Hofmann Scientific Data: A 50 Steps Guide using Python

1. Auflage 2024
ISBN: 978-3-11-133470-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 234 Seiten

Reihe: De Gruyter Textbook

"Scientific Data: A 50 Steps Guide using Python" is your guide towards experimental scientific data. It aims to bridge the gap between classical natural sciences as taught in universities and the ever-growing need for technological/digital capabilities, particularly in industrial research. Topics covered include instructions for setting up a workspace, guidelines for structuring data, examples for interfacing with results files and suggestions for drawing scientific conclusions therefrom. Additionally, concepts for designing experiments and visualizing the corresponding results are highlighted next to ways of extracting meaningful characteristics and leveraging those in terms of multi-objective optimizations. The concise problem-solution-discussion structure used throughout supported by Python code snippets emphasizes the work’s focus on practitioners. This guide will provide you with a solid understanding of how to process and understand experimental data within a natural scientific context while ensuring sustainable use of your findings and processing as seen through a programmer’s eyes.

Zielgruppe

Graduate students of the natural sciences, researchers and profes

Autoren/Hrsg.

Hofmann, Matthias Josef

Hofmann, Matthias

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Verfahrenstechnik, Chemieingenieurwesen
• Naturwissenschaften Physik Physik Allgemein
• Naturwissenschaften Chemie Chemie Allgemein

Über Autor(innen)

Matthias Hofmann holds a Ph.D. in Physical Chemistry from the University of Regensburg. At Albert Invent, Matthias continues to contribute to innovative methods in natural science research and accelerating R&D through a data-driven approach. He is the author of "Data Management for Natural Scientists - A Practical Guide to Data Extraction and Storage Using Python".