Postgraduate MSc/PgDip

Data Science

School of Computing, Science and Engineering

Full-time

Part-time

Attendance

One year

Three year

Course

September 2019

Next enrolment
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Introduction

In a nutshell

Learn to tell a story from data and become immersed in Big Data techniques. This unique postgraduate course is your opportunity to develop specialist knowledge in data science and prepare for one of the most in-demand roles.

Defined by Harvard Business Review as the sexiest job in the 21st century, there has been major interest and investment in data science. Such is the interest that demand has outstripped supply. Major global organisations including Google and Microsoft, and even public organisations, such as the NHS, are struggling to find qualified data scientists.

Developed as a postgraduate conversion pathway – the first of its kind in the UK - the course is suitable for students from any discipline with a demonstrable mathematical aptitude.

You will:
  • Develop an awareness of the latest developments in advanced databases, data mining and big data tools such as Hadoop
  • Gain SAS certification while you study through our partnership with the SAS Student Academy
  • Work with real-world messy data and gain experience across the data science stack
  • Explore 'Big Data', machine learning and data visualisation
International

students accepted

This is for you if...

1.

You want to enhance your existing skills and qualifications for a future career in data science

2.

You have an enquiring mind, with a practical and analytical approach to problem solving

3.

You're a knowledge-seeker and want to learn how to tell a story with data

Course details

All about the course

The MSc Data Science programme is delivered over one year full-time or three years part-time. It offers a comprehensive range of topics split into four modules worth 30 credits each. This modular structure is designed to improve the breadth of your learning and help you to generate ideas for your research project, which is worth an additional 60 credits.

As part of the course preparation, you will attend an intensive two-week session prior to the start. This session will review basic statistics and database concepts, plus an overview of either Python or R programming for data analysis. 

Modules

Principles of Data Science

This module aims to provide you with the history and context of data science, the skills, challenges, and methodologies the term implies. In addition you will learn how to develop skills in presenting quantitative data using appropriate displays, tabulations and summaries, and statistical methods in developing and testing hypotheses.

Advanced Databases

This module aims to provide you with a broad overview of the general field of database systems and to develop specialised knowledge in areas that demonstrate the interaction and synergy between ongoing research and practical deployment of this field of study.

Applied Statistics and Data Mining

This module aims to introduce you to the tools and techniques to build decision making systems for business organisations; from gathering large sets of data and information, to the production of outputs and reports that will allow organisations to make strategic decisions to improve their businesses and predict future trends.

Big Data Tools and Techniques

In this module you will develop your skills and understanding of the tools and techniques available to data scientists to analyze big data. You will be able to compare and contrast how different types of developers and users can exploit Big Data platforms such as Hadoop, text analytics, Internet of Things and Social Media. Additionally, you will gain experience in data visualisation tools and techniques.

MSc Project

The project module aims to provide you with an opportunity to integrate learning from the course modules, working under the direction of an academic supervisor to carry out high-level coordinated academic and practical work on researching a suitable problem and developing, evaluating and critically assessing a robust, scalable and usable solution.

Please note that it may not be possible to deliver the full list of options every year as this will depend on factors such as how many students choose a particular option. Exact modules may also vary in order to keep content current. When accepting your offer of a place to study on this programme, you should be aware that not all optional modules will be running each year. Your tutor will be able to advise you as to the available options on or before the start of the programme. Whilst the University tries to ensure that you are able to undertake your preferred options, it cannot guarantee this.

What will I be doing?

Teaching

Learning is delivered using a range of methods. Lectures will introduce ideas and stimulate group discussions. Tutorials will develop your ability to create problem-solving strategies and provide practice and feedback with scenarios to help with exam preparation. Workshops will develop your expertise in SAS tools, using analysis of complex datasets.

External speakers from multinational blue-chip organisations and local companies will deliver seminars to complement your learning and provide real-world case studies related to your studies.

Assessment

  • 50% of the assessment will comprise a practical project where you will be given some data, conduct analysis, present your interpretations and explain your strategy.
  • 50% will comprise an examination, which will assess more theoretical aspects of the course and will assess your immediate response to unseen scenarios or data.

