Joel Smith is the Director of Rapid Continuous Improvement at Keurig Dr. Pepper as well as the co-author of the Applied Statistics Manual. He will be hosting a panel on Leading Successful Data Analysis at the 2019 Minitab Insights Conference.
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.
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).
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.
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.