Data-Driven Dialogue: International Statistics and Data Science Conference at DU

An International Conference on Applied Statistics and Data Science was held at Nawab Nawab Ali Chowdhury Senate Bhaban of the University of Dhaka under the theme “Data-Driven Solutions: Unlocking New Possibilities.” Organised by the Institute of Statistical Research and Training (ISRT), University of Dhaka, the two-day conference took place on December 28–29, bringing together academicians, researchers, policymakers and professionals from home and abroad.

The conference focused on recent advances in applied statistics, data science and analytics, highlighting the effectiveness of data-driven approaches in addressing contemporary challenges across various sectors.

The inaugural session was attended as the chief guest by Professor Dr Mamun Ahmed, Pro-Vice-Chancellor (Academic) of the University of Dhaka. In his address, he underscored the growing importance of statistics and data science in evidence-based decision-making, policy formulation and sustainable development. He noted that although the use of data is increasing in governance, healthcare, education and economic planning, its responsible application requires strong analytical foundations, ethical considerations and contextual understanding.

Special guest Professor Dr Ahmed Mushtaq Raza Chowdhury, Professor of Population and Family Health at Columbia University, USA, and Trustee of BRAC University, emphasised the role of data-driven research in tackling social inequality and public health challenges in developing countries.

The session was chaired by Dr Tamanna Howlader, Director of the Institute of Statistical Research and Training. She stated that one of the key objectives of the conference was to strengthen collaboration among academia, industry and policymakers, and to bridge statistical and data science education with global demands.

The conference featured keynote speeches, technical paper presentations, panel discussions and interactive sessions. Topics included statistical learning, machine learning, data analytics, computational methods and the application of data science to real-world problem-solving. Participants actively engaged in discussions on methodological innovation, ethical use of data and interdisciplinary collaboration.

DBTech/MUM/OR