Welcome to the Responsible Data Analytics Hub, your central platform for all things related to data analytics with a focus on responsibility and inclusivity. Whether you’re a practitioner, an enthusiastic instructor, or a curious student, this hub is designed to be your go-to resource for knowledge, collaboration, and growth in the field of data analytics.
Our primary objective is to foster a community where ethical and responsible data practices are considered in all aspect of data analytics. To support this goal, we have developed a postgraduate degree program in Responsible Data Analytics. This postgraduate degree is tailored to equip students with the skills and ethics necessary to navigate the complex landscape of data analytics while prioritizing societal well-being.
In line with our commitment to inclusivity, we actively encourage women and individuals from all backgrounds to consider this program. We believe diversity strengthens our collective understanding and approach to data analytics, ensuring that our initiative is not only effective but also equitable. Additionally, we welcome instructors with academic expertise and encourage collaboration with employers, including companies and ICT professionals, to ensure that our curriculum remains relevant and impactful in the ever-evolving field of data analytics.
Join us in shaping the future of responsible data analytics.
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About The Project
A joint project between two prestigious institutions: Northumbria University in the UK and the Arab Academy for Science, Technology & Maritime Transport (AASTMT) in Egypt. Funded by the British Council, this collaboration is set to design and develop a TNE postgraduate degree program in the field of Responsible Data Analytics, a discipline poised to drive economic and social growth. The design of the programme will be based on careful exploration of critical success factors for a TNE activity. The project will engage with salient stakeholders in both countries through interviews, questionnaires, and focus groups to facilitate the efficient identification of such factors.