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:

• All six-sigma practitioners.
• Scientists, engineers, and technicians who are interested in performing experiments that maximize process knowledge with a minimum amount of resources. 
• Managers who are responsible for delivering products “on time” and “on budget”.
• Students, novice programmers, and any professionals who interact with data.
• Analytics managers.
• Undergraduate students who need to run and analyse experimental data.
• Anyone interested in designing, conducting, and analysing experiments.

After completing the program, participants will be able to master the following topics:

• Prepare data for effective analysis.
• Use data science to design experiments to be as informative as possible.
• Optimise experiments to save time and money
• Use laboratory automation and robotics to reduce research biases.
• Make research more reproducible.
• Identify the obstacles to integrating data-driven experimental design in real laboratories, from an ethnographic and sociological perspective.
• Analyse the data, and make decisions based on the results of the test.
• Learn about robotic laboratories and their effect on future of experimental research. 

• Introduction.
• Principles of data science.
• Data structures for data science.
• Theoretical foundations of data science.
• Practice and application of data science.
• Data analytics.
• Design and statistical analysis.
• Purpose of experimentation.
• Components of experimental design.
• Experiment design guidelines.
• Planning experiments..
• Conducting experiments.
• Analysing experiments.
• Theory into Practice.