Data Mining in JMP with Data Sets You Don’t Know
Hands on workshop
The key to effective data analysis is a structured approach that starts with understanding the data set and gradually moves towards more complex analysis techniques. Come to this hands-on workshop where we explore semiconductor data for processes and sensors, starting with raw data. We will cover best practices for getting data ready, explore different data visualizations, and use multiple data mining techniques to drill down to explore for root cause analysis using JMP’s GUI.
When approaching a new data set, especially for analysis in JMP, here are some best practices we will discuss:
Data Preparation
- Initial Data Overview
- Identify missing values, outliers, and inconsistencies
- Perform data cleaning operations such as filtering irrelevant data and deciding how to handle missing data
Data Analysis and Visualization
- Exploratory Data Analysis (EDA)
- Generate summary statistics to understand data distributions
- Uncover patterns and anomalies using data visualizations, such as histograms, scatter plots and box plots
- Data Visualization
- Utilize JMP's interactive graphical user interface to explore different data visualizations
- Experiment with various graphs to best represent the underlying data characteristics
Predictive Modeling
- Data Mining Techniques
- Apply clustering techniques to group similar data points
- Use decision tree methods to identify structure and relationships within the data
- Root Cause Analysis
- Investigate potential relationships by drilling down into data segments
- Leverage JMP's tools for hypothesis testing and regression analysis to validate findings
