A Gentle Introduction to Principal Component Analysis Workshop

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Hands-on experience with Principal Component Analysis and applying it to data sets

About this event


The Canadian Statistical Sciences Institute (CANSSI) is partnering with Simon Fraser University’s Big Data Hub to offer this one-day workshop designed to provide researchers with the basic understanding of Principal Component Analysis and how (and when) it can be used to work with complex data sets. This workshop will be taught by Dr. Don Estep, Director of CANSSI, and a Tier 1 Canada Research Chair in the Department of Statistics and Actuarial Science at Simon Fraser University.

This workshop introduces you to basic concepts from mathematics and geometry, allowing you to have a better understanding of how vectors and matrices (and associated operations on these constructs) can then be used in PCA to facilitate analysis of some example datasets. Topics include:

  • Clusters of points
  • Vectors and Matrices
  • Eigenvalues and eigenvectors
  • Principal Component Analysis: matrices, eigenstructure, transformed data, variance, number of PCs, and plots

Benefits of this Workshops

Data is everywhere. Analyzing and making conclusions from data has become important in a wide range of fields and contexts. One major challenge is the common situation in which data is “high dimensional”, that is concerned with a very large number of variables. Statisticians and Data Scientists have a wide range of techniques and tools to support analysis of high dimensional data. While there are many techniques that can be used in the processing and analysis of such high dimensional data, one of the most fundamental techniques is Principal Component Analysis (PCA). PCA is the beginning of many other approaches.



What You'll Learn

  • Gain foundational knowledge in mathematics and geometry needed to understand PCA
  • Understand how to use PCA for dimension reduction
  • Understand how and when to use PCA and some of the pitfalls


Dr. Don Estep, Scientific Director of CANSSI, and a Tier 1 Canada Research Chair in the Department of Statistics and Actuarial Science at Simon Fraser University.


High-school mathematics, or O-level Math, or equivalent


The Canadian Statistical Sciences Institute is a national virtual institute offering the leadership and infrastructure necessary to increase and further develop statistical sciences research in Canada and promote the discipline. Building on the international stature of the Canadian statistical community, CANSSI seeks to develop all areas of the statistical sciences, including interdisciplinary research where statistical innovation is essential to the development of other disciplines. Through national networks of researchers, CANSSI will tackle the big research questions in statistics of importance to science and the public interest, and will establish links with other disciplines and organizations that are heavy users and producers of data.

About SFU's Big Data Hub

Simon Fraser University leverages the power of big data so Canada can lead in a digital world. With over a decade of leadership in the big data field, SFU’s Big Data Hub engages with our partners to fill critical talent shortages, generate new knowledge and contribute to an innovative economy. We connect government to industry and the community to deliver data-driven solutions to challenging problems. With 24 governmental partners and over 100 companies actively engaged, we are empowering people to use data for impact and social good to solve global challenges and transform society.

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Online event

Organizer SFU's Big Data Hub

Organizer of A Gentle Introduction to Principal Component Analysis Workshop

SFU's Big Data Hub

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