Tracking & Trending Metrics in the Pharma & Biotech Industry

Regardless of how good your process is, there is always some variation. By monitoring the process over time, scientists and engineers can identify when the variation is becoming excessive and take steps to reduce it, so they can identify potential problems before they start affecting the product. An important consideration in process monitoring is knowing when to react and how to differentiate the signals from the noise. Protecting product quality is central to the role of a process engineer/scientist, however, it is rare that there is only one source of variation that requires attention. Prioritizing process improvement projects using data-based risk metrics from process capability analysis reduces cost, and improves efficiency and product safety.

Decoding Python: Shorten the time from discovery to model deployment using JMP Pro

Python is one of the most widely-used programming languages in data science and machine learning. Its versatility and power have made it a popular choice for analyzing, processing, and visualizing data in virtually every industry. However, as data sets become more complex and difficult to manage, it can be challenging to leverage the full potential of Python for data analysis. 

In this webinar, you’ll learn more about JMP Pro, a powerful data analysis software tool that provides an intuitive and user-friendly interface for data visualization, modeling, and analysis. With the release of Model Screening, JMP Pro has become even more versatile, allowing users to integrate Python into data analysis workflows, further leveraging the power of Python throughout the organization.

Analyzing Curve Data with JMP

Many scientists and engineers have data that are represented by a curve, and using curves in analysis is challenging. Curves are often summarized into a few parameters for analysis. However, parameterizing a curve can be difficult, time-consuming, and most of the data is left behind. In today’s workplace, doing more with less is critical; luckily, JMP can help.

In this webinar, we’ll learn more about analyzing two types of curves: curves that can be described by a formula and curves that need to be described with a flexible fit. 

In this webinar, you’ll learn how tools in JMP will help you better understand the variation in the shape of curves and how to use curves in Design of Experiments and modeling to better understand how input variables effect the shape of curves.