Machine learning course for Product Managers — [4.D / 8]

Why Product Managers Need to Know PCA and FA

Understanding PCA and Factor Analysis enables product managers to simplify complex datasets and uncover meaningful insights.

These techniques are essential for customer segmentation, feature prioritization, and product personalization.

Outline for the Article

1. Dimensionality Reduction & Principal Component Analysis (PCA)
2. How PCA Works? Real-World Case Study
3. Understanding Factor Analysis (FA)
4. PCA vs. Factor Analysis: Key Differences
5. How Product Managers Use PCA and FA in Product Strategy

In the world of machine learning and artificial intelligence, Principal Component Analysis (PCA) and Factor Analysis (FA) are two essential techniques used for dimensionality reduction and understanding patterns in complex datasets.

For Product Managers (PMs), these methods might sound too technical at first. However, they play a critical role in simplifying data, uncovering insights, and improving model performance, making them valuable concepts for driving AI/ML…