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"JMP®: Analyzing and Modeling Multidimensional Data" Course Homepage

ruthhummel

Summary: This course is for JMP users who work with data that have many variables. The course demonstrates various ways to examine high-dimensional data in fewer dimensions, as well as patterns that exist in the data.

Methods for unsupervised learning are presented in which relationships between the observations, as well as relationships between the variables, are uncovered. The course also demonstrates various ways of performing supervised learning where the relationships among both the output variables and the input variables are considered. In the course, emphasis is on understanding the results of the analysis and presenting conclusions with graphs.

Duration: 14 hours of content.

Modalities:

live online with instructor -- This course is available periodically in our public course schedule. The public courses are an opportunity to learn this content with a live instructor, but they are currently only offered in English and at times most convenient to a US audience (because most of our instructors are in US time zones). Don't see what you are looking for? Let us know.
through a third-party training vendor -- Any course in our JMP Curriculum could be taught by a licensed training vendor, including through the training department at your own company. Contact your JMP representative to learn more.

Prerequisites: Before attending this course, it is recommended that you complete the JMP® Software: A Case Study Approach to Data Exploration and JMP®: Statistical Decisions Using ANOVA and Regression courses or have equivalent experience.

Learning Objectives:

Use principal component analysis to reduce the number of data dimensions
Use loading plots to understand the relationships between variables
Interpret principal component scores and perform factor analysis
Build more stable models by removing collinearity with principal component regression (PCR)
Identify natural groupings in the data via cluster analysis
Identify clusters of variables
Classify observations into groups with discriminant analysis
Fit complex multivariate predictive models with partial least squares (PLS) regression models.

Course Outline:

Introduction to Multidimensional Data

Multidimensional analysis
Review of matrix algebra

Principal Component Analysis

Interpretation of principal components
Finding principal components

Principal Component Regression

Principal component regression

Factor Analysis

Factor extraction
Factor rotation

Cluster Analysis

Introduction to cluster analysis
Hierarchical clustering
K-means clustering
Variable clustering

Discriminant Analysis

Discrimination
Classification

Partial Least Squares Regression

PLS algorithms
PLS reports

Comments

Raaed

كيف يمكن التسجيل في الدورة

ruthhummel

Hi @Raaed, here are the course options:

Modalities:

live online with instructor -- This course is available periodically in our public course schedule. The public courses are an opportunity to learn this content with a live instructor, but they are currently only offered in English and at times most convenient to a US audience (because most of our instructors are in US time zones). Don't see what you are looking for? Let us know.
through a third-party training vendor -- Any course in our JMP Curriculum could be taught by a licensed training vendor, including through the training department at your own company. Contact your JMP representative to learn more.

There is currently no upcoming session of this course in the public calendar. If you want to request this course, use this option: "Don't see what you are looking for? Let us know."