Principal Component Analysis (PCA) and Factor Analysis

🎓 [Principal Component Analysis (PCA) & Factor Analysis: Mastering Dimensionality Reduction with SAS and R] 📚

Course Overview

Embark on a comprehensive journey into the fascinating world of Principal Component Analysis (PCA) and Factor Analysis with our specialized online course. This course is meticulously designed to demystify the complexities of dimensionality reduction techniques, making them accessible even for beginners. By leveraging real-world examples and hands-on practice with SAS and R, you'll gain a robust understanding of how to effectively apply these methods in machine learning, data science, and analytics.

What You'll Learn 🚀

🔍 Intuitive Understanding of PCA:

• 2D Case Study: Explore the concept of variance across different dimensions and introduce the idea of principal components (PCs).

• Formal Definition of PCs: Deep dive into the formal mathematical definition of PCA, ensuring a strong foundation in the theory.

📈 Properties of Principal Components:

• Visualizing PCA in 3D: Understand PCA through a vivid, three-dimensional image that clarifies the properties and significance of PCs.

• Summarize PCA Concepts: Gather all PCA concepts in one place, providing clarity on why each principal component's eigenvalue is larger than its successor.

📚 Data Treatment for PCA:

• Treating Ordinal and Numeric Variables: Learn the best practices for preparing your data for PCA analysis, from handling ordinal variables to treating numeric ones.

🧪 Conducting PCA using SAS:

• Correlation Matrix, Eigen Value Table, Scree Plot: Navigate through the key components of PCA in SAS, including interpreting the correlation matrix, eigen value table, and scree plot.

• Determining the Number of Principal Components: Discover how to decide on the optimal number of principal components to retain for your analysis.

🔬 Conducting PCA using R:

• Hands-on experience with R packages specifically designed for conducting PCA, such as factoextra and ggplot2.

🎯 Introduction to Factor Analysis:

• Side by Side Comparison: Learn how factor analysis differs from PCA and when to use each method.

📊 Factor Analysis with R and SAS:

• Apply your knowledge of factor analysis in real-world scenarios using both R and SAS.

🔭 Theoretical Insights on PCA for Variable Selection:

• Understand the theoretical underpinnings of PCA for variable selection, followed by a demonstration to solidify your grasp of the concept.

Course Features 🌟

• Downloadable Content: Get lifetime access to the entire course material, including PDFs, datasets, and code files for offline study and practice.

• Expert Instructor: Learn from an industry expert with extensive experience in PCA, factor analysis, and their applications in analytics and machine learning.

• Real-World Examples: Apply theoretical knowledge to real-world scenarios, making complex concepts tangible and practical.

• Hands-On Practice: Engage with interactive coding exercises using both SAS and R to reinforce your understanding and skills.

• Community Support: Join a community of like-minded learners, share insights, and grow your professional network.

Enroll in this course today and unlock the power of dimensionality reduction! Whether you're a data scientist, analyst, or simply passionate about machine learning, this course will equip you with the skills to efficiently handle high-dimensional data and extract meaningful insights. 🌟