Big data is the description used to encompass the huge amounts of data that is common to many businesses. It has been described as the next frontier for innovation, competition and productivity in business. It is essential for companies to embrace so that they can understand their customers better, develop new products and cut operational costs.
This course has been developed to create graduates who can become data scientists capable of working with the massive amounts of data now common to many businesses. It is aimed at people who want to move into this rapidly expanding and exciting area.
The modules on this course help you develop the core skills and expertise needed by the data scientist. The course can be split into three main areas, statistics, computing and management.
In the statistics section you study modules on data mining and data modelling. These modules cover the three main data areas, which are ensuring that data is reliable and of a high quality, searching the data to discover new information and presenting interpretations of that data to the end user.
The computing section covers areas related to data integration, massive datasets stored in the cloud, how data is stored and utilised within the distributed systems of an enterprise and how organisations can utilise data to change and improve business processes.
The management modules are focused on developing your core skills around professionalism and research. All of which are valuable skills during your university studies and in your career.
Our partnerships with business inform the course design, ensuring the content is relevant, up to date and meets the needs of industry. These partnerships also enable the inclusion of some leading edge software such as SAS, SAP Hana, and Hadroop within the course. You may be able to study abroad as part of the Erasmus programme.
Key areas of study
Key areas of study include • data quality and analysis • technologies to store and mine data • professionalism and research
This course includes the SAP Business Intelligence with SAP BW 7.3 and SAP BI 4.0 e-academy (UB130e). You also have the opportunity to sit the SAP certification exam and the SAS 9 base certification exam.
Full time – September start – typically 12 or 18 months
Part time – September start – typically 36 months
Choose one from :
Many jobs for data scientists, data analysts and data mining analysts are available with salaries ranging from £35,000 to £80,000.
Jobs typically list the skills to be in areas such as statistical analysis and machine learning techniques, database and programming technologies, and expertise in statistical theory, which are all areas you cover on this course.
You also gain skills and knowledge in HaDoop, MapReduce, Java, SAS, MSQL which are some of the common technologies used in data scientist roles.
Data Science brings together computational and statistical skills and machine learning for data-driven problem solving. This rapidly expanding area includes deep learning, large-scale data analysis and has applications in e-commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence.
The programme comprises core machine learning methodology and an introduction to statistical science, combined with a set of more specialised and advanced options covering computing and statistical modelling. Projects are offered both within UCL Computer Science and from a range of industry partners.
Students undertake modules to the value of 180 credits.
The programme consists of three compulsory modules (45 credits), four optional modules (75 credits) and a dissertation/report (60 credits).
Students must choose 30 credits from Group One options. For the remaining 45 credits, students may choose up to 30 credits from Group Two options or up to 45 credits from Electives.
Group One Options (30 credits)
Group Two Options (up to 30 credits)
Electives (up to 45 credits)
Please note: the availability and delivery of modules may vary, based on your selected options.
A list of acceptable elective modules is available on the Departmental page.
All students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words.
Teaching and learning
The programme is delivered though a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed through a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.
Further information on modules and degree structure is available on the department website: Data Science and Machine Learning MSc
Data science professionals are increasingly sought after as the integration of statistical and computational analytical tools becomes more essential to organisations. This is a very new degree and information on graduate destinations is not currently available. However, MSc graduates from across the department frequently find roles with major tech and finance companies including:
Students gain a thorough understanding of the fundamentals required from the best practitioners, and the programme's broad base enables data scientists to adapt to rapidly evolving goals.
UCL received the highest percentage (96%) for quality of research in Computer Science and Informatics in the UK's most recent Research Excellence Framework (REF2014).
UCL Computer Science staff have research interests ranging from foundational machine learning and large-scale data analysis to commercial aspect of business intelligence. Our extensive links to companies provide students with opportunities to carry out the research project with an industry partner.
The department also enjoys strong collaborative relationships across UCL; exposure to interdisciplinary research spanning UCL Computer Science and UCL Statistical Science will provide students with a broad perspective of the field. UCL is home to regular machine learning masterclasses and big data seminars.
