on a full-time or part-time basis.
The 120-ECTS programme consists of a common core curriculum (one semester), option-specific courses (one semester), elective courses (one semester), and a master’s thesis.
To tailor the programme to your needs and interests, you choose one of the following options:
LStat hosts international experts and is a stimulating environment for multidisciplinary statistical research. LStat is a privileged meeting space for statistics researchers from a range of domains:
The master of Statistics:
KNOWLEDGE AND INSIGHT
APPLYING KNOWLEDGE AND INSIGHT
DEVELOPING AN OPINION
DEPENDING ON THE CHOSEN OPTION, THE STUDENT MASTERS FOLLOWING ADDITIONAL LEARNING OUTCOMES:
As statistician, you'll be recruited by industry, banks or government institutions. You may find yourself designing clinical trials and supporting the biomedical sector, coaching research for new medicines, setting up and analysing psychological tests and surveys, performing financial risk analyses, statistically managing R&D projects and quality controls, or developing statistical software. And don't forget the academic world. The applications of statistics are very diverse, just like your professional options.
In this digital and data-rich era the demand for statistics graduates from industry, the public sector and academia is high, yet the pool of such graduates is small. The recent growth of data science has increased the awareness of the importance of statistics, with the analysis of data and interpretation of the results firmly embedded within this newly recognised field.
This programme is designed to train the next generation of statisticians with a focus on the newly recognised field of data science. The syllabus combines rigorous statistical theory with wider hands-on practical experience of applying statistical models to data. In particular the programme includes:
Graduates will be in high demand. It is anticipated that the majority of students will be employed as statisticians within private and public institutions providing statistical advice/consultancy.
To be awarded the MSc degree you need to obtain a total of 180 credits. All students take courses during semester 1 and 2 to the value of 120 credits which will be a combination of compulsory and optional courses. Successful performance in these courses (assessed via coursework or examinations or both) permits you to start work on your dissertation (60 credits) for the award of the MSc degree. The dissertation will generally take the form of two consultancy-style case projects or an externally supervised project.
The set of courses available is subject to review in order to maintain a modern and relevant MSc programme.
Previous compulsory courses for 2016-17:
Previous optional courses for 2016-17 include:
At the end of this programme you will have:
Trained statisticians are in high demand both in public and private institutions. This programme will provide graduates with the necessary statistical skills, able to handle and analyse different forms of data, interpret the results and effectively communicate the conclusions obtained.
Graduates will have a deep knowledge of the underlying statistical principles coupled with practical experience of implementing the statistical techniques using standard software across a range of application areas, ensuring they are ideally placed for a range of different job opportunities.
The degree is also excellent preparation for further study in statistics or data science.