Today, we are witnessing a paradigm shift as technological advancements generate data in unprecedented volume, velocity, and variety ranging from structured to unstructured, wide to long, and everything in between. In an increasingly interconnected world where information is power, data has become the driving force behind innovation, progress, and decision-making. Data-driven solutions present immense opportunities to address challenges and unlock new possibilities across different sectors including industries, academia, and governance.  As organizations and societies transition to a more data-driven paradigm, the role of statistics and data science in deriving actionable insights from data has become inevitable and more crucial than ever.  The ongoing data revolution also demands changes in approaches to applied statistics and data science education by improving the curriculum to ensure that students acquire the indispensable skills needed to develop data-driven solutions, address challenges in the modern data landscape, and thrive in the data-centric world. 

Keeping this in mind, the Institute of Statistical Research and Training (ISRT), University of Dhaka, welcomes you to attend the International Conference on Applied Statistics and Data Science 2025 (ICASDS-2025), with the theme "Data-driven Solutions: Unlocking New Possibilities". The conference aims to bridge the gap between education and emerging industry demands, developing collaboration among educators, researchers, industry leaders, and related stakeholders, and fostering discussions on how to empower the next generation with the necessary tools and knowledge to harness the power of data effectively. Researchers, academicians, and representatives from business, industry, government, and non-government organizations are invited to participate in the event.

The main objectives of the conference are to

1. Promote Knowledge Sharing

   • Facilitate the exchange of ideas, research findings, modern data-driven solutions, and best practices among statisticians, data scientists, and domain experts.

2. Highlight Emerging Trends and Address Data Challenges

   • Showcase the latest advancements in statistical methods, machine learning, and data science to address real-world challenges.

   • Explore innovative approaches to managing the volume, variety, and complexity of modern data while ensuring data quality and integrity.

   • Highlight real-world applications of data science and its impact on solving practical challenges in everyday life and various industry domains.

   • Offer hands-on workshops, tutorials, and sessions to enhance the technical skills of participants.

3. Redefine Statistics and  Data Science Education Integrating Modern Tools and Technologies

• Enhance curricula by embedding contemporary programming languages, machine learning frameworks, and cutting-edge teaching methodologies that seamlessly blend theoretical foundations with practical, hands-on applications.

• Explore educational programs and training initiatives aimed at preparing professionals to tackle the evolving challenges posed by emerging technologies.

4. Bridge the Gap Between Academia and Industry  

   • Foster dialogue between educators and industry leaders to align curriculum development with the skills needed in industry, ensuring students are job-ready.

   • Connect professionals, researchers, and students from diverse industries and academia.

5. Foster Interdisciplinary Collaboration

   • Encourage collaboration among academia, industry, and government to develop data-driven solutions for critical problems across various sectors.

   • Develop collaboration between statisticians and researchers in other disciplines (e.g., biology, engineering, social sciences, healthcare, business) to apply data science techniques across diverse sectors.

6. Highlight Ethical Data Utilization

   • Address the importance of ethical considerations, data privacy, fairness, and transparency discussing frameworks and best practices for responsible data usage.

   • Educate participants on responsible data handling and usage.

7. Encourage Diversity and Inclusion

• Highlight the importance of diversity in the data science field and relate it with several SDG targets.

• Provide support and opportunities for underrepresented groups to participate in data science.

Scope of the Conference

The conference invites papers in all areas of statistics including the following: artificial intelligence, agricultural statistics; actuarial science; Bayesian methods; big data analytics; blockchain in data science; business analytics, bioinformatics; biostatistics; categorical data analysis; causal inference; clinical trials; data engineering; data mining; data science; data science cross-disciplines; data visualization tools; deep learning; demography; design of experiments; econometrics; environmental statistics; epidemiology; health care; image processing; impact evaluation; industrial statistics; longitudinal data analysis; machine learning, mathematical statistics; meta-analysis; medical statistics; multivariate methods; natural language processing; neural networks; nonparametric methods; official statistics; operations research; pharmaceutical statistics; predictive analytics; public health; quality control; resampling methods; sampling techniques; semi-parametric methods; signal processing; spatial and spatio-temporal modeling; statistical computing; statistical genetics; statistical measures of poverty; statistical modeling; sustainable development goals, stochastic models; survival analysis; time series analysis.