Data Analytics MSc

2019-20 (also available for 2020-21)

This course is eligible for Master's loan funding. Find out more.

Start date

16 September 2019

Duration

1 year full-time

Places available (subject to change)

20

Phone contact: +44 (0)1484 473116

About the course

Our Master's course is designed to meet the demand for a new kind of IT specialist with the skills and knowledge in data science.

The total demand in ‘big data’ users is set to rise to around 644,000 across all industries.More connectivity means more data and UK companies are leading the way in collecting, processing and understanding it. It’s estimated that 31,000 people work in specialist big data roles in the UK and the talent pipeline, together with demand, should see that increase by 243% over the next 5 years.*

In order to address these demands, given the large amount of data collected by all kinds of organisations, graduates of the course will be equipped with an understanding of:

  • statistics
  • data mining techniques
  • big data and associated file systems
  • data visualisation

and will ultimately be able to combine this knowledge to provide solutions to novel problems associated with the organisation and the analysis of data.

The course seeks to develop the ability to critically evaluate existing and emerging Data Science technology, apply knowledge, understanding and analytical and design skills in support of analytical and big data problems.

*invest.great.gov.uk, February 2019

The age has dawned where we have the tools, the  hardware, and access to sufficient data to do great things. We have only scratched the surface of what can be achieved by manipulating, visualising and asking new questions about data. These skills sit at the core of this course, so come and be part of the data science revolution.

Prof Richard Hill

Prof Richard Hill, Head of Department, Computer Science

Course detail

Effective Research and Professional Practice

This module aims to provide you with skills that are key to helping you become a successful computing researcher or practitioner. You'll get the opportunity to study topics including the nature of research, the scientific method, research methods, literature review and referencing. The module aims to cover the structure of research papers and project reports, reviewing research papers, ethical issues (including plagiarism), defining projects, project management, writing project reports and making presentations.

Data Analysis and Statistics

Statistical methodology and statistical practice are very central for data analysis. In official statistics, in hypothesis testing, in distributional properties of data, in very many application domains that support decision making, and so on, such as case studies where both statistical practice is important and the underlying and underpinning statistical methodology that is at issue. Statistical methods and statistical implementation are also very complementary to machine learning and data mining, covering supervised and unsupervised methods. Quite major developments in mathematics, in the past few hundred years, were brought about by statistical methods and their implementation. Real world applications are addressed by modelling and implementing applications encountered in business, e.g. Customer analytics, Credit scoring, Financial forecasting), in Health and Medical research (e.g. Automatic diagnosing, genetic data mining and Bioinformatics, mining online medical publications libraries), in structured and unstructured data analysis, etc. Students are exposed to current core research topics in data mining, machine learning, and interdisciplinary research in which data analysis plays an essential role.

Data Mining

Data mining is a collection of tools, methods and statistical techniques for exploring and extracting meaningful information from large data sets. It is a rapidly growing field due to the increasing quantity of data gathered by organisations. There is a potential high value in discovering the patterns contained within such data collections. This module looks at different data mining techniques and gives students the chance to use appropriate data-mining tools in order to evaluate the quality of the discovered knowledge. Topics studied include looking at the value of data; approaches to preparing data for exploration; supervised and un-supervised approaches to data mining; exploring unstructured data; social impact of data mining. Current application areas and research topics in data mining will also be discussed and students will be expected to develop their knowledge such that they are able to contribute to such discussions and to increase their background knowledge and understanding of issues and developments associated with data mining.

Big Data Analytics

The ever-increasing advancements in sensing technologies, network infrastructure, storage and social media have enabled us to acquire an unprecedented volume of data at an explosive rate. As a result, the ability to efficiently and accurately derive human-understandable knowledge from these datasets has become increasingly critical to our digitally-driven society and economy. Under this Big Data phenomenon, tremendous endeavours have been devoted to tackle its underlying challenges through both novel solutions and the evolution of existing methodology. The module aims to provide students with the knowledge and critical understanding of contemporary challenges posed by the big data. The topics covered here include the fundamental characteristics and operations associated with big data; existing and emerging architectures and processing techniques; domain applications of big data in practice. Through this module, students will develop an informed understanding of the principles and practice of big data analytics in both general and application specific contexts.

