Design of experiments are an important part of scientific research. It is a methodology for choosing the best set of experiments to get data that will help you answer research questions. This book will show you how to use Python to setup and analyze several different kinds of design of experiments. We start with some visualization tools, which is needed when you have the data and want to see trends in it. Then we cover several conventional approaches to design of experiments, including Latin Hypercubes and surface response designs. After this we introduce more modern approaches that build on machine learning and sequential experiments. We conclude with sampling methods that you might use to build your own design of experiment tools.
This book assumes you are already familiar with Python and pycse (e.g. regression, optimization, etc).
A PDF and Jupyter notebook, and requirements.txt file for a Python virtual environment