International Conference on Big Data Analytics and Statistical Applications

ICBDASA

7th Oct – 8th Oct 2026 Tauranga, New Zealand

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Registration Summary

Conference ICBDASA

Date 7th Oct – 8th Oct 2026

Location Tauranga, New Zealand

Participation Type Listener

Organizer Science Leagues

Registration Mode Standard / Physical

Registration Fee USD 0.00

Bank Convenience Charges (5.8%)i USD 0.00

Calculated based on 5.8% of the registration fee

Discount USD 0.00

Total Payable USD 0.00

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Conference Session Tracks

Aligned with UN Sustainable Development Goals

The conference's session tracks effectively support the following SDGs.

01 Advanced Statistical Methods in Big Data +

This track focuses on innovative statistical methodologies tailored for big data contexts. Participants will explore techniques that enhance data interpretation and decision-making processes.

SDG 4 SDG 9

02 Machine Learning Techniques for Data Analysis +

This session will delve into the application of machine learning algorithms in data analysis and predictive modeling. Emphasis will be placed on practical implementations and case studies.

SDG 4 SDG 9

03 Predictive Modeling in Complex Systems +

This track examines the development and application of predictive models in various complex systems. Attendees will discuss the challenges and solutions in forecasting outcomes using statistical techniques.

SDG 9 SDG 11

04 Artificial Intelligence in Statistical Applications +

This session explores the intersection of artificial intelligence and statistical applications. Participants will analyze how AI can enhance statistical modeling and data analysis.

SDG 4 SDG 9

05 Data Mining Techniques for Big Data Insights +

This track will cover advanced data mining techniques that facilitate the extraction of meaningful insights from large datasets. Discussions will include methodologies and tools that support effective data mining.

SDG 9 SDG 11

06 Regression Analysis in Big Data Environments +

This session focuses on the application of regression analysis techniques in the context of big data. Participants will explore various regression models and their effectiveness in real-world scenarios.

SDG 9

07 Clustering Algorithms for Data Segmentation +

This track will investigate clustering algorithms used for data segmentation and pattern recognition. Attendees will learn about the latest advancements and applications in clustering techniques.

SDG 9

08 Data Analytics for Decision Support Systems +

This session emphasizes the role of data analytics in enhancing decision support systems. Participants will discuss methodologies that improve data-driven decision-making processes.

SDG 4 SDG 9

09 Simulation Techniques in Statistical Research +

This track will explore the use of simulation techniques in statistical research and analysis. Participants will discuss the benefits and challenges of implementing simulations in various fields.

SDG 4 SDG 9

10 Quantitative Methods in Data Science +

This session will focus on quantitative methods that underpin data science practices. Participants will explore statistical techniques that enhance data analysis and interpretation.

SDG 4 SDG 9

11 Optimization Techniques in Big Data Analytics +

This track examines optimization techniques that improve the efficiency of big data analytics. Discussions will include algorithms and methodologies that enhance performance in data processing.

SDG 9 SDG 11