In an era increasingly defined by an explosion of data, coupled with the transformative power of artificial intelligence, data science has emerged as a pivotal field driving innovation and insights across every sector. This event will bring together researchers, practitioners, and industry leaders to explore the latest advancements, challenges, and opportunities in the field, recognizing the crucial role big data and advanced AI techniques play in shaping its trajectory.

We invite submissions that delve into, but are not limited to, the following key areas:

Advanced AI and Machine Learning Integration: Explore novel applications and methodologies where AI and machine learning are deeply embedded in data science workflows. This includes the use of generative AI, sophisticated natural language processing (NLP), and advancements in automating tasks like data cleaning, feature engineering, and model building.
Automated Machine Learning (AutoML): Submissions on the development, application, and impact of AutoML tools are highly encouraged. We are interested in research that addresses how AutoML is increasing efficiency, democratizing machine learning, and making it accessible to a wider range of users, including domain experts.
Cloud and Edge Computing in Data Science: We welcome papers discussing the continued role of cloud computing for scalable data storage, processing, and model deployment, as well as the growing significance of edge computing for real-time data analysis in applications like autonomous vehicles and IoT devices.
Big Data Technologies and Analytics: Submissions exploring the infrastructure, frameworks, and techniques required to manage and analyze massive, high-velocity datasets. Topics of interest include distributed processing (e.g., Spark, Hadoop), scalable data warehousing, real-time data streaming technologies, and data lakes.
Business Analytics and Intelligence: Papers focusing on the application of data science techniques to solve business problems. This includes descriptive, predictive, and prescriptive analytics, performance metrics (KPIs), data visualization, dashboarding, and strategies for translating data insights into actionable business decisions.
Risk Management and Analytics: We welcome papers on the application of data science and predictive modeling to identify, assess, and mitigate risks. This includes areas such as financial risk (e.g., credit scoring), operational risk, fraud detection, and cybersecurity threats.
Data Science for Sustainability and ESG: We seek papers on the use of data science, AI, and analytics to address environmental, social, and governance (ESG) challenges. This includes topics like climate modeling, sustainable resource management, supply chain transparency, and societal impact analysis.
Data Democratization: Papers focusing on strategies and technologies that promote making data and analytics tools more accessible to a wider range of users, fostering a data-driven culture within organizations.
Data Governance: Submissions on policies, processes, and technologies for managing data assets effectively, ensuring data quality, compliance, and security.
Data Privacy and Ethics: We invite research addressing crucial considerations related to data privacy, including compliance with regulations (e.g., GDPR, CCPA), anonymization techniques, and secure data handling. This also encompasses broader ethical considerations in data science, such as bias detection, fairness in algorithms, and responsible AI practices.
Geospatial Data Science: Submissions exploring the use, analysis, and visualization of geospatial data, including applications in urban planning, environmental monitoring, logistics, and location intelligence, are highly encouraged.
Augmented Analytics: We seek contributions that showcase innovations in augmented analytics, where AI and machine learning enhance and automate the data analysis process, leading to more accessible insights, automated data cleaning, feature engineering, and report generation.
Data Science in Education: Submissions focusing on learning analytics, educational data mining, personalized learning paths, and the use of AI to enhance teaching, student engagement, and institutional effectiveness.
Emerging Trends in Data Science: We are also keen to receive papers on cutting-edge and future-looking topics, such as:
Quantum Computing's impact on data processing and complex problem-solving.
Data Fabric architectures for integrated data management across diverse environments.
XOps (including MLOps) for comprehensive lifecycle management of machine learning models.
The increasing demand for actionable data that directly informs business decisions.
The burgeoning field of decision intelligence, leveraging data and AI to improve decision-making processes.