How You Can Now Learn More from Your Analytical Chemistry Data and See Inside the Black Box of Machine Learning Tools.
Developing robust and accurate analytical methods relies on collecting the best data and extracting maximum insight. The volume, diversity and complexity of data in analytical chemistry is increasing all the time. This means that analytical chemists often need the skills and tools of a data scientist to efficiently and effectively deal with these challenges. There is much promise around Machine Learning methods, in particular. However, many of the methods and their results can be appear to be somewhat of a black box.
In this presentation we will show the insight and efficiency that can be gained from applying modern data analytics to analytical chemistry data. You will also gain a greater understanding of the mechanisms behind methods like Neural Nets, Clustering and Decision Trees. And how you can make sense of the options to find the most useful solution for your analytical problem by visually interacting with the data and the models.
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Phil Kay (SAS Institute)
Phil Kay is a Senior Systems Engineer for JMP, a division of SAS. Previously, Phil was a key scientist in the development of numerous processes for the manufacture of colorants for digital printing at FujiFilm Imaging Colorants. Phil has a master's degree in applied statistics and a master's and PhD in chemistry. He is also a Royal Society of Chemistry Chartered Chemist and Fellow of the Royal Statistical Society.
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