Stream: Virtual Room 7
Time: 13:45 - 14:30
Most data science students spend their time working with open-source tools, cloud platforms, and classroom-sized datasets. I was no different. Enterprise systems rarely come up in lectures, and mainframes are often dismissed as old or irrelevant. What I didn’t realize at first was that some of the world’s most critical, high-volume, and high-value data runs on IBM Z, supporting industries like banking, insurance, and healthcare every single day. In this session, I share my journey as a data science student who unexpectedly discovered IBM Z and enterprise systems. I talk honestly about my initial assumptions, what surprised me, and how my understanding of data at enterprise scale changed. The session explores where IBM Z fits in today’s data ecosystem, how analytics and AI are used in real-world enterprise environments, and why mainframes continue to matter. Attendees will walk away with a more realistic picture of enterprise data and a clearer view of how IBM Z skills can create unique learning and career opportunities for data science students.
I am a postgraduate student in Data Science and Analytics at the University of Hertfordshire, with a strong passion for bridging traditional enterprise systems and modern data technologies. I have hands-on experience in Python, R, and Tableau, and have developed projects in data visualization, machine learning, and analytics using real-world datasets, including weather prediction, spam detection, and credit card fraud analysis. As an IBM Z Student Ambassador and dedicated mainframe enthusiast, I actively explore complex enterprise systems, using storytelling and visual insights to make them accessible. I have participated in multiple tech events and workshops, including the IBM Z Xplore learning platform, hackathons at the University of Hertfordshire, and the WAVEZ virtual conference, where I collaborated with peers to solve real-world challenges. Beyond academics, I serve as a student representative for my department, mentoring peers and supporting collaborative projects. I am committed to sharing knowledge, empowering emerging professionals, and inspiring others to embrace data science and enterprise computing.
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