Key information
Duration: 1 year full time
Institution code: R72
Campus: Egham
UK fees*: £13,200
International/EU fees**: £28,500
The course
Applied Data Science (MSc)
MSc Applied Data Science is a new masters designed for those wanting to learn the skills required to gain insights from data.
The conversion course enables students with little prior exposure to computing to develop expertise and valuable skills that will prepare you for a career in the growing data science industry where there is significant demand for skilled personnel, both in the UK and internationally. (Students with an interest in cyber security may also consider our combined MSc Applied Data Science and Cyber Security). The importance of data science grows year on year, with sectors including healthcare, manufacturing, retail, finance and others reliant on the insights that accurate data capture and analysis can provide.
The course introduces you to a variety of languages essential to data science, including Python, and how to use different software packages such as scikit-learn. As you develop your understanding of how a wide variety of data science methods work, including clustering, regression, decision trees, and neural networks, you will learn how to apply them through hands-on examples from real-world settings.
Throughout your studies you’ll acquire the skills to work with large datasets, interpret and communicate results in the presence of bias and uncertainty, and build your range of soft skills for making presentations and writing.
You will gain the technical expertise to work with data and design and implement data analysis to find smart solutions for real-world problems. You will also explore the multitude of ethical, social, and political issues, the implication of artificial intelligence and advanced computing, and acquire the skills to analyse and critically evaluate ethical issues.
You will be taught by world experts in one of the top Computer Science departments in the UK, ranked among the top 25 UK Computer Science Department (The Complete University Guide, 2024). Our teachers are specialists in their field and much of our curriculum and research is informed by and closely linked with industry. You will learn about the most recent industrial developments in data science, machine learning and cyber security, with guest speakers from our extensive network of industry contacts, including our former MSc graduates.
You’ll graduate with capable skills to tackle complex problems, extract insights from data, uncover otherwise-hidden information, and use it to make informed decisions, giving you a competitive edge to pursue a successful career.
The Department of Computer Science at Royal Holloway has a rich history in the development of machine learning with members of the Centre for Reliable Machine Learning (set up in 1998 as the Computer Learning Research Centre) making key contributions to the development of support vector machines, conformal predictors, reinforcement learning and other important machine learning and artificial intelligence methods.
Course structure
Core Modules
Year 1
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The module will focus on programming problems and assignments designed to teach students algorithmic thinking and problem solving and covering programming concepts such as conditional statements, loops, and arrays. The students will use standard libraries to manipulate data and apply machine learning algorithms for regression, classification, and clustering and learn to interpret the results.
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In this module, the students will acquire proficiency in advanced mathematical concepts, including linear algebra and probability. The students will master vector and matrix operations and terminology as well as concepts in probability theory including random events, variables, their description in terms of density and distribution functions and conditional probability. The students will practice the concepts in lab sessions and perform calculations using a numerical package.
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This module precedes the dissertation and is aimed at allowing you to develop a plan for your research, resulting in a detailed proposal. As such, work done on the module contributes towards the first three chapters of your Dissertation. In this module you will acquire an understanding of the research context. You will learn how to develop feasible research objectives and an appropriate conceptual/analytical framework for your research. You will learn how to identify and critically review appropriate literature, and how to make informed decisions about which research philosophies, strategies and methods are suitable for your research. The subjects of triangulation, reliability, validity and research ethics will be explored, with the aim that you learn how to select a combination of methods that form a critically robust research design, such that you can apply this in your dissertation module.
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The aim of this module is to practice the use of Data Science by working through a series of case studies. The case studies will be based on real- life problems and will start with a description of the setting of the problem and the intended outcomes. Analyses will start with raw data that will have to be sense-checked and manipulated into a form that is suitable for the intended analyses. Deciding on the exact form of the analyses in each case will be a central focus of this module and an important aim of this module will be developing the skills to make decisions in this regard, drawing on information from the setting, the exact nature of the problem being assessed and knowledge of the techniques and methods that are available. In each case study, the results of the chosen form of analyses will be interpreted, with particular attention given to the best way of communicating the results to a variety of technical and non-technical audiences. Activities will include problem formulation, knowledge discovery, the application of statistical and machine learning techniques, report writing and presentation. Assessment will be based on practical examples using real-world data examples.
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The module aims to teach the science and arts of statistical visualisation and exploratory analysis of data. There are principles, theory, and skills to be acquired. The module covers construction of informative bivariate plots, visualisation of distributions (histograms, binning, and kernel density estimation; cumulative distributions and QQ plots), visualisation of multivariate data, dimensionality reduction, linear projections and principal components analysis, statistical methods to understand, interpret, and communicate insights from data.
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This course is designed to enhance your awareness of the many ethical implications of working with advanced technology. The course recognises that the ethical issues in computing and AI come to the forefront through developments in technology, bringing new responsibility for novel ethical, social, and legal implications of technology almost on a daily basis.
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The individual project is by far the most important single piece of work in the MSc programme. It provides the opportunity for students to demonstrate independence and originality, to plan and organise a large project over a long period, and to put into practice some of the techniques they have been taught throughout the course. The content of each project is individual. The student selects preferred project topics from the provided list or proposes their own project topic. Each student is allocated a supervisor who is the main point of contact for the duration of project work. The project leads to the production of the dissertation.
