- Study a course that is unique in the UK and has been specifically developed to meet the skills gap. Course content can be applied to very diverse fields- there are many job opportunities in this area.
- Gain SAS certification.
- Learn to tell a story from data. Become immersed in Big Data techniques and platforms, working with real-world messy data to gain experience across the data science stack.
- Part-time study option
- International students can apply
Have you ever wanted to ‘Mung’ data? Apply Machine Learning techniques? Search for hidden patterns? Be part of Big Data?
This course is your opportunity to specialize as a Data Scientist, one of the most in-demand roles across all sectors including health, retail, and energy. Companies such as Google and Microsoft, and also public organisations such as the NHS are struggling to fill their vacancies in this field due to a lack of suitably qualified people. This course is unique in the UK in that it has been developed as an MSc conversion course – if you have a good honours degree in any discipline with a demonstrable mathematical aptitude, an enquiring mind, a practical and analytical approach to problem-solving,andan ambition for a career in data science; then this course is for you.
During your time with us, you will develop an awareness of the latest developments in the fields of Data Science and Big Data including advanced databases, data mining and big data tools such as Hadoop. You will also gain substantial knowledge and skills with the SAS business intelligence software suite due to the partnership of the University with the SAS Student Academy.
"We are especially pleased to endorse the new MSc in Data Science. With the explosion of interest and investment in data science teams, our customers cannot get enough graduates with SAS-based analytical skills. Courses such as this new MSc are an important step forward by the University to addressing this skills shortage, especially amongst home students." - SAS
This course covers a very comprehensive range of topics split into four large modules worth 30 credits each plus the MSc Project worth 60 credits. External speakers from blue-chip and local companies will give seminars to complement your learning, that will be real-world case studies related to the subjects you are studying in your modules. These are designed to improve the breadth of your learning and could lead to ideas that you can develop for your MSc Project.
Before you begin this course, you will attend an intensive week-long session, which will prepare you for the course by introducing basic statistic and database concepts, as well as an overview of either Python or R programming for data analysis.
Principles of Data Science (30 Credits)
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 (30 Credits)
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 (30 Credits)
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 (30 Credits)
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 SocialMedia.Additionally, you will gain experience in data visualisation tools and techniques.
MSc Project (60 Credits)
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 arobust,scalable and usable solution.
- The minimum requirement is a second class division 2 Honours degree or
- Equivalent in any discipline, with previous demonstrable mathematical aptitude e.g. (A-level or BTEC Mathematics).
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).
English Language Requirements
International applicants will be required to show a proficiency in English. An IELTS score of 6.0 (no element below 5.5) is proof of this.
Students who want to become trained professionals in:
- Data Science and Analysis Consultancy
- Implementing and designing Big Data platforms ie Data Warehouses, Hadoop, NoSQL databases
- Modelling and Visualisation of data
- Full-time: £7,560
- Part-time: £1,260 per 30 credit module
- Full-time International: £13,860
You should also consider further costs which may include books, stationery, printing, binding and general subsistence on trips and visits.
The course is focused on the underpinning knowledge and practical skills needed for employment within the data sciences industry. There will be 22 hours of lectures; 11 hours of tutorials and 22 hours workshops; 2 hours of examination-based assessment; and 245 hours of independent study, assessed coursework and preparation for an examination. This makes a total of 300 hours total learning experience.
- Lectures will be used to introduce ideas, and to stimulate group discussions.
- Tutorials will be used to develop problem-solving strategies and to provide practice and feedback with scenarios to help with exam preparation.
- Workshops will be used to develop expertise in SAS tools, by analysing example datasets of increasing complexity.
- 50% of the assessment will comprise a practical project where students will be given some data, will devise and carry out an analysis strategy and will present their interpretations and explain their strategy.
- 50% will comprise an examination, which will assess more theoretical aspects of the course and will explore students’ immediate response to unseen scenarios or data.
A recent report by e-Skills and SAS (Big Data Analytics: An assessment of the demand for labour and skills, 2012-1017) indicates the demand forecast for staff with big data skills is predicted to ”rise by 92% between 2012 and 2017, and by 2017 there will be at least 28,000 job openings for big data staff in the UK each year…”
With this qualification, you’ll be equipped with the skill set and technical knowledge relevant for the data science and big data job market.
The Informatics Research Centre in the School of Computing, Science and Engineering at the University of Salford builds on the history, success and achievements of the research in Computer Science and Information Systems developed at the University of Salford over the last thirty years.
Evolving around Data and Information in all their types and usages, the Centre covers all phases and processes from data pre-processing to engineering and visualisation. The Centre is developing novel methods and systems for the analysis and recognition of various data sets, learning behaviours and causal models. The techniques and systems developed have a wide range of potential applications including digitisation of historical documents, medical diagnosis, semantic tagging, segmentation of types of viewers and their behaviours, text mining and retrieval and data visualisation.
Forensic computing, digital investigation and Cybersecurity is another area of expertise supported by the centre both at the theoretical and application levels.
Many students go on to further research in the fields of:
- Actionable Knowledge Discovery and Semantic Web
- Software Engineering and applications
- Big Data, Data Mining and Analytics
- Image and document processing and analysis
- Cyber Security and Forensics
- Information visualisation and virtual environments
Facilities include a new Dell Cloud Computing platform with OpenStack and lab workstations, providing access to software platforms and languages specialized in Machine Learning, Data Mining, Statistical Analysis and Big Data including:
- R, SAS Enterprise Guide & Miner, Python, Apache Hadoop & Spark, RapidMiner
- NoSQL databases ie MongoDB
This school offers programs in:
Last updated April 10, 2018