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: Computer Science
96% 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.
Almost every communication or interaction that takes place in the world today involves a digital interface, whether this is a computer, a laptop, a mobile phone, a smartcard, a camera or a sensor. All of the information form these myriad of these interactions is stored as data. All of this data can be mined to make better decisions, to make better systems, to do better research.
Recent advances in computational power, machine intelligence and the massive growth of sources of data has led to the development of a new area study: Data Science.
We are no longer looking at data about machine parts or airlines, or stocks and shares; we are looking at data about people and the word they inhabit. Jake Porway (Executive Director of DataKind) says: “A data scientist is a rare hybrid, a computer scientist with the programming abilities to build software to scrape, combine, and manage data from a variety of sources and a statistician who knows how to derive insights from the information within. S/he combines the skills to create new prototypes with the creativity and thoroughness to ask and answer the deepest questions about the data and what secrets it”. This programme is designed for such people.
This Data Science (DS) MSc programme is the evolution of the MSc Advanced Computer Science and is built around the strong skill base of experts in the Mathematics and Computer Science department. The programme has been built illustrate how new technologies, cutting edge research and novel scientific perspectives can be used together to influence future society in significant and fundamental ways.
Demand is growing for high value data specialists across the sciences, medicine, arts and humanities. The aim of this unique, modular, online distance learning programme is to enhance existing career paths with an additional dimension in data science.
The programme is designed to fully equip tomorrow’s data professionals, offering different entry points into the world of data science – across the sciences, medicine, arts and humanities.
Students will develop a strong knowledge foundation of specific disciplines as well as direction in technology, concentrating on the practical application of data research in the real world.
You can study to an MSc, Postgraduate Diploma, Postgraduate Certificate or Postgraduate Professional Development level.
Our online learning technology is fully interactive, award-winning and enables you to communicate with our highly qualified teaching staff from the comfort of your own home or workplace.
Our online students not only have access to the University of Edinburgh’s excellent resources, but also become part of a supportive online community, bringing together students and tutors from around the world.
You can study to an MSc, MSc with Medical Informatics specialism, Postgraduate Diploma, Postgraduate Certificate or Postgraduate Professional Development level.
These credits will be recognised in their own right for postgraduate level credits or may be put towards gaining a higher award such as a PgCert.
The modular course structure offers broad engagement at different career stages. Individual courses provide an understanding of modern data-intensive approaches while the programme provides the knowledge base to develop a career that majors in data science in an applied domain.
This programme is intended for professionals wishing to develop an awareness of applications and implications of data intensive systems. Our aim is to enhance existing career paths with an additional dimension in data science, through new technological skills and/or better ability to engage with data in target domains of application.
This programme provides practical, career-orientated training in social science research methods, including research design, data collection and data analysis relating to both qualitative and quantitative modes of inquiry.
Students will have the opportunity to specialise in particular methodologies and to learn more about the application of these methodologies to illuminate important issues and debates in contemporary society.
The programme is designed to provide a fundamental grounding in both quantitative and qualitative research skills, along with the opportunity to specialise in more advanced training in quantitative research, qualitative research or in practical applications of research techniques.
This module offers an introduction to the different styles of social science research as well as guidance and illustrations of how to operationalize research questions and assess them empirically. Students will be shown how to conduct systematic literature searches and how to manage empirical research projects. The module will also explore issues around the ethics of social science research as well as the connection between social science research and policy concerns. It is designed as preparation for undertaking postgraduate research and dissertation work.
This module aims to deepen students' understanding of key debates in social theory and research, providing advanced level teaching for those building upon basic knowledge and undertaking postgraduate research. It is designed to demonstrate and explore how social theory is utilised, critiqued and developed through the pursuit of social science research.
The purpose of this module is to illuminate the theoretical underpinnings of qualitative research. The module will discuss the impact of various theories on the nature and conduct of qualitative research particularly around questions of epistemology and ontology. The role of different types of interviewing in qualitative research will be utilised in order to explore the relationship between theory and methods.