Machine Learning

Machine Learning techniques are now used widely in a range of applications either stand-alone or integrated with other AI techniques. The Machine Learning module allows you to obtain a fundamental understanding of the subject as a whole: how to embody machines with the ability to learn how to recognise, classify, decide, plan, revise, optimise etc. You will learn which machine learning techniques are appropriate for which learning problem, and what the advantages and disadvantages are for a range of ML techniques. We will consider the widely known data-driven approaches, and specific techniques such as “deep learning”, and investigate the typical applications and potential limitations of these approaches. We will introduce available tools and use them in practical classes, evaluating learning bias and characteristics of training sets. High profile applications of data driven, stand-alone, ML systems will be investigated, such as the AlphaGo method. Where data is sparse, and knowledge is already present in a system, we will investigate methods to improve heuristics of existing AI systems, and to learn or revise domain knowledge. This is essentially the area of model-driven ML, where the learning system is often integrated to other reasoning systems.

Case Studies in Data Analytics and Artificial Intelligence

The purpose of this module is to enable students to appreciate the historical, current and future application areas of AI and DA in relation to both theoretical and practical aspects and to investigate at least one application area in depth. Case studies discussed in the sessions will provide an exploration of applications in a variety of different areas and will be achieved by combinations of study of current research papers, tutors’ own research & the investigative work of the students within the module.

Data Visualisation

With ever-increasing advancements in Internet-of-Things, Cyber-Physical Systems, and social media applications huge volume of complex and multi-dimensional datasets are being generated every day. Visually analysing these datasets facilitates the transformation of raw data into valuable knowledge and information. The biggest challenge is to articulate suitable solutions of complex analytical problems by visually interacting with the designed artefacts without going into underlying complexities. Tremendous endeavours have been devoted to streamline innovative solutions, novel methods, tools, processes and methodologies to address underlying challenges. This module aims to provide students with core knowledge and deep understanding of advanced theories underpinning data visualisation, best practices in using visualisation artefacts effectively and practical skills in implementing the theoretical knowledge into certain application domains. Students will be engaged in practical utilisation of state-of-the-art visualisation tools and methods to understand real-world big data problems, and to rectify complex issues with visual analysis. Topics that will be covered in this module include exploratory data visualisation; data visualisation theories, existing and emerging interactive 2D and 3D visualisation toolkits, and application of visualisation skillset in application specific domains.

Databases for Large Data-sets

The data needs of modern Enterprises and organisations require a more flexible approach to data management than that offered by traditional relational database management systems (RDBMS). With organizations increasingly looking to Big Data to provide valuable business insights, it has become clear that new approaches are required to handle these new data requirements. Primarily focusing on non-relational data models, this module introduces students to alternative approaches to modelling the data needs of an organization. It also provides students with an opportunity to use non-relational databases and database technologies to build robust and effective organizational information systems. The aim of this module is to introduce the student to the fundamental concepts, core principles, formalism, and practical skills that underpin modern data system where students will develop a practical understanding of methods, techniques and architectures required to build big data systems in order to extract information from large heterogeneous data sets.

Individual Project

This module enables the student to work independently on a project related to a self-selected problem. A key feature in this final stage of the MSc is that students will be encouraged to undertake an in-company project with an external Client. Where appropriate, however, the Project may be undertaken with an internal Client - research-active staff - on larger research and knowledge transfer projects. The Project is intended to be integrative, a culmination of knowledge, skills, competencies and experiences acquired in other modules, coupled with further development of these assets. In the case where an external client is involved, both the Client and Student will be required to sign a learning agreement that clearly outlines scope, responsibilities and ownership of the project and its products or other deliverables. The Project will be student-driven, with the clear onus on the student to negotiate agreement, and communicate effectively, with all parties involved at each stage of the Project.

Entry requirements

Entry requirements for this course are normally:

  • A BSc or BEng Honours degree (2:2 or above) in Computing or Engineering or scientific related subject or an equivalent professional qualification.
  • Applicants are expected to be familiar with and have some aptitude for basic Mathematics and basic Statistical concepts and methods
  • Other qualifications and/or experience that demonstrate appropriate knowledge and skills at an Honours degree level - the qualification and experience should be in the area of Computer Science or Mathematics/Statistics

If your first language is not English, you will need to meet the minimum requirements of an English Language qualification. The minimum of IELTS 6.5 overall with 6.0 in Writing and no element lower than 5.5, or equivalent will be considered acceptable.

Why Choose Huddersfield?


Watch this clip to find out five great reasons to choose the University of Huddersfield for postgraduate study.

Teaching excellence

  1. Huddersfield is a TEF gold-rated institution delivering consistently outstanding teaching and learning of the highest quality found in the UK (Teaching Excellence Framework, 2017).
  2. We won the first Global Teaching Excellence Award recognising the University’s commitment to world-class teaching and its success in developing students as independent learners and critical thinkers (HEA, 2017).
  3. Here at Huddersfield, you’ll be taught by some of the best lecturers in the country. We’ve been the English university with the highest proportion of professionally-qualified teaching staff for the past four years*.
  4. For the past ten years, we’ve been the UK’s leading university for National Teaching Fellowships too, which rate Britain’s best lecturers. It’s all part of our ongoing drive for teaching excellence, which helps our students to achieve great things too.
  5. We’re unique in the fact that all our permanent teaching staff** have, or are completing, doctorates. This expertise, together with our teaching credentials, means that students here learn from knowledgeable and well-qualified teachers and academics who are at the forefront of their subject area.

