Led by JMP developers and technical staff, these 90-minute tutorials are a rare opportunity for you to go in-depth on specific topics with the experts themselves. You’ll learn about some of the core tenets of JMP and see the software in action. You can sign up for tutorials when you register for the conference. All sessions are $150. We also offer pre-conference training and certification exams, presented by SAS Education.

Power User:4

JSL Application Development

  • Session ID: 2019-US-TUT-246

    Finger Rock I

    Building Dashboards in JMP®

    Dan Schikore, JMP Principal Software Developer, SAS

    • Topic: JSL Application Development
    • Level: 2

    JMP platforms are often used in combination with one another to help guide a workflow or to provide complementary analysis and visualization of the data. The JMP Dashboard Builder can help you organize multiple reports in a single window and reproduce the set of reports using the same data table or a new table. Dashboard layout can be done automatically or through interactive drag-and-drop operations, and one report can optionally be used to filter others in the dashboard. Dashboards can be saved to a JMP data table to reproduce the reports with the same table, or they can be saved to an add-in to share the dashboard with colleagues and reproduce the same report on different data tables. When dashboards depend on the results of database queries, you have the choice to re-run the query each time, or to use a saved copy of the query. Dashboard results can also be saved to JMP Public or a private JMP Live server to share interactive reports with others via the web.

  • Session ID: 2019-US-TUT-290

    Finger Rock I

    Essential Scripting for Efficiency and Reproducibility: Do Less to Do More

    Drew Foglia, JMP Distinguished Software Developer, SAS

    Evan McCorkle, JMP Software Developer, SAS

    • Topic: JSL Application Development
    • Level: 2

    From reproducing simple tasks to automating daily processes to sharing scripts with colleagues to deploying full applications across your organization, challenges exist at every level that can limit the efficiency and reliability of your JSL scripts. In this tutorial, we will travel along the arc from small scripts to large applications presenting best-practice techniques for mitigating many of the common, yet subtle, pitfalls that often hinder JSL novices and veterans alike. We will discuss strategies for combining multiple steps into a cohesive script, and conversely splitting overly large scripts into more manageable files. We will also present tips for handling errors, wrangling windows, isolating variables from unexpected changes, protecting the integrity of your scripts without compromising usefulness and more.

Data Access and Manipulation

  • Session ID: 2019-US-TUT-291

    Finger Rock II

    Advanced Data Preparation: 10 Essential Tools in JMP® to Get From “Messy” to “Analysis Ready”

    Julian Parris, JMP Learning Strategy Manager, SAS

    • Topic: Data Access and Manipulation
    • Level: 2

    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.

  • Session ID: 2019-US-TUT-264

    Finger Rock I

    Before the Modeling: Feature Engineering in JMP®

    Jordan Hiller, JMP Senior Systems Engineer, SAS

    Mike Muhlada, JMP Senior Test Engineer, SAS

    • Topic: Data Access and Manipulation
    • Level: 2

    Successful predictive modeling projects require careful feature engineering. Both a science and an art, feature engineering involves gathering, summarizing, reshaping and transforming data into a form usable by modeling algorithms. This tutorial will describe some of the common activities in feature engineering and demonstrate JMP tools that can be used to perform them. There will be in-depth discussion and examples showing the use of formula columns for binning and transformation, the Recode platform for collapsing categories and Query Builder for summarization.

  • Session ID: 2019-US-TUT-295

    Finger Rock II

    Let's Talk Tables

    Kelci Miclaus, Senior Manager Advanced Analytics R&D, JMP Life Sciences, SAS

    Mandy Chambers, JMP Principal Test Engineer, SAS

    • Topic: Data Access and Manipulation
    • Level: 2

    JMP has many ways to join data tables. Using traditional Join, you can easily join two tables together. JMP Query Builder enhances the ability to join, providing a rich interface allowing additional options, including inner and outer joins; combining more than two tables; and adding new columns, customizations and filtering. In JMP 13, virtual joins for data tables were developed that enable you to use common keys to link multiple tables without using the time/memory necessary to create a joined (denormalized) copy of your data. Virtually joining tables gives a table access to columns from the linked tables for easy data exploration. In JMP 14 and JMP 15, new capabilities were added to allow linked tables to communicate with row state synchronization. Column options allow you to set up a link reference table to listen and/or dispatch row state changes among virtually joined tables. This feature provides an incredibly powerful data exploration interface that avoids unnecessary table manipulations or data duplications. Additionally, there are now selections to use shorter column names, auto-open your tables and a way to go a step further, using a Link ID and Link Reference on the same column to virtually “pass through” tables. This presentation will highlight the new features in JMP with examples using Human Resources data followed by a practical application of these features as implemented in JMP Clinical. We will create a review of multiple metrics on patients in a clinical trial that are virtually linked to a subject demographic table and show how a data filter on the Link ID table enables global filtering throughout all the linked clinical metric (adverse events, labs, etc.) tables.

Data Visualization and Exploration

  • Session ID: 2019-US-TUT-292

    Finger Rock II

    Graph Builder: From Discovery to Presentation

    Bill Worley, JMP Senior Systems Engineer, SAS

    • Topic: Data Visualization
    • Level: 1

    Graph Builder is a jack-of-all-trades when it comes to visualizing your data. It is useful throughout the analysis process, from exploring your data to understand what you've got, to discovering relationships, to crafting the perfect graph to present your results. This session will show you how to use Graph Builder effectively with an overview of the graph elements available and the options for each one. It will also show you some tricks to help you become a master graph builder.