The aim of the module is to provide a comprehensive overview of the theory and practice of measurement and constructing quantitative data in the social sciences. Through lectures and practical exercises, this module will provide students with relevant knowledge of secondary data sources and large datasets, their respective uses and usefulness, and their relevance for the study of contemporary social issues
The module will provide students with an overview of different approaches to qualitative data analysis. It will include introductory training to this skill that includes such techniques as thematic analysis and discourse analysis, as well as computer assisted qualitative data analysis. It will provide the knowledge necessary for the informed use of the qualitative data analysis software package NVivo. The module gives students a base level introduction to the analytical and technical skills in qualitative data analysis appropriate to the production of a Master's dissertation and/or use of CAQDAS software for social science research purposes.
This module provides an introduction to the basics of quantitative data analysis. The module will begin with a brief review of basic univariate and bivariate statistical procedures as well as cover data manipulation techniques. The module is taught through a series of seminars and practical workshops. These two strands are interwoven within each teaching session. Please note that students may be granted an exemption from this module if they have already successfully completed a module that has the equivalent learning outcomes.
This module advances students' confidence and knowledge in the use of SPSS. The module focuses on multivariate regression models, including the appropriate use and awareness of statistical assumptions underlying regression and the testing and refinement of such models.
A dissertation of no more than 15,000 words on a topic relevant to social science research methods training. The thesis will involve either carrying out and reporting on a small social science research project which includes a full and considered description and discussion of the research methods employed or the discussion of a research issue or technique to a level appropriate for publication.
We offer a range of advanced modules in quantitative and qualitative research methods, for example, logistic regression, internet-based research and visual research methods. We also provide specialist modules which reflect the teaching team’s diverse research interests, from the social logic of emotional life to conflict and change in divided societies. Optional modules generally run during the Spring semester and are offered subject to sufficient student demand and staff availability. Students will be able to choose a maximum of three to four option modules (depending on whether they need to complete Quantitative Data Analysis: Foundational). Please note that it is unlikely that all the following modules will be available for 2017/8. Please check with the Programme Director for queries about specific modules.
The MSc Data Analytics for Business is developed with industry, this course will give you the analytical and technical skills needed to maximise the big data revolution. You will learn key management techniques and how to apply these to improve practice.
This brand new course will engage you with the latest sector thinking. You will have access to our breadth of knowledge across the School of Science and Technology, and Nottingham Business School - giving you an exciting opportunity to enhance your skills and career progression.
The majority of this course is online learning with 3-day study blocks once every 12 weeks (running Thrusday-Saturday) where you would attend on campus and meet fellow colleagues. These dates will be scheduled in advance to help you to schedule your time accordingly.
The work-based project will provide you with a three to six-month project directly related and relevant to your business or company objectives. Working with the academic university team and in consultation with your employer, you will develop a unique project based on your workplace and academic study.
Visit us on campus throughout the year, find and register for our next open event on http://www.ntu.ac.uk/pgevents.
The MSc Data Science has six core taught modules divided into two streams. At the beginning of each stream you will be taught the fundamentals of applied statistics, computing technology, programming and data base systems. Your existing analytical and technical skills will be developed to understand how to use the industry software used in the workplace, in turn preparing you for your individual research project.
Alongside these modules, you will complete an individual research project. Your project will be proposed and supported by local employers across a range of industries, including ONS Data Science Camps.
The MSc Data Science course is delivered through a series of lectures, practical classes and workshops where you will have the opportunity to put into practice what you have learnt via hands-on exercises and design projects. You will also be taught by a number of guest lecturers and have the option to visit workplaces.
The Data Science Masters offers a flexible approach to learning, allowing you to study full-time, part-time or through continuing professional development (CPD) for working professionals. The CPD route is an accessible pathway for employers to equip staff with further training opportunities to work towards a postgraduate qualification.
Full-time students will typically spend 12 hours in classes each week. For those studying part-time, this is reduced to six hours each week.
You will be taught by active researchers and leading professionals exposing you to current real-world problems, methodologies, and industry-standard techniques and software.
Several modules are assessed entirely through coursework and some involve coursework and in-class examinations.