Data science scholarship
Give your career a ‘big data' focus with a fully funded, short course development opportunity.
Predicting the shape of economic recovery in Scotland post-Covid is difficult, but as the University for the Common Good, we believe that collaboration to build a sufficiency of data skills and support data-driven innovation is critical.
One hundred and seventy-five zettabytes of data are forecast to exist worldwide by 2025, ballooned by the growing number of mobile devices and sensors. As the world becomes more data driven, future-focused business analysts, developers, tech leaders and engineers need to stay relevant and build cross-disciplinary data skills. Big data techniques are revolutionising how organisations acquire and analyse data, offering valuable insights into how people live, work, play, travel, shop and exercise. These insights are valuable to marketers, researchers, designers, city planners, app developers, educators and many more.
Whether you seek to grow your data science knowledge and skills, enhance your career prospects or inform your organisation’s decision making and strategy in your current role, this course will give you access to selected modules at masters level to help you achieve this. Supported by the Scottish Funding Council, GCU has flexible learning scholarships for up to 40 candidates and invites competitive applications to secure a place on this fully online, data science upskilling course. GCU would particularly like to support candidates seeking to upskill, mobilise their careers or those at risk of unemployment.
Entry requirements and module details
These SFC-funded scholarships are designed to introduce learners to object-oriented programming, the programming language Python and its use for data programming and analytics.
Learners will build depth of knowledge in software programming skills and the management of data throughout its life cycle.
Candidates have the opportunity to study Masters level modules:
- Software Development in Data Science – (20 Credits at SCQF 11 )
Modules are accredited and subject to assessment. Successful completion of each module will earn the learner portable academic credit. Portable academic credit may be used at a later stage towards a structured award at masters level, for example, GCU’s MSc Big Data Technologies.
These short courses require no prerequisite knowledge; a basic understanding of matrix algebra and statistics would be advantageous. No prior experience of programming is required but learners should expect a swift learning pace. Learners without programming experience should expect to invest more time in study.
Get in touch
If you are interested in this upskilling programme or have any further questions please do not hesitate to get in touch with Andrew.Campbell@gcu.ac.uk
