Data science is rapidly emerging in organisations and the need for fully trained data scientists, analysts and programmers is in demand now more than ever. Businesses rely on big data to uncover hidden patterns and unknown correlations to make informed decisions and drive business success. On the Data Science course you will learn how to apply high level analytical skills and knowledge to solve a range of real-world problems.
The MSc Data Science is designed to support your development of transferable skills and expertise to play a leading role at a technical and practical level in the industry. Additionally, you will work towards a globally recognised qualification, the SAS Joint Certificate in Applied Statistics and Data Mining. The Joint Certificate will validate your skills and ability to use SAS software, giving you a competitive advantage in the job market.
The MSc Data Science has six core taught modules divided into two streams. At the beginning of each stream you will be taught the fundamentals of applied statistics, computing technology, programming and data base systems. Your existing analytical and technical skills will be developed to understand how to use the industry software used in the workplace, in turn preparing you for your individual research project.
Alongside these modules, you will complete an individual research project. Your project will be proposed and supported by local employers across a range of industries, including ONS Data Science Camps.
The MSc Data Science course is delivered through a series of lectures, practical classes and workshops where you will have the opportunity to put into practice what you have learnt via hands-on exercises and design projects. You will also be taught by a number of guest lecturers and have the option to visit workplaces.
The Data Science Masters offers a flexible approach to learning, allowing you to study full-time, part-time or through continuing professional development (CPD) for working professionals. The CPD route is an accessible pathway for employers to equip staff with further training opportunities to work towards a postgraduate qualification.
You will be taught by active researchers and leading professionals exposing you to current real-world problems, methodologies, and industry-standard techniques and software.
Full-time students will typically spend 12 hours in classes each week. For those studying part-time, this is reduced to six hours each week.
Each stream is delivered on a single day per week.
Stream One runs on a Wednesday and Stream Two on a Friday. Full-time students will undertake both streams in one year whilst a part-time student will start on Stream One in Year One and then move on to Stream Two in Year Two.
Students undertake one module at a time for each stream and each module within a stream runs across three consecutive eight-week blocks.
All students will also undertake a 60-credit individual project. Typically, a full-time student will work on the individual project from June-September.
A part-time student will have the opportunity to start work on their individual project during the first summer and will finish their individual project during the second summer completing by the September of Year Two.
Several modules are assessed entirely through coursework and some involve coursework and in-class examinations.
In industry, computing power is now vital to aid complex mathematical calculations on a daily basis. Therefore, throughout your Masters degree in Data Science, you will be exposed to a variety of key industry computer packages to facilitate your learning. You will also be taught sought-after programming skills.
Practice is so important in gaining understanding of complex data science techniques, which is why we have a dedicated Data Science computer laboratory and access to student workrooms dedicated to our mathematics students. These facilitate a learning environment where you can work individually or in groups and as they are located next to the staff offices you will have no problem finding help if you get stuck.
Suitable for graduates with a minimum 2:2 Honours degree or equivalent in a numerate discipline across any STEM or business subject. Relevant industry experience can also be taken into account for applicants without a relevant first degree.
The course also welcomes international applicants and requires an English level IELTS 6.5 or equivalent.
Full-time fees are per year. Part-time fees are per 20 credits. Once enrolled, the fee will remain at the same rate throughout the duration of your study on this course.
Find out how to pay your tuition fees in full or by payment plan.
This course is eligible under the Enhanced Learning Credits scheme for Ex-Armed Forces personnel.
International Scholarships are available for self-funding international students.
Please note the full-time UK and EU fee is £9000, however USW is offering a full-time UK and EU bursary of £3000 which means students pay £6000
Please note the part-time UK and EU fee is £1000, however USW is offering a part-time UK and EU bursary of £333 which means students pay £667 per 20 credits
Students have access to a wide range of resources including textbooks, publications, and computers in the University’s library and via online resources. In most cases they are more than sufficient to complete a course of study. Where there are additional costs, either obligatory or optional, these are detailed below. Of course students may choose to purchase their own additional personal resources/tools over and above those listed to support their studies at their own expense. All stationery and printing costs are at a student’s own expense.
You can apply for a postgraduate loan as a contribution towards your course and living costs.
Employment prospects are strong in this rapidly growing and demanding industry. There are a number of careers available to those trained in data science - students could go on to be data scientists, statistics officers, business analysts, predictive modellers or computer programmers, these will continue to grow as organisations are required to adapt to improving technologies.