{"id":6390,"date":"2024-03-10T11:35:41","date_gmt":"2024-03-10T11:35:41","guid":{"rendered":"https:\/\/engineering.tiu.edu.iq\/surveying-and-geomatics\/?p=6390"},"modified":"2024-06-24T11:48:53","modified_gmt":"2024-06-24T11:48:53","slug":"national-workshop-pca-multi-disciplinary-applications","status":"publish","type":"post","link":"https:\/\/engineering.tiu.edu.iq\/surveying-and-geomatics\/national-workshop-pca-multi-disciplinary-applications\/","title":{"rendered":"National Workshop PCA Multi-Disciplinary Applications"},"content":{"rendered":"
<\/div><\/div>

Principal Component Analysis- Multi-Disciplinary Applications<\/strong><\/p>\n

The Surveying and Geomatics Department<\/a>,\u00a0Faculty of Engineering<\/a>\u00a0at\u00a0Tishk International University<\/a> organized a National Workshop<\/strong> on Principal Component Analysis- Multi-Disciplinary Applications,<\/strong> on March 6th, 2024<\/span>. Professor Dr. Tariq H. Karim presented the workshop.<\/p>\n

In a comprehensive national workshop, experts gathered to explore Principal Component Analysis (PCA) as a robust tool for managing large sets of input variables in data analysis. The workshop aimed to equip participants with the knowledge to effectively reduce high dimensions and tackle the adverse effects of multicollinearity.
\nThe opening session delved into the fundamentals of PCA, offering a thorough understanding of the definitions and background of the technique. Participants learned about the crucial concept of axis rotation, which is essential for transforming data into principal components.<\/p>\n

The second session provided a step-by-step guide to performing PCA using matrix operations. Attendees were introduced to the Scree plot, a graphical method to determine the number of significant components, and the biplot, a valuable tool for visualizing relationships among variables.<\/p>\n

By the end of the workshop, participants gained a solid grounding in the basics of PCA. They learned how to shrink large dimensions, effectively avoid multicollinearity, and use biplots to detect and interpret relationships among variables.<\/p>\n

This workshop has empowered attendees with practical skills and knowledge to enhance their data analysis capabilities using PCA, promising advancements in various fields requiring sophisticated data management techniques.<\/p>\n<\/div>