Data Analysis Seminar for Engineers: Learning through "Try & Error"
Data Analysis Learning through 'Trial & Error' for Engineers: Practical Exploration of Factors and Building Predictive Models
This is an in-person seminar on the statistical analysis software JMP. [Overview] Targeted mainly at engineers in the manufacturing industry, this seminar will focus on data utilization, explaining practical approaches to factor exploration and predictive model building using JMP products. Analysis does not yield optimal results in one go; it is important to enhance accuracy through repeated trial and error in data preparation and model building. In particular, the quality of data preparation, such as preprocessing and feature creation, significantly impacts the analysis results. JMP allows for efficient progress while visualizing these processes. In this seminar, we will introduce a series of processes from data visualization, preprocessing, and feature creation to factor exploration through correlation and regression analysis, as well as predictive model building (such as neural networks and random forests), all through trial and error. We will also explain how to extract important factors from the constructed models. Through examples of regression data predicting material strength and classification data determining good and defective products, participants will gain an understanding of how to conduct analysis relevant to practical applications.
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basic information
Date and Time: June 19, 2026 (Friday) 14:00 - 16:00 Venue: SAS Institute Japan, Inc. Tokyo Headquarters 11th Floor, Roppongi Hills Mori Tower, 6-10-1 Roppongi, Minato-ku, Tokyo Nearest Station: Toei Subway / Tokyo Metro "Roppongi" Target Participants: - Engineers who want to conduct quality improvement and factor analysis based on data - Those who want to practically utilize the entire process of building predictive models, including preprocessing and feature creation - Individuals who wish to efficiently build and evaluate factor exploration and machine learning models using JMP products Program: First Half... What to Do Before the Model: Key Points for Data Preparation and Trial & Error Using JMP * Understanding the contents of the data (visualization and summary statistics) * Handling missing values and outliers * Creating variables to enhance model accuracy (feature engineering) Second Half... How to Conduct Factor Exploration and Build Predictive Models Without Creating a Black Box * Visualization of factors using correlation analysis, regression models, and decision trees * Model evaluation using training and validation data * Comparison and selection of multiple machine learning models (e.g., neural networks, random forests, etc.) * Intuitive understanding of important factors and prediction results
Price information
Participation fee: Free *Advance registration is required from the page below https://www.jmp.com/ja/events/seminars/non-series/2026/06-19-factor
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P1
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Applications/Examples of results
Examples of major companies around the world utilizing JMP are introduced on the page below. https://www.jmp.com/ja/customer-stories/customer-listing/all
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Company information
The JMP story goes back to 1989 when John Sall decided to combine statistical analysis capabilities with graphical visualizations to animate and visualize data. For more than 35 years now, John Sall has led JMP R&D, making each version of JMP more visual, more interactive, and more practical to help users understand their data. What started as a passion project has grown by leaps and bounds. It’s now a family of statistical software products designed with scientists and engineers in mind and used worldwide in nearly every industry.
