The importance of Big Data grows year on year, with sectors including healthcare, manufacture, retail, administration and more reliant on the insights that accurate data capture and analysis can provide. Study Data Science and Analytics at Royal Holloway, University of London and you’ll develop the practical skills needed to handle and analyse data in a wide variety of fields, preparing you for a rewarding career in Big Data.
You’ll study in a department with a strong reputation for research excellence. The Department of Computer Science was ranked 11th in the UK for the quality of its research publications (Research Excellence Framework 2014), and you’ll have the opportunity to contribute to this leading research culture with your own Individual Project.
This flexible programme gives you the chance to tailor your learning to your own strengths and interests, with a broad range of optional modules including Online Machine Learning, Methods of Bioinformatics and Microeconometrics providing scope and variety. You’ll be well-equipped to continue your studies at PhD level, which will place you in a strong position to pursue more advanced, research-based roles after graduation.
Follow your passion for Data Science and Analytics at Royal Holloway and you’ll graduate with a desirable Masters degree from a highly regarded department, as well as transferable skillset that’s both in short supply and in high demand by employers. Our location near the M4 corridor – also known as ‘England’s Silicon Valley’ – means students can benefit from networking and placement opportunities with some of the country’s top technology institutions.
- Study in a highly-regarded department, ranked 11th in the UK for research publications (Research Excellence Framework 2014).
- Benefit from strong industry ties, with close proximity to ‘England’s Silicon Valley’.
- Graduate with a Masters degree leading to excellent graduate employability prospects.
- Tailor your learning with a wide range of engaging optional modules.
- Choose from a one-year programme structure or add an optional year in industry.
Computation with Data
The module is structured around a series of programming problems and assignments designed to teach you the basics of algorithmic thinking and problem solving using programming. You will be introduced to the Java programming language features and constructs as well as basics of object-oriented programming, which will be put in the context of solving specific algorithmic tasks.
This module covers algorithm-independent machine learning; unsupervised learning and clustering; exploratory data analysis; Bayesian methods; Bayes networks and causality; and applications, such as information retrieval and natural language processing. You will develop skills in data analysis, including data mining and statistics.
This module provides you with an introduction to the core concepts in data and information management. It is centered around the core skills of identifying organizational information requirements, modeling them using conceptual data modeling techniques, converting the conceptual data models into relational data models and verifying its structural characteristics with normalization techniques, and implementing and utilizing a relational database using an industrial-strength database management system.
Large-Scale Data Storage and Processing
You will study the underlying principles of storage and processing massive collections of data, typical of today's Big Data systems, gaining hands-on experience in using large and unstructured data sets for analysis and prediction. The topics covered will include techniques and paradigms for querying and processing massive data sets (MapReduce, Hadoop, data warehousing, SQL for data analytics, and stream processing), fundamentals of scalable data storage (NoSQL data bases such as MongoDB, Cassandra, and HBase), working with dynamic web data (data acquisition and data formats), elements of cloud computing, and applications to real world data analytics and data mining problems (sentiment analysis and social network mining).
Programming for Data Analysis
In this module you will learn how to use MATLAB (Matrix Laboratory) and WEKA (Waikato Environment for Knowledge Analysis) as tools for machine learning and data mining. For MATLAB, you will develop an understanding of how to input and output data using vectors, arrays and matrics; learn techniques in data visualization, including plots in 2 and 3 dimensions, scatter plots, barplots, and histograms; and learn how to implement concepts from linear algebra and statistics, including probability and matrix decompositions. For WEKA, you will develop an understanding of how to use the software as a tool for training and testing, predicting generalisation performance, and cross-validation; and learn how to implement decision trees, naïve Bayes classifiers, and clustering methods.
The individual project provides you will the opportunity 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 you have been taught throughout the programme.
In addition to these mandatory course units there are a number of optional course units available during your degree studies. The following is a selection of optional course units that are likely to be available. Please note that although the College will keep changes to a minimum, new units may be offered or existing units may be withdrawn, for example, in response to a change in staff. Applicants will be informed if any significant changes need to be made.
Advanced Data Communications
Advanced Distributed Systems
Business Intelligence Systems, Infrastructures and Technologies
Decision Theory and Behaviour
Intelligent Agents and Multi-Agent Systems
Introduction to Cryptography
Methods of Bioinformatics
Methods of Computational Finance
On-line Machine Learning
Smart Cards, RFIDs and Embedded Systems Security
Topics in Applied Statistics
Visualisation and Exploratory Analysis
Wireless, Sensor and Actuator Networks
Teaching is organized in terms of 11 weeks each. Examinations are taken in April / May of each academic year, except for Data Analysis for which the exam is in January. The individual project is taken over 12 weeks during the Summer.
A weekly seminar series runs in parallel with the academic programme, which includes talks by professionals in a variety of application areas as well as workshops that will train you to find a placement or a job and lead a successful career.
Assessment is carried out by a variety of methods including coursework, small group projects, and examinations, the proportions of which vary according to the nature of the modules.
This degree can be taken part-time.
UK 2:1 (Honours) or equivalent in Computer Science, Economics, Mathematics, Physics, or other subjects that include a strong element of both mathematics and computing.
Relevant professional qualifications and relevant experience in an associated area will be considered.
English language requirements:
Pass IELTS 6.5 overall with no subscore below 5.5
If you require Royal Holloway to sponsor you study in the UK, your IELTS must a UK government-approved Secure English Language Test (SELT).
International and EU entry requirements
Please select your country from the drop-down list below
Students from overseas should visit the International pages for information on the entry requirements from their country and further information on English language requirements. Royal Holloway offers a Pre-Master’s Diploma for International Students and English language pre-sessional courses, allowing students the opportunity to develop their study skills and English language before starting their postgraduate degree.
Study Data Science and Analytics at Royal Holloway, University of London and you'll graduate with excellent employability prospects in a range of fields.
You’ll develop a range of highly sought-after transferable skills, while our proximity to the M4 corridor technology hub – also known as ‘England’s Silicon Valley’ – will provide you with excellent placement and networking opportunities to pave the way for a rewarding future career. Our recent graduates have gone on to enjoy roles at prestigious organisations including British Aerospace, Microsoft, Amazon, American Express and many more.
- 90% of Royal Holloway graduates in work or further education within six months of graduating.
- Strong industry ties help to provide placement and networking opportunities with some of the country’s leading institutions.
- On-site College Careers Service provides help and support for students.
Home and EU students tuition fee per year 2017/18*: £8,300
Overseas students tuition fee per year 2017/18*: £18,500
Other essential costs**: There are no single associated costs greater than £50 per item on this course
Find out more about funding options, including loans, grants, scholarships and bursaries.
*The tuition fees given above apply to students enrolled on a full-time basis. Students studying part-time are charged a pro-rata tuition fee and information is available from the Royal Holloway Student Fees Office on Student-Fees@rhul.ac.uk. All fees are likely to rise annually in line with inflation but no more than 5 per cent per year.
For further information, please see Royal Holloway’s Terms & Conditions.
** These estimated costs relate to studying this particular degree programme at Royal Holloway. Costs, such as accommodation, food, books and other learning materials and printing etc., have not been included.