The School of Computing, Science and Engineering

The School of Computing, Science and Engineering (CSE) seeks to improve lives through proactive collaboration with industry and society. Our stimulating, industry-accredited courses and research programmes explore engineering, physics, acoustics, computing, mathematics and robotics. Through our award-winning lecturers, world-class facilities and research-led teaching, CSE produces highly employable graduates ready for the challenges of today and tomorrow.

Facilities

We have sophisticated computing suites at our Peel Park and MediaCityUK campus. These facilities include a Cloud Computing platform with OpenStack and lab workstations, providing access to software platforms and languages specialised in machine learning, data mining, statistical analysis and Big Data. These include: SAS Enterprise Guide & Miner, Python, Apache Hadoop & Spark, RapidMiner  It also includes NoSQL databases, such as MongoDB.

Postgraduate Staff Profile

Dr Mohamad (Mo) Saraee

Dr Mohamad Saree is a Senior Lecturer in Data Mining & Bioinformatics, & Programme Leader, MSc DWBS (Databases & Web-Based Systems). His research interests include intelligent databases, advanced data types (temporal, spatial and multimedia), data mining and big data (theory and applications), actionable knowledge discovery, bio and medical informatics, semantic web and e-commerce. 

Mohamad holds a PhD in Computer Science from the University of Manchester. He is a member of our Informatics Research hub and has co-authored four book chapters, 23 research articles in leading ISI / International referred journals and 66 papers in IEEE and International conferences.

Employment and stats

What about after uni?

Employment

Demand for data scientists outstrips supply and the is continued demand for qualified, talented graduates across many global industries. With this qualification, you’ll be equipped with the skill set and technical knowledge relevant for the data science and big data job market.

Further study

Some of our graduates progress to postgraduate and doctoral research in our Salford Innovation and Research Centre (SIRC). The Centre aims to build on our world-class research and provide industries with guidance and expertise in the advancement of technology for business success and economic growth. 

Research at the Centre is supported by EPSRC, TSB, DoH, MoD, Royal Society, European Commission funding, as well as direct investment from industry.  Our Informatics Research hub builds on the history, success, and achievements at the University of Salford over the last thirty years. Evolving around data and information, the centre covers all phases and processes from data pre-processing to engineering and visualisation.

Many students go on to further research in knowledge discovery and semantic web, software engineering and applications, big data, data mining and analytics, cyber security, information visualisation, and virtual environments

 

A taste of what you could become

A data scientist

A statistician

A business intelligence professional

A data analyst

A big data engineer

And more...

Career Links

Salford leads an industrial liaison committee to gain advice on our computing programmes and course content. Companies involved in this initiative include Web Applications UK, AutoTrader, Cooperative, DAI and FastWebMedia - a mixture of companies who rely on IT and data for their operations. This diversity ensures we understand industry needs from multiple perspectives and helps us to nurture graduates with strong employability and transferable skill sets.

Requirements

What you need to know

English Language Requirements

International applicants are required to demonstrate proficiency in English. An IELTS score of 6.0 (with no element below 5.5) is proof of this.

International Students - Academic Technology Approval Scheme (ATAS)

International students are required by the Home Office and/or the Foreign & Commonwealth Office (FCO) to apply for an Academic Technology Approval Scheme (ATAS) Certificate before they begin studies. To comply with Home Office regulations, you must obtain an ATAS Certificate before you come to the UK. Please refer to your offer conditions. 

Pre-Enrollment Attendance

As part of the course preparation, you will attend an intensive two-week session prior to the start. This session will review basic statistics and database concepts, plus an overview of either Python or R programming for data analysis. 

What qualifications do I need?

Entry requirements

  • The minimum requirement is a second class division honours degree or
  • Equivalent in any discipline, with previous demonstrable mathematical aptitude e.g. (A-level or BTEC Mathematics).

How much?

Type of study Year Fees
Full-time home/EU 2019 £7,776per year
Full-time international 2019 £14,310per year
Part-time 2019 £1,296 per 30 credits module
Additional costs

You should also consider further costs which may include books, stationery, printing, binding and general subsistence on trips and visits.

Apply now

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Enrolment dates

September 2019

September 2020