Data Science and Analytics

Successfully continuing its academic activities since our university's founding in 2010, our department has carried its vision into the future by being renamed "Data Science and Analytics" as of 2026, aligned with today’s rapidly evolving technological dynamics and advancements in artificial intelligence and the big data ecosystem.

Operating under the Faculty of Engineering and Natural Sciences, our department serves as a multidisciplinary bridge that combines the theoretical strength of classical statistics with modern computational methods and advanced analytical tools.

Data science is a multidisciplinary field that integrates statistics, mathematics, computer science, and domain expertise to extract meaningful insights, patterns, and value from complex structured and unstructured data. Data analytics, on the other hand, goes beyond merely describing historical data to encompass methods for forecasting future outcomes (predictive analytics) and determining optimal strategic actions under uncertainty (prescriptive analytics).

In an increasingly digitized world, data has become the essential driver of innovation and strategic decision-making. Across diverse sectors ranging from healthcare and industry to finance and natural sciences, managing massive data streams to:

• Develop statistical models that mitigate uncertainty,
• Uncover hidden dynamics within complex systems using machine learning and AI algorithms,
• Drive rational, optimized, and data-driven decisions,

is the most critical imperative for contemporary scientific research and sustainable institutional success.

While our department does not currently admit undergraduate students directly, it undertakes the core and advanced-level courses in statistics, data analytics, data mining, and research methodology required across all faculties, departments, and research centers of our University. We aim to equip our students and researchers not only with theoretical knowledge, but also with a holistic analytical perspective that critically questions, models, accurately interprets, and ethically translates data into action.