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PCA for XLSTAT: Basic Principles and Practical ApplicationsFree webinar |
English
50 minutes
In this XLSTAT webinar on Principal Component Analysis (PCA), we will explore the basics and practical applications of PCA, a powerful technique for analyzing multidimensional data. Learn how to extract key information from your datasets, reduce their dimensionality while preserving their structure, and visualize relationships between variables and/or observations. Whether you're a beginner or experienced in using XLSTAT, this webinar will help you master the fundamentals of PCA and discover how to integrate it into your analytical projects. RECORDING will be sent to all who register.
The basics and practical applications of Principal Component Analysis
Learn how to extract key information from your datasets
Senior consultant in statistics, Amaury Labenne holds a PhD in applied mathematics. While writing his doctoral thesis on dimension reduction methods, he taught statistics and their uses at university. In 2016, Amaury joined the Addinsoft R&D team, of which he was then in charge until 2020. He participated to the development and improvement of XLSTAT major statistical features. Amaury Labenne is now a senior data scientist consultant. He also provides training on data analysis and statistics. Very pedagogical, he knows how to adapt his courses to his audience. He favors teaching methods based on examples and real business applications rather than on theoretical and mathematical explanations.