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Advanced Data Preparation: 10 Essential Tools in JMP(R) to Get From “Messy” to “Analysis Ready” ( 2019-EU-TUT-143 )

Level: Intermediate
Job Function: Analyst / Scientist / Engineer
Julian Parris, JMP Learning Strategy Manager, SAS

Rarely, if ever, do data come to us in an “analysis ready” format. Luckily, JMP has a rich and expansive set of tools that enable you to efficiently prepare your data for analysis. In this tutorial we explore 10 of the essential tools in JMP that help us get our data from “messy” to “analysis ready,” including methods for handling table restructuring and joining, computed and derived variables, outliers and influential points, recoding of variables, missing values and more. After we explore each of the 10 essential tools in depth and discuss best practices (and even some “off-label” uses for certain tools), we’ll work through three case studies where we will apply these tools in various ways to efficiently import, recode, restructure and reorganize complex and challenging data sets. Previous experience using JMP is highly recommended, though not strictly necessary.

 

 

Comments
tajrida

Hi Julian,

Excellent Tutorial, Can you please upload the journal used in this tutorial.

 

Thanks

tajrida

 

julian

Certainly, @tajrida! I just added the journal to this post -- apologies for it not already being there.
@julian