Advanced Techniques for Working With Big Data in JMP

Advanced Mastering JMP Webinar - August 2019
JMP Methods for working with Big Data, including virtual joins, Data Filtering/Switching, Predictor/Response Screening, Data table graphs, and proxy modeling and saving python score code for JMP models

[@Zane_Zaminsky]

He’s back!! Another FABULOUS Dr. Parris JMP video! Despite the fact that I have been using JMP since version 3, I always learn from Dr. Parris! Thank you!

[@JulianParrisPhD]

Such kind words, thank you!

[@markchahl6408]

I too have been using JMP since version 3 and always learn from Dr. Parris' videos. Keep up the great work educating the user base!

[@PatrickGiuliano]

I'm summarizing a section of this video that demonstrates a very useful JMP technique!
"Uncovering missingness for every possible indicator:"
ua-cam.com/users/clipUgkxI0WTsr_sAOnWWMV5YvoZYzbciL7NcNEb?si=0e80gXR7EHEaBPyr
ua-cam.com/users/clipUgkxiHTDpOjKxrY236O8HnWyYdc8JybUvc7_?si=xIzHWvQwZSqRUGZW.
This approach can be further useful in the context of trying to define all possible category levels for every indicator. In this way, we can actually "show" missingness (or values that might otherwise we considered zero) in a data structure that otherwise doesn't have those levels explicitly defined.