Select customers were invited to our London office for a workshop on data preparation in JMP with Learning Strategy Manager, Julian Parris (@julian). You can find a description below.
Materials used in the workshop and resources for further learning are attached. (For exercise 3, in order to make the map visuals work, you need to have the two "district_burrough..." jmp data tables saved in the same folder as the LifeSat table that you are working from). Please let me (@phil_kay) know by comment below if you have any questions or need anything else.
My Copenhagen talk: Narcististic - Lessons learned (about JMP and life) during a 14k+ mile journey collecting, analyzing, and visualizing personal health data:
Rarely, if ever, do data come to us analysis-ready. Scientists and engineers tell us that more than 80% of their work with data is spent on preparation, leaving little time for exploring and extracting insight. In extreme cases your data might be so messy that you can’t imagine that you will ever be able to use them to answer your important technical questions. These common challenges prevent organisations from realising the full value of data analytics to reduce costs and speed products to market.
Luckily, JMP has a rich set of tools that enable you to efficiently prepare your data for analysis. In this workshop you will explore 10 of the essential tools in JMP to help you get your data from “messy” to “analysis-ready”.
Through a combination of case study demonstrations and hands-on exercises you will learn how to handle: • 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.
Dissolution data.jmp
district_borough_unitary_region-Name.jmp
district_borough_unitary_region-XY.jmp
LifeSatSplit.jmp
Tablet Supplier Data.jmp
Data Prep Tutorial - London.jrn
Last Modified: Nov 4, 2019 2:33 AM
Comments
You must be a registered user to add a comment. If you've already registered, sign in. Otherwise, register and sign in.