|Study location||United Kingdom, Egham, Surrey|
|Type||Master courses, full-time|
|Nominal duration||2 years|
Undergraduate diploma (or higher)
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.
The entry qualification documents are accepted in the following languages: English.
Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original.
IELTS: 6.5 (with a minimum of 5.5 in all other subscores)
At least 2 reference(s) must be provided.
A motivation letter must be added to your application.
Interested? To learn more about this study programme, entry requirements and application process, please contact one of our consultants in a country nearest to you.
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.
Computation with Data
In this module you will develop an understanding of the basics of algorithmic thinking and problem solving using programming. You will become familiar in using the Java programming language, examining particular features and constructs as well as basics of object-oriented programming. You will use these to solve specific algorithmic tasks and evaluate programming solutions.
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.
In this module you will develop an understanding of the core concepts in data and information management, looking at the role of databases and database management systems in managing organisational data and information. You will learn how to identify organisational information requirements, model them using conceptual data modeling techniques, convert the conceptual data models into relational data models and verify their structural characteristics using normalisation techniques. You will gain experience in designing and implementing a relational database using an industrial database management system, and examine how to mainipulate data using SQL.
You will only take this module if you lack background in this area.
Large-Scale Data Storage and Processing
In this module you will develop an understanding of the underlying principles of large scale data storage and processing frameworks. You will look at the opportunities and challenges of building massive scale analytics soltutions, gaining hands-on experience in using large and unstructured data sets for analysis and prediction. You will examine the techniques and paradigms for querying and processing massive data sets, such as MapReduce, Hadoop, data warehousing, SQL for data analytics, and stream processing. You will consider the fundamentals of scalable data storage, including NoSQL databases, and will design, develop, and evaluate an end-to -nd analytics solution combining large scale data storage and processing frameworks.
You will spend this year on a work placement. You will be supported by the Department of Computer Science and the Royal Holloway Careers and Employability Service to find a suitable placement. This year forms an integral part of the degree programme and you will be asked to complete assessed work. The mark for this work will count towards your final degree classification.
You will carry out an extended piece of individual work under the supervision of an academic member of staff, including the preparation of a dissertation and any programs you may have written. Your project may stress theoretical, methodological, or implementation aspects of a problem or case study, and you may wish to build on the experience that you will have gained during your placement.
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.
Students of Data Science and Analytics with a Year in Industry at Royal Holloway, University of London will 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’ – gives you the chance to enjoy a year in industry that will pave the way for a rewarding future career. Our recent graduates have gone on to enjoy roles in organisations such as British Aerospace, Microsoft, Amazon and American Express.
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