  • Session ID: 2019-US-TUT-270

    Finger Rock III

    Which Model When?

    Ruth Hummel, JMP Academic Ambassador, SAS

    Mary Loveless, JMP Systems Engineer Manager, SAS

    • Topic: Data Exploration
    • Level: 2

    You have a business or research question, you’ve collected or found appropriate data, and you are ready to analyze. But which analytical methods should you try? And how will you choose a final model? In this talk, we will look at several data scenarios and present modeling options and a framework for comparison. We will look at how different questions or goals affect the modeling choices we make. (Predict? Explain? Find associations?) Models covered will include traditional regression, penalized regression, partial least squares and a few others. Comparison techniques will include residuals analysis, comparing fit statistics, and cross-validation or validation on new data.

Design of Experiments

  • Session ID: 2019-US-TUT-287

    Finger Rock II

    The Fundamentals of Modern Experimentation Using JMP®

    Bradley Jones, JMP Distinguished Research Fellow, SAS

    • Topic: Design of Experiments
    • Level: 1

    Designed experimentation is the best way to learn about industrial processes. But, in the long history of the design of experiments, most experimenters learned methods that focused on conforming the experiment to classical designs. The need to understand these constraints limited the number of effective experimenters. JMP’s approach to DOE takes the burden of understanding the constraints of textbook designs off the practitioner, instead putting the experimenter in control. See how to use the Custom Designer to design an experiment for any process. The Custom Designer creates an experiment tailor-made for your situation. Do you have constraints on the input factors? No problem. Mixture factors along with process inputs? No problem. What about a run budget that limits the number of experiments you can perform? The Custom Designer will give you a design that will allow you to learn as much as possible within your budget. A few years ago Stu Hunter said that, "[the technology behind the DOE platforms in JMP] would change the way I teach DOE.” Come see how.

Predictive Modeling

  • Session ID: 2019-US-TUT-289

    Finger Rock III

    Introduction to Functional Data Analysis

    Chris Gotwalt, JMP Director of Statistical Research and Development, SAS

    Ryan Parker, JMP Senior Research Statistician, SAS

    • Topic: Predictive Modeling
    • Level: 3

    JMP Pro is now able to model and visualize functional data in a new way that is direct, straightforward and leads to highly accurate models. We will introduce the Functional Data Explorer platform and go through a set of examples that illustrate two particularly useful families of problems that it facilitates solving, function response DOE analysis (FR-DOE) and functional regressor machine learning (FR-ML). FR-DOE analysis allows one to see directly how the shape of a response curve changes as a result in changes in the value of the DOE factors. FR-ML is an approach to feature extraction for sensor data from manufacturing processes that faciliates accurate early prediction of final batch yield or faulty products. Our examples will be primarily from pharmaceutical, chemical and semiconductor manufacturing, but our approach can be applied to practically any industry.

  • Session ID: 2019-US-TUT-293

    Finger Rock III

    The Most Flexible Modeling Platform That You're Not Using

    Clay Barker, JMP Principal Research Statistician Developer, SAS

    • Topic: Predictive Modeling
    • Level: 2

    Generalized Regression — it sounds scary, but it's not. This platform in JMP Pro can handle most common linear modeling exercises. Need to find the best set of predictors in a sea of possibilities (i.e., variable selection)? This platform can do that. Need to model a categorical response with more than two levels? Generalized Regression can do that. Have problems with non-normal responses like count data or yield percentage? Where other modeling methods fall down with these responses, Generalized Regression handles them with aplomb. If you're not using this platform, you're missing out. We will look at using Genreg to build models in a variety of settings: starting with an orthogonal designed experiment and moving to large observational data sets.

Quality and Reliability

  • Session ID: 2019-US-TUT-193

    Finger Rock III

    Large-Scale Process Monitoring Using JMP®

    Laura Lancaster, JMP Principal Research Statistician Developer, SAS

    Jianfeng Ding, JMP Senior Research Statistician Developer, SAS

    • Topic: Quality and Reliability
    • Level: 2

    In this age of big data and complex manufacturing there is often an enormous amount of process data that needs to be monitored and analyzed to maintain or improve quality. JMP has several tools to help the analyst quickly and efficiently increase the scale of process monitoring. The Process Screening platform allows users to easily scan processes for stability and capability, enabling them to focus attention on processes needing improvement. The platform initially computes a summary report based on control chart, capability and stability calculations, and creates several graphs for quick visual assessment of process health. Based on these initial results, it is easy to select the processes needing attention and explore them more in depth with access to Control Chart Builder and Process Capability. The Model Driven Multivariate Control Chart (MDMCC) platform, new in JMP 15, allows users to monitor large amounts of highly correlated processes. This platform can be used in conjunction with the PCA and PLS platforms to monitor multivariate process variation over time, give advanced warnings of process shifts and suggest probable causes of process changes. We will use case studies to demonstrate how to use JMP to monitor and analyze many processes for fast and efficient improvement.

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