Understanding PCA using Shiny and Stack Overflow data 2018-03-08T00:13:24+00:00

Understanding PCA using Shiny and Stack Overflow data

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Principal component analysis (PCA) is a powerful approach for exploring high-dimensional data, but can be challenging for learners to comprehend. In this talk, I will walk through a practical and interactive explanation of what PCA is and how it works. As a case study I’ll explore a domain that many data analysts and data scientists are familiar with: programming languages and technologies, as understood through traffic to Stack Overflow questions. We will explore how interactive visualization using Shiny gives us insight into the complex, real-world relationships in high-dimensional datasets.

About the speaker

Julia Silge
Data Scientist, Stack Overflow

I love making beautiful charts, the statistical programming language R, Jane Austen, black coffee, and red wine.

I studied physics and astronomy, finishing my PhD in 2005. I worked in academia (teaching and doing research) and ed tech before moving into data science and discovering R. Now, my background in astronomy, physics, and education has given me a strong foundation for using data to answer interesting questions and communicate about technical topics with diverse audiences. I wrote a book with my collaborator Dave about text mining with R.