A rising sophomore in college, I am nearing the end of my summer internship with the JMP marketing team. While I’ve spent previous summers doing more technical work, I was interested in learning the ways that technical knowledge could help to solve business problems. I got the chance to complete a wide range of technical and non-technical projects, but some of my favorite tasks involved using JMP to analyze marketing data. As I reflected on my summer and what I've learned about JMP, I put together a list of my top three favorites things about JMP.
1. You can JMP right into JSL
JMP allows you to easily access the script for Distributions and graphs.
2. Cleaning data doesn’t need to be tedious
I watched Dick De Veaux, Professor of Statistics at Williams College, in an Analytically Speaking webcast and later read a quote by him that stuck with me: “Most of the time on a data project is spent cleaning and preparing the data.” Before my internship at JMP, I had never really worked with huge data sets, and the idea of cleaning and preparing data seemed less appealing than working with the graphs and analyses. After I was introduced to the Recode tool in JMP, I changed my mind about data cleanup. I had imagined having to rewrite each row of data, but JMP did a ton of that work for me, turning all the data to uppercase, lowercase, or titlecase, and grouping similar values. Of course, I still had to make sure the data matched up correctly, but JMP even made this part easier. Instead of finding myself overwhelmed by all the data, I actually enjoyed recoding my data, and my graphs turned out a lot better and simpler to interpret.
It's simple to quickly combine similar values using the Recode tool.
3. You can make a data table into your data table
I quickly learned that you don’t always get data tables in a form that’s ideal for analysis. Luckily, JMP has many built-in tools to manipulate your table or create a new one based on the original. For example, I wanted to compare how many people replied yes to a series of questions, but each question was in its own column, so I couldn’t create one clean Distribution. Because I had access to JMP 13, I used the new “Expanded Modeling Types” capability in JMP to create a “Multiple Response” column that I could analyze all at once. When I wanted to compare responses to different questions that had the same answer choices, I used the “Stack” table tool. Creating a summary table of different columns was also useful for seeing how basic statistics like mean and standard deviation matched up. Those tools only begin to touch on the different data manipulation tools that are available, and there are even more ways to restructure or reshape your data.
Turn multiple columns into one column using the Stack tool.
I am still exploring the capabilities of JMP, but as you can see, even a beginner can do a lot with JMP! My summer with JMP has been an incredible experience, and I am looking forward to expanding my JMP skills in the future.