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Cover: 9783111334578 | Scientific Data: A 50 Steps Guide using Python | Hofmann | Taschenbuch
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Scientific Data: A 50 Steps Guide using Python
Taschenbuch von Matthias Josef Hofmann
Sprache: Englisch

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Beschreibung
"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.

"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.

Zusammenfassung
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".

Details
Medium: Taschenbuch
Inhalt: XVI
218 S.
85 farbige Illustr.
85 col. ill.
ISBN-13: 9783111334578
ISBN-10: 3111334570
Sprache: Englisch
Ausstattung / Beilage: Großformatiges Paperback. Klappenbroschur
Einband: Paperback
Autor: Hofmann, Matthias Josef
Hersteller: De Gruyter
Verantwortliche Person für die EU: Walter de Gruyter GmbH, De Gruyter GmbH, Genthiner Str. 13, D-10785 Berlin, productsafety@degruyterbrill.com
Abbildungen: 85 col. ill.
Maße: 240 x 170 x 15 mm
Von/Mit: Matthias Josef Hofmann
Erscheinungsdatum: 07.10.2024
Gewicht: 0,401 kg
Artikel-ID: 129107653
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