Designed experiments are an advanced and powerful analysis tool during projects. An effective experimenter can filter out noise and discover significant process factors. The factors can then be used to control response properties in a process and teams can then engineer a process to the exact specification their product or service requires.
Experimental design choices influence both what a scientist can discover, as well the confidence that can be placed in the final outcome. Poor experimental design decisions can also lead to wasted time and resources, as well as non-unreproducible research.
A well-built experiment can save not only project time but also solve critical problems which have remained unseen in processes. Specifically, interactions of factors can be observed and evaluated. Ultimately, teams will learn what factors matter and what factors do not.
The emerging area of data science of experimental design aims to develop computational strategies to design experiments in a principled fashion, exploiting data science to produce more efficient, more accurate, and more reproducible research. In this course, we will explore how data science can help us design, perform, and analyse scientific experiments. The course will cover aspects of experimental parameter optimization, laboratory automation, and issues around reproducibility of data analysis. It will include a discussion about the barriers to incorporating data science of experimental design in real laboratories.
The British Academy for Training and Development offers this course to the following categories:
After completing the program, participants will be able to master the following topics:
Note / Price varies according to the selected city
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