Statistics for Scientists: A Concise Guide for Data-driven Research - Hardcover

Über die Autorin bzw. den Autor

Umberto Michelucci is an Award-winning artificial intelligence researcher, lecturer, advisor, and mentor with 20 years of experience in solving complex problems with innovative and advanced technologies. As lecturer I help universities and research groups to learn and use machine learning techniques in their research projects and publications. He is responsible for artificial intelligence in large European funded projects with large international consortia. He brings his passion and experience as long-distance runner into research and helping companies into generate value from artificial intelligence by knowing how to plan and use the right techniques to get to the finish line of a “long distance” project. He believes that artificial intelligence will make our society better, and he is willing to do everything in his power to make this possible. He has a PhD in machine learning and physics, and is the founder of TOELT, a company focused on research in AI and of the AI Center of Excellence at Helsana Versicherung AG in Switzerland. He is also a Google Developer Expert in Machine learning based in Switzerland.

He published three books with Apress on Deep Learning and TensorFlow, and a textbook on mathematical methods for Machine Learning with Springer.

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This book offers researchers and practitioners a concise and accessible guide to the essential concepts in statistics, emphasizing their proper application. It encourages readers to delve deeper into the fascinating field of statistics, a branch of mathematics that enhances our understanding of the world around us. Designed to provide enough material for a short introductory course, Statistics for Scientists caters to students at all levels. It emphasizes real-world applications, providing scientists with the tools they need to conduct more reliable and valid studies, ultimately contributing to the advancement of scientific knowledge. Learn to interpret statistical results accurately and draw meaningful conclusions from your data, significantly contributing to the advancement of scientific knowledge.

Structured to deliver a clear overview of statistics and data analysis for scientific research, the book begins with fundamental concepts, including random variables, outcome spaces, and the distinction between descriptive and inferential statistics. It then explores data types, measures of central tendency, dispersion, and position. The discussion continues with an examination of outliers and various methods for identifying them. As the chapters progress, more complex topics such as distributions, hypothesis testing, and regression analysis are introduced in a step-by-step manner. This structure makes the book suitable for readers ranging from beginners to those seeking a quick refresher.

The author has selected key concepts that anyone interested in using statistics should be familiar with. Some topics, such as hypothesis testing, are covered briefly; a more comprehensive treatment would require a stronger background in statistics and mathematics (such as calculus). With pedagogical elements that include text boxes with Definitions, Examples, and Warnings, this book introduces the necessary concepts of statistics for scientists described in a short and concise way, enriched with tips and rigorous explanations. This book is an invaluable resource for scientists seeking to improve their data analysis skills and contribute to the growing body of scientific knowledge through rigorous and reliable research.