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ICSAMDS · Registering as Listener

International Conference on Statistical Analysis and Modeling in Data Science

18th Dec – 19th Dec 2026 Kobe, Japan Standard / Physical Participation

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ConferenceICSAMDS

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Conference Session Tracks

SDG-Aligned Research Themes

International Conference on Statistical Analysis and Modeling in Data Science conference tracks support global knowledge exchange, innovation, and sustainable development priorities across diverse disciplines.

SDG 9 - Industry, Innovation and Infrastructure

SDG 11 - Sustainable Cities and Communities

This track focuses on the latest advancements in statistical modeling techniques applicable to data science. Participants will explore methodologies that enhance predictive accuracy and model interpretability.

This session will delve into the practical applications of various machine learning algorithms across different domains. Attendees will discuss case studies that highlight the effectiveness of these algorithms in real-world scenarios.

This track emphasizes the role of quantitative methods in analyzing complex datasets. Presentations will cover innovative approaches to data interpretation and decision-making processes.

Focusing on predictive analytics, this session will examine techniques that transform data into actionable insights. Participants will share applications across industries that demonstrate the power of predictive modeling.

This track will explore various simulation techniques used in statistical analysis and their applications in data science. Discussions will include the benefits and limitations of simulation in model validation and hypothesis testing.

This session will highlight innovative data mining methods that uncover hidden patterns within large datasets. Participants will engage in discussions about the implications of these methods for data-driven decision-making.

This track focuses on the application of statistical principles in the field of data science. Presenters will share insights on how applied statistics can inform and enhance data-driven strategies.

This session will cover both the theoretical foundations and practical applications of regression analysis. Attendees will discuss various regression techniques and their relevance in predictive modeling.

This track will investigate various classification techniques utilized in machine learning. Participants will analyze the effectiveness of these techniques in different contexts and datasets.

This session will focus on computational statistics and the tools that facilitate complex statistical analyses. Participants will explore software and programming techniques that enhance statistical modeling capabilities.

This track will highlight emerging trends and future directions in data science research. Presenters will discuss innovative methodologies and their potential impact on the field of mathematics and statistics.