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Basic Course Offering (Virtual, Group Format):
This 3-day course covers the fundamentals and applications of cluster analysis, providing participants with an understanding of clustering algorithms and their use in real-world datasets. Topics will include hierarchical and partitioning methods, fuzzy clustering, and model-based clustering approaches. The course will also introduce advanced topics such as regularized clustering with L0 penalties and hybrid methods for mixed data (numerical and categorical), with hands-on sessions in MATLAB. Other practical applications will be covered using R.
The "Cluster Analysis: Techniques and Applications" workshop, covering foundational and advanced clustering methods, has a broad range of applications across multiple fields. Here are some of the primary applications and uses for the knowledge gained from such a workshop:
Day 1: Introduction to Cluster Analysis and Core Techniques
Day 2: Fuzzy Clustering and K-Prototype Methods
Day 3: Advanced Topics and Applications to Large Datasets
Marco Forti completed his Bachelor and Master studies in Economics at the University of Rome “La Sapienza,” in Italy, where he also received his Ph.D. in Statistics in 2022. He also worked in several public and private institutions and research centers as SviMez, Agenas (Italian Ministry of Health), CER, KPMG international, Deloitte, etc., leading theoretical and applied research in the fields of statistics, economics, policy evaluation, epidemiology, and data management.
Financial Analysts and Economists
Clustering methods are essential for segmenting financial datasets, performing time series analysis, and grouping economic data patterns. Financial professionals will benefit from techniques like time series clustering and large-scale data clustering covered in the workshop.
Researchers in fields like genomics, epidemiology, and medical imaging can gain practical clustering skills for gene expression analysis, patient data segmentation, and medical image processing, making the advanced topics highly relevant.
Professionals in data science who need to apply clustering techniques for customer segmentation, market analysis, anomaly detection, or image processing will find value in this workshop, especially with hands-on practice on real-world datasets.
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