Postgraduate MSc/PgDip

Data Science

School of Science, Engineering and Environment





One year

Three year

Next enrolment

January 2022


In a nutshell

Make discoveries in data and bring value to businesses and organisations. With our Data Science postgraduate degree, you can enhance your existing computing knowledge, or gain new skills that can help you secure a role in one of the fastest-growing employment fields in the digital economy.

Developed as one of the UK's first data science postgraduate conversion courses, this Chartered Institute for IT (BCS) accredited degree will equip graduates and professionals from a wide range of disciplines, with analytical knowledge and applied programming skills.

Available with full and part-time study routes, the course will provide you with specialist data science knowledge to apply in a wide range of sectors, including healthcare, commerce, professional services and the built environment. Start dates are available each January and September.

You can learn more about studying data science, explore course modules and speak to the course team, by joining our next online Open Day.

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
  • Broaden your mathematical aptitude as you explore 'Big Data', machine learning and data visualisation

students accepted

Course accreditations
British Computing Society Accredited Degree logo

This is for you if...


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


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


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

Course details

All about the course

Computer code

Course delivery

The course is delivered through a range of highly-focused modules. The 180-credit MSc award comprises four taught modules, plus a research dissertation.  The 120-credit PgDip award comprises four taught modules.

Flexibility is at the heart of our learning approach. You can choose to study this postgraduate course full-time or part-time on campus, with start dates in January and September:

  • As a full-time student, you will complete taught modules in each of the first two trimesters, and complete your 60-credit dissertation project in trimester three
  • As a part-time student, you will complete taught modules in years one and two, and complete your dissertation project in year three

As part of your study preparations, you will be required to attend an intensive one-week session before formal teaching begins. This session will review basic statistics and database concepts, and provide an overview of either Python or R programming for data analysis. 

Learning experience

We've designed the course to upskill and empower graduates and professionals with limited prior subject knowledge so they are ready to take up exciting, data-driven career opportunities. Through our industry-focus, we invite speakers from major companies and employers to participate in course delivery, providing you with real-world case studies and projects.

Current course topics include big data analytics, advanced databases, applied statistics and data mining. We regularly review module content with our industry partners to ensure your acquired knowledge and skill set reflects trends and needs within professional and business communities.

Learn more about the current course modules in the section below.


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?


Practical projects




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.


  • 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.

School of Science, Engineering and Environment

Rising to the challenge of a changing world, our postgraduate courses are designed to shape the next generation of urbanists, scientists, engineers, consultants and industry leaders.

Shaped by industry, and delivered by supportive programme teams, you can develop the skills to take your career potential further.

Learning Environment

During your studies, you will experience a modern learning environment, enriched with accessible lecture theatres and AV-equipped classrooms, computing suites and multimedia libraries, with access to industry journals, databases, and simulation software.

As a data science student, you will use computing suites equipped with software platforms and languages in machine learning, data mining, and statistical analysis. These include SAS Enterprise Guide & Miner, Python, Apache Hadoop & Spark, RapidMiner, plus NoSQL databases, such as MongoDB.

Programme Team

The Data Science postgraduate programme is delivered by an academic team with extensive industry experience and research connections.

Industry Partnerships

The University of Salford is a key partner in both the Greater Manchester Cyber Foundry and the Greater Manchester AI Foundry.

The Cyber Foundry is a collaborative programme that provides support to regional SMEs with business growth, stability and security.

The AI Foundry is an innovative programme that provides support to SMEs to utilise Artificial Intelligence (AI) to become innovation ready, and enhance or develop new products and services.

Employment and stats

What about after uni?

Computer scientist


Demand for data scientists outstrips supply and there is continued demand for qualified professionals across many global industries. 

As a data science graduate, you’ll be equipped with the skill set and technical knowledge relevant for the data science and big data job market.

> Listen to graduate Priya talk about how her placement resulted in a new role

> Read how course graduate Gemma secured a role with the NHS

> Read how the live placement feature helped graduate Tom to secure a role at Channel 4


You might also choose to take your subject interest further with postgraduate research. The Salford Innovation and Research Centre (SIRC) is home to Informatics PhD and Research Master’s opportunities in knowledge discovery and semantic web, software engineering, big data, data mining and analytics, cyber security, information visualisation and virtual environments.

Explore our Doctoral School to learn more about research training, support and opportunities.

A taste of what you could become

A data scientist

A statistician

A data engineer

A data analyst

A machine learning 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.


What you need to know


This course is ideal for mathematics, computing or science graduates, and experienced professionals, eager to join the data storytelling revolution. 


All of our courses are taught and assessed in English. If English is not your first language, you must meet our minimum English language entry requirements. An IELTS score of 6.0 (no element below 5.5) is proof of this, and we also accept a range of equivalent qualifications.  

Read more about our English language requirements, including information about pathways that can help you gain entry on to our degree courses. If you do not have the English language requirements, you could take our Pre-Sessional English course


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


This degree is accredited by BCS, the Chartered Institute for IT. An accredited degree provides a valuable contribution towards professional membership and evidencing breadth of knowledge. Some employers give preference to applicants who have accredited degrees, and an accredited degree is likely to be recognised by other countries that are signatories to international accords.

Standard entry requirements

Undergraduate degree

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).

International student entry requirements

We accept qualifications from all around the world. Find your country to see a full list of entry requirements.

Alternative entry requirements

Accreditation of Prior Learning (APL)

We welcome applications from students who may not have formal/traditional entry criteria but who have relevant experience or the ability to pursue the course successfully.

The Accreditation of Prior Learning (APL) process could help you to make your work and life experience count. The APL process can be used for entry onto courses or to give you exemptions from parts of your course.

Two forms of APL may be used for entry: the Accreditation of Prior Certificated Learning (APCL) or the Accreditation of Prior Experiential Learning (APEL).

For more information or enquires about this scheme, please contact:  

Learn more about the Salford Alternative Entry Scheme.

How much?

Type of study Year Fees
Full-time home 2021/22 £8,640per year
Full-time international 2021/22 £15,030per year
Part-time 2021/22 £1,440 per 30 credits
Full-time home 2022/23 £8,820per year
Full-time international 2022/23 £15300per year
Part-time 2022/23 £1,470 per 30 credits
Additional costs

Having your own laptop (16GB of RAM and an Ethernet port) is not essential, but it will give you more flexibility in where and how you engage with the software you will need to use during your studies (software is provided as part of the course).

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

International student scholarships

If you are a high-achieving international student, you may be eligible for one of our scholarships. We offer a range of scholarships worth between £3,000-£5,000.

Learn more about our latest international scholarships.

Apply now

All set? Let's apply

Enrolment dates

January 2022

September 2022