MSc Big Data and Text Analytics

Program Overview

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Our modern world is witnessing a growth of online data in a variety of forms, including web documents, blogs, social networks, digital libraries and medical records. Much of this data contains valuable information, such as emerging opinions in social networks, search trends from search engines, consumer purchase behaviour, and patterns that emerge from these huge data sources. The sheer volume of this information means that traditional stand-alone applications are no longer suitable to process and analyse this data. Our course equips you with the knowledge to contribute to this rapidly emerging area. We give you hands-on experience with various types of large-scale data and information handling, and start by providing you with a solid understanding of the underlying technologies, in particular cloud computing and high-performance computing. You explore areas including:

• Mobile and social application programming
• Human-computer interaction
• Computer vision
• Computer networking
• Computer security

You also obtain practical knowledge of processing textual data on a large scale in order to turn this data into meaningful information, and have the chance to work on projects that are derived from actual industry needs proposed by our industrial partners.

Professional accreditation

Accredited by BCS, the Chartered Institute for IT for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional. Accredited by BCS, the Chartered Institute for IT on behalf of the Engineering Council for the purposes of partially meeting the academic requirement for registration as a Chartered Engineer. Accredited by the Institution of Engineering and Technology (IET) on behalf of the Engineering Council as meeting the requirements for Further Learning for registration as a Chartered Engineer. Candidates must hold a CEng accredited BEng/BSc (Hons) undergraduate first degree to comply with full CEng registration requirements. Why we're great.

• Develop the knowledge to handle data and the skills needed to turn it into valuable, meaningful information
• Gain hands-on experience – work on projects derived from the needs of our industrial partners
• Our Employability and Careers Centre is on hand to help with careers advice and planning. You will also have opportunities to present your research and travel to international conferences

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Our expert staff

Today’s computer scientists are creative people who are focused and committed, yet restless and experimental. Our staff are driven by creativity and imagination as well as technical excellence. We are conducting research in areas such as evolutionary computation, brain-computer interfacing, intelligent inhabited environments and financial forecasting. Specialist staff working on data analytics include:

• Professor Luca Citi – machine learning, learning from biological signals and data (EEG, etc)
• Dr Adrian Clark – automatic construction of vision systems using machine learning and evaluation of algorithms, data visualisation and augmented reality
• Professor Maria Fasli – analysis of structured/unstructured data, machine learning, adaptation, semantic information extraction, ontologies, data exploration, recommendation technologies
• Professor John Gan – machine learning for data modelling and analysis, dimensionality reduction and feature selection in high-dimensional data space

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Specialist facilities

We are one of the largest and best resourced computer science and electronic engineering schools in the UK. Our work is supported by extensive networked computer facilities and software aids, together with a wide range of test and instrumentation equipment.

• We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
• All computers run either Windows 10 or are dual boot with Linux
• Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
• Students have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
• We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors

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Your future

Demand for skilled graduates in the areas of big data and data science is growing rapidly in both the public and private sector, and there is a predicted shortage of data scientists with the skills to understand and make commercial decisions based on the analysis of big data. Our recent graduates have progressed to a variety of senior positions in industry and academia, and now work for global companies such as Microsoft as well as regional start-ups, as software developers, development managers and product managers. We also work with the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities. Read more about computer science and electronic engineering career destinations here.

Program Outline

Course structure

Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field. The following modules are based on the current course structure and may change in response to new curriculum developments and innovation. We understand that deciding where and what to study is a very important decision for you. We’ll make all reasonable efforts to provide you with the courses, services and facilities as described on our website. However, if we need to make material changes, for example due to significant disruption, or in response to COVID-19, we’ll let our applicants and students know as soon as possible.

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Components

Components are the blocks of study that make up your course. A component may have a set module which you must study, or a number of modules from which you can choose. Each component has a status and carries a certain number of credits towards your qualification.

Status	What this means
Core	You must take the set module for this component and you must pass. No failure can be permitted.
Core with Options	You can choose which module to study from the available options for this component but you must pass. No failure can be permitted.
Compulsory	You must take the set module for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.
Compulsory with Options	You can choose which module to study from the available options for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.
Optional	You can choose which module to study from the available options for this component. There may be limited opportunities to continue on the course/be eligible for the qualification if you fail.

The modules that are available for you to choose for each component will depend on several factors, including which modules you have chosen for other components, which modules you have completed in previous years of your course, and which term the module is taught in.

