Postgraduate study
Computing & Web

This course is available for January 2019 entry

MSc Data Science

Data scientists use a range of computational and statistical techniques to unlock insight from data and solve complex problems. This emerging profession sits at the cutting-edge of computer science and graduates are increasingly in demand from industry. This innovative data science course equips you with the specialist skills and knowledge to make an immediate and meaningful contribution to a range of industry environments.

Course information

Full-time

  • within 1 year (September start), 16 months (January start) or 2 years with advanced practice (September or January start)

More full-time details

2018 entry

Part-time

  • 2 years

More part-time details

2018 entry

  • Enrolment date: September and February
  • Admission enquiries: 01642 342639
  • Fee for UK/EU applicants: £561 per 20 credits
    More details about our fees

Contact details

Further information

  • Facilities

    Computing and Web

    Teesside has fantastic state-of-the-art facilities for web and computing students including a wide range of web, multimedia, network and programming studios. This environment prepares students for work in industry by promoting team work and the use of case studies, problem solving and methods such as peer programming.

 

You are taught by expert staff from our machine intelligence research group, ensuring that you have access to the very latest thinking from the field of data science. You have the opportunity to contribute to live research and to progress from postgraduate study to postdoctorate research.The School of Computing, Media & the Arts has a proven record of successful research, consultancy and enterprise projects with industry in the field of data science, which means that staff have relevant real-world case studies to draw upon for teaching materials.

There are three routes you can choose from to gain an MSc Data Science:

  • full-time - 2 years with advanced practice (September and January start)
  • full-time - 1 year (September start) or 16 months (January start)
  • part-time - 2 years.

Course structure

Core modules

Advanced Data Analytics

This module provides you with the core principles and practical skills to apply state-of-the-art computational methods to perform data analytics. The skills are very important in the new horizon of data analysis where existing massive amount of data contains valuable knowledge, which is critical for prediction and decision-making. Due to its characters (3V: volume, velocity, and variety), computational methods are required to extract such knowledge.

You form a solid foundation of 1) predictive analytics, 2) data-driven decision making which refers to tools and techniques for building statistical or machine learning models to make predictions and decisions based on data. Practical guidance about how to handle unlabelled, noisy, incomplete, large-scale data is discussed and you learn how to select the best technique to handle different type of data in different scenarios.

Data Mining

Data mining is the process of automatically extracting novel information from data sets. These data sets are typically of a scale that precludes any attempt at manual analysis. Key tasks in data mining include: categorising phenomena; finding similarities in subgroups; detecting unusual phenomena; and determining dependencies between phenomena.

Data mining develops techniques predominantly on the basis of statistics and probability theory, but also draws heavily on other areas of computer science in order to engineer systems that are able to process data on a vast scale. This module provides an in-depth, systematic, and critical understanding of data mining.

Emerging Database Technologies

You develop your ability to design and implement database, big data and analytics applications to meet business needs. A case study is used to follow the system development life cycle. You develop a plausible application from inception to implementation for a real world scenario.

You investigate the issues and technologies associated with implementing and supporting large scale databases and the services that are needed to maintain and access a repository of data. Investigations are undertaken in a number of areas including big data, data warehouses, integrating legacy data, data management and approaches that support the modelling and visualisation of data for a range of use views.

Interactive Graphics

Dynamic, interactive visualisations enable the reader to explore the data for themselves through a variety of perspectives. Static visualisations are excellent for print medium but are restricted to showing a single perspective and do not handle multidimensional datasets well. Using an interactive graphic the reader can zoom in on sections of the data which are of interest, explore more than one dimension at a time, and sort and filter to discover new patterns and themes within the data. Particularly useful is the ability to provide a macro/micro view of the same data, i.e. a big picture view of the full dataset from which the reader can then ‘drill down’ into the lower level detail.

This module uses the javascript library for Data-Driven Documents (D3js) for creating animated, dynamic graphics for the web, and looks at other alternatives available.

Master's Project: Data Science

You undertake a major, in-depth, individual study in an aspect of data science. Normally the data science master's project will be drawn from commercial, industrial or research-based problem areas. The project involves you researching and investigating aspects of data science and then producing a major deliverable (e.g. software package or tool, design, web-site, research findings etc.). You also carry out a critical evaluation of your major deliverable, including obtaining third party evaluation where appropriate. Alternatively, you may undertake a research study and report findings in the style of a scholarly article.

Quantitative Visualisation

The field of information visualisation has expanded rapidly with many designers generating new forms of charts through which to view quantitative data. This module explores the range of charts available from the traditional such as bar charts and pie charts, to the more novel such as stream graphs, tree maps, sunbursts, and force diagrams, and examines their mathematical properties.
By accurately representing quantitative data using appropriate charts, the intended audience can make their own interpretations of the data and identify emerging patterns and themes that are more readily recognisable in chart form than in the form of raw data.

Research Methods for Computing

You gain the knowledge and skills to understand the research process in computing and digital media, and the necessary skills to undertake your masters project. You learn how to use and critically evaluate previous academic research, and to generate good evidence material to justify their professional practice. This involves you learning about different research strategies and data generation methods and how they fit into the development lifecycle and the evaluation of the user experience, the use of the academic research literature, and research ethics.

Assessment involves you preparing a research proposal which can form the basis of their masters project.

 

Advanced practice

Internship

Internship is normally a six month period of placement working in a host organisation where you usually receive a salary. You gain practical experience over a substantial period of time, enhance your employability and put your academic learning into practice. The placement office identify suitable placements, but you can also submit relevant placement opportunities for consideration. You are interviewed and selected by the host organisation.

 

Modules offered may vary.

How you learn

You learn about concepts and methods primarily through keynote lectures and tutorials using case studies and examples. Lectures include presentations from guest speakers from industry. Critical reflection is key to successful problem solving and essential to the creative process. You develop your own reflective practice at an advanced level, then test and assess your solutions against criteria that you develop in the light of your research.

How you are assessed

Work placement

Career opportunities

We prepare you for a career in industry. In addition to your taught classes, we create opportunities for you to meet and network with our industry partners through events such as our ExpoSeries, which showcases student work to industry. ExpoTees is the pinnacle of the ExpoSeries with over 100 businesses from across the UK coming to the campus to meet our exceptional students, with a view to recruitment.

Graduates can expect to find employment in one of the increasing number of sectors needing data science specialists, such as the defence industry, financial industry, telecommunications, and health sector.

Entry requirements

You will normally have a first degree in related discipline (2.2 minimum) or relevant experience or equivalent qualifications.

In addition, international students will require IELTS 6.0 or equivalent.

For additional information please see the entry requirements in our admissions section

International applicants can find out what qualifications they need by visiting Your Country

Course information

Full-time

  • within 1 year (September start), 16 months (January start) or 2 years with advanced practice (September or January start)

More full-time details

2018 entry

Part-time

  • 2 years

More part-time details

2018 entry

  • Enrolment date: September and February
  • Admission enquiries: 01642 342639
  • Fee for UK/EU applicants: £561 per 20 credits
    More details about our fees

Contact details

Further information