School of Science, Engineering and Environment
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, our 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.
International applicant? Please check international intakes for the latest information and application dates.
Start your MSc Data Science study journey
Register for our next Open Day where you can learn more about the course, tour our impressive new computing suites and meet the tutors
- 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
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
All about the course
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.
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.
The Data Science postgraduate course is delivered by an academic team with extensive industry experience and research connections, and a track record of developing solutions through industry partnerships.
Course leader: Professor Mo Saraee
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.
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.
Machine Learning and Data Mining
In this module, you will explore the full data mining lifecycle, from data pre-processing and exploratory data analysis to the application and evaluation of supervised and unsupervised machine learning algorithms. Through practical, hands-on workshops using Python and Microsoft Azure Machine Learning, you will gain experience at using machine learning and data mining tools and techniques to extract insights from data.
Applied Statistics and Data Visualisation
This module will equip you with a strong foundation in applied statistics which you need to become a successful Data Scientist. You will cover descriptive and inferential statistics, including regression, hypothesis testing and time series analysis and will learn how to use programming languages like R to solve statistical problems.
In addition, you will also be introduced to the theory and practice of data visualisation and will learn to use platforms such as Power BI and Tableau to create dashboards, reports and data stories.
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?
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.
Data Science is a very interesting course that requires a lot of dedication and creativity. To successfully complete the course, it is advisable to do additional research and lots of personal learning to get a whole rounded experience. For students looking to get jobs in the UK, this course is exactly what you need as lots of opportunities come up immediately!
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 leaders.
Shaped by industry, and delivered by supportive programme teams, you can develop the skills to take your career potential further.
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.
Industry collaboration and research
When you start this degree course with Salford, you are joining a community making a difference in industry, our local region and in our wider society.
Many of our academics and technicians who support your course also lead collaborative, interdisciplinary, high-impact work in a range of local and global computing and informatics issues and challenges.
Discover how you are part of something bigger.
What about after uni?
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.
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.
ENGLISH LANGUAGE REQUIREMENTS
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.
Please check international intakes for the latest information and application dates.
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.
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.
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: AdmissionsSEE-PGT@salford.ac.uk
|Type of study||Year||Fees|
|Full-time home||2023/24||£9,090per year|
|Full-time international||2023/24||£15,750per year|
|Part-time||2023/24||£1,515 per 30 credits|
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. Learn more about our latest international scholarships.