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Modules

Modules are the individual units of study for your course. Each module has its own set of learning outcomes and assessment criteria and also carries a certain number of credits. In most cases you will study one module per component, but in some cases you may need to study more than one module. For example, a 30-credit component may comprise of either one 30-credit module, or two 15-credit modules, depending on the options available. Modules may be taught at different times of the year and by a different department or school to the one your course is primarily based in. You can find this information from the module code . For example, the module code HR100-4-FY means:

HR	100	4	FY
The department or school the module will be taught by. In this example, the module would be taught by the Department of History.	The module number.	The UK academic level of the module. A standard undergraduate course will comprise of level 4, 5 and 6 modules - increasing as you progress through the course. A standard postgraduate taught course will comprise of level 7 modules. A postgraduate research degree is a level 8 qualification.	The term the module will be taught in. AU : Autumn term SP : Spring term SU : Summer term FY : Full year AP : Autumn and Spring terms PS: Spring and Summer terms AS: Autumn and Summer terms

Year 1 What fascinates you? Apply your learning in computer science or engineering to solve a problem. Design, implement and evaluate a solution, producing a dissertation on your investigation and giving an oral presentation of your work. Test your knowledge, while gaining practical experience and building your project management skills. View MSc Project and Dissertation on our Module Directory Humans can often perform a task extremely well (e.g., telling cats from dogs) but are unable to understand and describe the decision process followed. Without this explicit knowledge, we cannot write computer programs that can be used by machines to perform the same task. “Machine learning” is the study and application of methods to learn such algorithms automatically from sets of examples, just like babies can learn to tell cats from dogs simply by being shown examples of dogs and cats by their parents. Machine learning has proven particularly suited to cases such as optical character recognition, dictation software, language translators, fraud detection in financial transactions, and many others. View Machine Learning on our Module Directory Search engines have become the first entry point into a world of knowledge and they form an essential part of many modern computer applications. While much of the underlying principles have been developed over decades, the landscape of search engine technology has changed dramatically in recent years to deal with data sources magnitudes larger than ever before (the rise of 'big data'). As a result of this, new paradigms for storing, indexing and accessing information have emerged. This module will provide the essential foundation of information retrieval and equip students with solid, applicable knowledge of state-of-the-art search technology. View Information Retrieval on our Module Directory We live in an era in which the amount of information available in textual form - whether of scientific or commercial interest - greatly exceeds the capability of any man to read or even skim. Text analytics is the area of artificial intelligence concerned with making such vast amounts of textual information manageable - by classifying documents as relevant or not, by extracting relevant information from document collections, and/or by summarizing the content of multiple documents. In this module we cover all three types of techniques. View Text Analytics on our Module Directory This module aims to prepare students for conducting an independent research project leading to a dissertation and to provide them with an appreciation of research and business skills related to their professional career. As a precursor to their project students, individually select an area of Computer Science, or Electronic Engineering, or Computational Finance and perform the necessary background research to define a topic and prepare a project proposal under the guidance of a supervisor. The module guides them by a) introducing common research methods b) creating an understanding of basic statistics for describing and making conclusions from data c) helping to write a strong proposal including learning how to perform literature search and evaluation and d) giving an in-depth view into the business enterprise, financial and management accounting and investment appraisal. View Professional Practice and Research Methodology on our Module Directory Teamwork skills are essential for employability. The aim of this module is to provide students with the opportunity to apply their specialised knowledge to a realistic problem and gain practical experience of the processes involved in the team-based production of software. Wherever possible, teams are organised on the basis of shared interest, and the problem is designed to exercise their understanding of their area of specialised study. Starting from an outline description of a realistic problem, each team is required to develop a fully implemented software solution using appropriate engineering and project management techniques. View Group Project on our Module Directory As humans we are adept in understanding the meaning of texts and conversations. We can also perform tasks such as summarize a set of documents to focus on key information, answer questions based on a text, and when bilingual, translate a text from one language into fluent text in another language. Natural Language Engineering (NLE) aims to create computer programs that perform language tasks with similar proficiency. This course provides a strong foundation to understand the fundamental problems in NLE and also equips students with the practical skills to build small-scale NLE systems. Students are introduced to three core ideas of NLE: a) gaining an understanding the core elements of language--- the structure and grammar of words, sentences and full documents, and how NLE problems are related to defining and learning such structures, b) identify the computational complexity that naturally exists in language tasks and the unique problems that humans easily solve but are incredibly hard for computers to do, and c) gain expertise in developing intelligent computing techniques which can overcome these challenges. View Natural Language Engineering on our Module Directory COMPONENT 08: OPTIOL Option from list (30 CREDITS)

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Teaching

• Courses provide a thorough and up-to-date knowledge of the theory, methods and applications of computer science
• Core components combined with optional modules, to enable you to gain either in-depth specialisation or a breadth of understanding
• Our postgraduates are encouraged to attend conferences and seminars, as well as engage with the wider research community

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Assessment

• Courses are assessed on the results of your written examinations, together with continual assessments of your practical work and coursework

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Dissertation

• Your research project allows you to focus in depth on your chosen topic from April
• Close supervision by faculty staff