Multivariate Data Analysis For Manufacturing – 2-Day Course

Multivariate Data Analysis For Manufacturing – 2-Day Course2019-06-26T09:44:41-04:00

In the era of Big Data, Industry 4.0 and AI, multivariate anlaysis is an essential tool for manufacturers engaged in analytics projects. This 2-day course provides comprehensive training on how to effectively extract actionable insights from available historical data, including how to efficiently visualize and identify the independent driving forces affecting process performance. Workshops using real industrial data, executed on desktop MVA software, will provide practical experience to strengthen your Big Data analytics skillset.

Dates: June 11 & 12, 2019
Location: ProSensus Headquarters – Burlington, Ontario, Canada
Price: USD $1,600  / participant
Software: Course participants require either Aspen ProMV or Sartorious Simca for completion of the software workshops. ProSensus has a limited quantity of laptops with Aspen ProMV that can be loaned to participants who do not have either software.


Are you interested in attending a future ProSensus course? Register below and we will notify you of our next available course offerings.


Dr. John MacGregor will teach this course, with assistance from Dr. Brandon Corbett.

John F. MacGregor, PhD – Founder and Chairman

John MacGregorJohn has dedicated the last 40 years to helping manufacturers improve and optimize their processes using multivariate data analysis. During his tenure at McMaster University, John authored over 200 peer-reviewed journal publications in the areas of mathematical modeling of processes, optimization and control, rapid product development and image analysis.  John has received many awards for his pioneering work in developing and applying multivariate analysis to solve complex manufacturing problems, including the Shewhart Medal and W.G. Hunter Award from the American Society for Quality, the Herman Wold Medal from the Swedish Chemical Society and the R.S. Jane Award and Century of Achievement Award from the Canadian Society for Chemical Engineering. In 2004, John founded ProSensus to help manufacturers learn more from their big data to increase yield, reduce operating costs, and improve product quality.

Brandon Corbett, PhD – Senior Project Engineer

Brandon Corbett PhDDr. Brandon Corbett holds a Ph.D. in chemical engineering (applied multivariate analysis / process systems) from McMaster University. He has been heavily involved with client projects in rapid product development, troubleshooting analyses, and batch modeling during his 2 years at ProSensus.  Brandon is an experienced lecturer with a passion for teaching, and teaches many of ProSensus’ in-house courses.


Course Outline:

Day 1 (8:30 AM – 12:00 PM, 1:00 PM – 4:30 PM)

  • Introduction
    • Objectives and overview of the course
    • Nature of multivariate process data
    • Why use multivariate methods?
    • The concept of latent variables
    • Some process examples
  • Principal component analysis (PCA)
    • PCA: a method for extracting information from a single data matrix
    • PCA concepts and methods
    • Examples
  • Software introduction
  • PCA workshops
    • PCA workshops to learn the software and to explore PCA analysis of industrial data sets

Day 2 (8:30 AM – 12:00 PM, 1:00 PM – 4:30 PM)

  • PLS: Projection to Latent Structures / Partial Least Squares
    • Relationship of PLS to multiple linear regression (MLR) and Principal Component Regression
    • PLS: geometric & algebraic interpretations
    • PLS tools: scores & loading plots, analysis of residuals
    • Model interrogation: contribution plots
    • Calculating the principal components
  • Multivariate SPC
    • A multivariate approach to process monitoring (MSPC)
    • Detecting & diagnosing abnormal operation
    • Interpreting and optimizing processes using empirical models correlation vs. causation
    • Soft sensors and inferentials
    • Optimizing processes based on models from historical data
  • PLS workshops
    • PLS workshops to learn the software and to explore PLS analysis of industrial data sets