Doha, Qatar (29 May 2025) - SAS, a global leader in data and AI, in partnership with the University of Doha for Science and Technology (UDST), has launched a three-day Forecasting Hackathon bringing together more than 50 talented students from the College of Computing and Information Technology to tackle real-world challenges using SAS advanced data analytics and machine learning.

“Hackathons present the perfect opportunity to give students real-life use cases, allowing them to glimpse the work they could be doing tomorrow if they pursue careers in analytics and AI. This initiative prepares them to enter the workforce and bridges the gap between academic output and private-sector needs,” said Nader Kassir, Regional Director Upper Gulf at SAS.

Dr. Zakaria Maamar, Dean of the College of Computing and Information Technology at UDST said: “Such initiatives support applied learning and empower students with cutting-edge tools and techniques that nurture their talent in analytics and AI.  By engaging our students in real-world challenges through initiatives like the SAS Forecasting Hackathon, we are not only enhancing their practical skills but also empowering the next generation of innovators to contribute meaningfully to Qatar’s vision for a sustainable and knowledge-driven economy. This collaboration between academia and industry exemplifies our commitment to fostering talents that are ready to lead in the digital economy and address the complex challenges facing our society.”

The hackathon focused on four critical BFSI , Renewable Energy, Sustainability, Workforce, and Logistics, giving students a unique opportunity to apply forecasting techniques to pressing global and regional issues. The initiative demonstrated the potential of youth-led innovation in shaping resilient and data-driven solutions that will enable better and more sustainable future.

The hackathon was kicked off with a hands-on workshop on using SAS Visual Forecasting, a powerful low-code/no-code platform that allowed students to quickly prototype forecasting solutions using industry-relevant sample data.

Each project was evaluated by an expert jury based on how effectively the model addressed the business problem and provided actionable insights. Visualization was used to clearly present model performance, the application of innovative techniques, and the stability and accuracy of the forecasts. Rigorous evaluation ensured that the winning teams excelled across technical, analytical, and creative dimensions. 

1st Place: Team 'Pip Install'
Case: A Data-Driven Platform for Future-Ready Career Decisions
An all-women team, Team 'pip install' took the top spot with a forward-thinking platform designed to help university students and academic advisors make smarter, more informed career decisions. Teams goal was to bridge the gap between academic paths and real-world labor market trends.

Focusing on university students, career services, academic advisors, university leadership, and policy makers, winning team created platform, using open data from data from Qatar’s National Planning Council, that can empower institutions and individuals with actionable insights to align education with evolving job market demands.

 2nd Place: Team 'SolarIntel'  

Case: AI Powered Solar Prediction

Solarintel focused on tackling one of the key challenges in renewable energy: the unpredictability of solar power generation due to weather volatility. Their solution leveraged AI and machine learning models trained on historical irradiance and weather data to significantly improve short-term solar forecasts. By enhancing forecast accuracy, their approach helps reduce battery wear, minimize reliance on backup generators, and avoid financial penalties in energy markets—ultimately contributing to more efficient, reliable, and sustainable energy systems.

3rd Place: Team 'HackaForecast'
Case: Transit Traffic Dynamics – A Comparative Study of Airports

Team HackaForecast showcased a powerful forecasting solution aimed at helping airports enhance operational efficiency and strategic planning. Addressing the challenge of inaccurate transit passenger forecasts, which often lead to staffing, resource allocation, and capacity management issues, the team utilized SAS Viya Forecasting to build high-accuracy traffic models. They applied techniques such as ARIMA, exponential smoothing, and seasonal decomposition to generate reliable forecasts. Their solution enables data-driven airport management, reduces terminal congestion, optimizes resource use, and supports growth in transit passenger volumes.

Aligning to the Vision of Qatar’s Future

The initiative reflects the broader goals of Qatar National Vision 2030 and the Third National Development Strategy (2024–2030), which place education and youth empowerment at the center of the nation’s transformation. With a strategic focus on integrating digital learning tools, fostering skills in AI, STEM, and sustainability, and preparing youth for future labor market demands, programs like this hackathon play a vital role in shaping Qatar’s knowledge economy.