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This module will describe the key principles of academic integrity, focusing on university assignments. Plagiarism, collusion and commissioning will be described as activities that undermine academic integrity, and the possible consequences of engaging in such activities will be described. Activities, with feedback, will provide you with opportunities to reflect and develop your understanding of academic integrity principles.
Teaching & assessment
The course is structured into six taught modules and an individual project leading to the production of a dissertation.
You will learn through a variety of teaching methods on the course includes lectures, lab sessions, and small group sessions.
Typical module assessment will involve a combination of an examination in an invigilated setting, together with coursework and assignments completed throughout the term. Smaller assignments provide hands-on experience with real-world data and enable you to build your confidence throughout your studies. Modules building soft skills are assessed with presentations and essays.
Hands-on experience is an essential part of the training of a specialist in all areas of computing, data science and cyber security. You will be expected to do substantial practice outside of the classroom, following up on examples from the lectures, working towards coursework assignments, and performing background research for their dissertations.
Each student is assigned a personal tutor, who oversees your academic progress and helps with the development of soft skills as part of the Ethics module. Your tutor is available for advice throughout the year. For the individual project at the end of the course, you will be assigned a supervisor who is an expert (and often a practicing researcher) in the allocated topic.
Entry requirements
STEM subjects: Accounting, Aeronautical and Aerospace Engineering, Architecture, Artificial Intelligence, Astronomy, Biological Sciences, Biomedical Sciences, Chemical Engineering, Chemistry, Civil Engineering, Computer Science, Economics, Electrical and Electronic Engineering, Finance, Information Technology, Mathematics, Mechanical Engineering, Medicine, Natural Sciences, Paramedic Science, Pharmacology and Pharmacy, Physics, Psychology, Statistics, Web Development.
2:2 or equivalent - we welcome students from a wide variety of subject backgrounds on this conversion course Candidates with professional qualifications or relevant professional experience in an associated area will also be considered. Successful applicants will usually have at least an A-level or equivalent in Mathematics and/or have received quantitative skills training as part of their undergraduate degree or professional experience.
International & EU requirements
English language requirements
All teaching at Royal Holloway is in English. You will therefore need to have good enough written and spoken English to cope with your studies right from the start.
The scores we require
- IELTS: 6.5 overall. No subscore lower than 5.5.
- Pearson Test of English: 61 overall. Writing 54. No subscore lower than 51.
- Trinity College London Integrated Skills in English (ISE): ISE III.
Country-specific requirements
For more information about country-specific entry requirements for your country please see here.
Your future career
Our new conversion degree joins a suite of successful MSc programmes within the Department of Computer Science and the Information Security Group at Royal Holloway that have earned an outstanding track record of graduate employability, with our students gaining employment in top international companies, research institutes and universities.
Demand for data scientists is buoyant, in the UK and worldwide, with salaries much higher than other IT professions and at least double the UK average full-time wage.
Our proximity to the M4 corridor – also known as 'England’s Silicon Valley' – provides excellent networking opportunities with some of the country’s top technology institutions. We bring several companies to our campus throughout the year, both for fairs and for delivering advanced topics seminars, which are an excellent opportunity to learn about what they do and discuss possible placements or jobs.
Our strong industry links help to provide placement and networking opportunities with some of the country’s leading institutions. In addition to the support provided by university’s Careers and Employability Service, the department has a dedicated administrator and an academic who coordinates and oversees placements and job opportunities, one-to-one coaching sessions and workshops, providing additional support to help you prepare for a successful career.
Our graduates go on to rewarding careers in academia or in companies or organisations operating in highly competitive areas. In recent years, these have included Amazon, American Express, BGL Group, Bupa, Capita, Centrica, EY, Facebook, Google, Hortonworks, JP Morgan, Microsoft, ONS, PWC, QuintilesIMS, Rolls Royce, Shell, UBS, VMware, Xerox and the Z/Yen Group. Examples of roles include Data Scientist, Quantitative Analyst, Big Data Engineer, and Technology Analyst.
Fees, funding & scholarships
Home (UK) students tuition fee per year*: £13,200
EU and international students tuition fee per year**: £28,500
Other essential costs***: There are no single associated costs greater than £50 per item on this degree course.
How do I pay for it? Find out more about funding options, including loans, grants, scholarships and bursaries.
* and ** These tuition fees apply to students enrolled on a full-time basis in the academic year 2024/25. Students studying on the standard part-time course structure over two years are charged 50% of the full-time applicable fee for each study year.
Royal Holloway reserves the right to increase all postgraduate tuition fees annually, based on the UK’s Retail Price Index (RPI). Please therefore be aware that tuition fees can rise during your degree (if longer than one year’s duration), and that this also means that the overall cost of studying the course part-time will be slightly higher than studying it full-time in one year. For further information, please see our terms and conditions.
** This figure is the fee for EU and international students starting a degree in the academic year 2024/25. Find out more
*** These estimated costs relate to studying this particular degree at Royal Holloway during the 2024/25 academic year, and are included as a guide. Costs, such as accommodation, food, books and other learning materials and printing, have not been included.