*HESA - First awarded in 2016, maintained in 2017, 2018 and 2019.

**Permanent staff, after probation: some recently appointed colleagues will only obtain recognition in the months after their arrival in Huddersfield, once they have started teaching; research degrees applies to those on contracts of more than half-time.

Enhance your career


* Percentage of our postgraduate students who go on to work and/or further study within six months of graduating (Destination of Leavers from Higher Education Survey 2016/17).

93.8%*

Student support

At the University of Huddersfield, you'll find support networks and services to help you get ahead in your studies and social life. Whether you study at undergraduate or postgraduate level, you'll soon discover that you're never far away from our dedicated staff and resources to help you to navigate through your personal student journey. Find out more about all our support services.

In the School of Computing and Engineering we have a dedicated guidance team that provides the students that need it, guidance and support on both academic and non-curriculum matters.These may include:

  • Settling in
  • Personal development
  • Health and wellbeing
  • Balancing work and studies
  • Exam and assignment preparation
  • Staying the course (attendance, change course, extensions etc.)
  • Study skills and Technical English support from our Academic Skills Tutor

Important information

We will always try to deliver your course as described on this web page. However, sometimes we may have to make changes as set out below.

Changes to a course you have applied for

If we propose to make a major change to a course that you are holding an offer for, then we will tell you as soon as possible so that you can decide whether to withdraw your application prior to enrolment.

Changes to your course after you enrol as a student

We will always try to deliver your course and other services as described. However, sometimes we may have to make changes as set out below:

Changes to option modules

Where your course allows you to choose modules from a range of options, we will review these each year and change them to reflect the expertise of our staff, current trends in research and as a result of student feedback or demand for certain modules. We will always ensure that you have a range of options to choose from and we will let you know in good time the options available for you to choose for the following year.

Major changes

We will only make major changes to the core curriculum of a course or to our services if it is necessary for us to do so and provided such changes are reasonable. A major change in this context is a change that materially changes the services available to you; or the outcomes, or a significant part, of your course, such as the nature of the award or a substantial change to module content, teaching days (part time provision), classes, type of delivery or assessment of the core curriculum.

For example, it may be necessary to make a major change to reflect changes in the law or the requirements of the University’s regulators; to meet the latest requirements of a commissioning or accrediting body; to improve the quality of educational provision; in response to student, examiners’ or other course evaluators’ feedback; and/or to reflect academic or professional changes within subject areas. Major changes may also be necessary because of circumstances outside our reasonable control, such as a key member of staff leaving the University or being unable to teach, where they have a particular specialism that can’t be adequately covered by other members of staff; or due to damage or interruption to buildings, facilities or equipment.

Major changes would usually be made with effect from the next academic year, but this may not always be the case. We will notify you as soon as possible should we need to make a major change and will carry out suitable consultation with affected students. If you reasonably believe that the proposed change will cause you detriment or hardship we will, if appropriate, work with you to try to reduce the adverse effect on you or find an appropriate solution. Where an appropriate solution cannot be found and you contact us in writing before the change takes effect you can cancel your registration and withdraw from the University without liability to the University for future tuition fees. We will provide reasonable support to assist you with transferring to another university if you wish to do so.

Termination of course

In exceptional circumstances, we may, for reasons outside of our control, be forced to discontinue or suspend your course. Where this is the case, a formal exit strategy will be followed and we will notify you as soon as possible about what your options are, which may include transferring to a suitable replacement course for which you are qualified, being provided with individual teaching to complete the award for which you were registered, or claiming an interim award and exiting the University. If you do not wish to take up any of the options that are made available to you, then you can cancel your registration and withdraw from the course without liability to the University for future tuition fees and you will be entitled to a refund of all course fees paid to date. We will provide reasonable support to assist you with transferring to another university if you wish to do so.

When you enrol as a student of the University, your study and time with us will be governed by a framework of regulations, policies and procedures, which form the basis of your agreement with us. These include regulations regarding the assessment of your course, academic integrity, your conduct (including attendance) and disciplinary procedure, fees and finance and compliance with visa requirements (where relevant). It is important that you familiarise yourself with these as you will be asked to agree to abide by them when you join us as a student. You will find a guide to the key terms here, where you will also find links to the full text of each of the regulations, policies and procedures referred to.

The Office for Students (OfS) is the principal regulator for the University.

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