Experimental Design and Scientific Data Analysis (Science for Conservators)

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Free Download Experimental Design and Scientific Data Analysis (Science for Conservators) by Lynda K. Skipper, Philip J. A. Skipper
English | November 26, 2024 | ISBN: 1032501634 | 138 pages | MOBI | 6.98 Mb
Experimental Design and Scientific Data Analysis provides accessible information about the research process from start to finish as applied to conservation to enable readers to successfully plan and carry out research and share findings effectively with others.

The book is divided into two parts, starting with experimental design in Part I and then considering data analysis in Part II. Part I takes the reader through the process of designing and planning experiments, from the initial design of aims and objectives to ethical testing, and includes a range of examples to demonstrate how these ideas apply to a range of specialisms and materials. Part II focuses on what to do with data once it has been collected, including choosing the right statistical test, different methods of presenting information, and communicating your findings clearly to your chosen audience. Questions at the end of each chapter are designed to help readers check and consolidate their knowledge of the different topics.
Experimental Design and Scientific Data Analysis is suitable for students, emerging professionals, and experienced conservators at different stages of their careers, and is particularly suitable for those without a scientific background.

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Experimental Design for Data Analysis
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 44m | 348 MB
Instructor: Janani Ravi​

This course covers conceptual and practical aspects of building and evaluating machine learning models in a way that uses data judiciously, while also accounting for considerations such as ordering and relationships within data and other biases.

Providing crisp, clear, actionable points-of-view to senior executives is becoming an increasingly important role of data scientists and data professionals these days. Now, a point-of-view must represent a hypothesis, ideally backed by data. In this course, Experimental Design for Data Analysis, you will gain the ability to construct such hypotheses from data and use rigorous frameworks to test whether they hold true. First, you will learn how inferential statistics and hypothesis testing form the basis of data modeling and machine learning. Next, you will discover how the process of building machine learning models is akin to that of designing an experiment and how training and validation techniques help rigorously evaluate the results of such experiments. Then, you will round out the course by studying various forms of cross-validation, including both singular and iterative techniques to cope with independent, identically distributed data and grouped data. Finally, you will also learn how you can refine your models using these techniques with hyperparameter tuning. When you're finished with this course, you will have the skills and knowledge to build and evaluate models, specifically including machine learning models, using rigorous cross-validation frameworks and hyperparameter tuning.

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