Taking Machine Learning from Myths to Business Reality
You now see “Machine Learning” appearing along with “Big Data”, “AI” or “Internet of Things” as part of the digital transformation of business. Yet this new hype is creating a series of myths, which can obscure the role of data-driven professionals in this transformation. Watch our webinar today.
Process Validation Tools For Clinical Approval: An Example For Passing the 3 FDA Stage Goals
The FDA recommends three stages for process validation. Let’s explore the stage goals and the types of activities and statistical techniques typically conducted within each. You can use Minitab Statistical Software to run any of the analyses here. If you don’t yet have Minitab, try it free for 30 days.
Industry Expert Greg Kinsey: Black Belts Can (and Should) Drive the Digital Transformation of Manufacturing
Greg Kinsey, Industry Expert, shares his view on the Future of Operational Excellence and the new role of Lean Six Sigma – and how you can be part of it.
A Quality Career: Dr Rozzeta Dolah Part 2
To celebrate Minitab’s support of the recent International Conference on Teaching Statistics in Kyoto, Japan, we sat down with Dr Rozzeta Dolah, whose career and work in statistics and engineering has taken her to her native Malaysia, to Japan and then the USA where she is currently working at MIT.
The Future of Operational Excellence according to Industry Expert Greg Kinsey
Greg Kinsey, Industry Expert, shares his view on the Future of Operational Excellence and the new role of Lean Six Sigma – and how you can be part of it.
A Quality Career: Dr Rozzeta Dolah Part 1
To celebrate Minitab’s support of the recent International Conference on Teaching Statistics in Kyoto, Japan, we sat down with Dr Rozzeta Dolah, whose career and work in statistics and engineering has taken her to her native Malaysia, to Japan and then the USA where she is currently working at MIT.
Guest Post: Location Matters – Data Mining Research to Enhance Accuracy, Benefit Future Studies
Research institutions and museums seek to provide the most accurate data possible to record the past and choose where to explore in the future. Modeling the accuracy of data is essential and nailing down geographical locations of specimen samples as exactly as possible is vital to do it effectively.
Guest Post: It’s Tough to Make Predictions, Especially about the Future (even with Machine Learning)
At its core, all Machine Learning algorithms follow a two-part process. First a sequence of increasingly complex functions is fit to part of the data (training data set). Then each model in the sequence is evaluated on how well it performs on the data that was held out (the holdout set).
Guest Post: How quickly and effectively can you drive to a solution?
Deductive logic has been used for millennia to describe the natural world. With the explosion of data availability and machine learning, some suggest inductive reasoning will make deduction obsolete – the end of science. An approach that supports learning and drives toward a solution involves both.
Guest Post: Predictive Analytics Accelerates Problem Solving
Adam Russell, Global Operations Master Black Belt explains how Tate & Lyle deploys Continuous Improvement (CI) tools, including Minitab and Salford Predictive Modeler (SPM), to challenging engineering and manufacturing problems.