This guide provides a solid understanding of how to understand experimental data within a natural scientific context while ensuring sustainable use of findings and processing as seen through a programmer’s eye. The concise problem - solution - discussion structure used throughout supported by Python code snippets emphasizes the book’s focus on practitioners.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Matthias Hofmann holds a Ph.D. in Physical Chemistry from the University of Regensburg. Currently, he is working in the chemical industry where his focus is on streamlining data processing routines and ensuring the accessibility of experimental data.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
Da: Books From California, Simi Valley, CA, U.S.A.
perfect. Condizione: Very Good. Cover and edges may have some wear. Codice articolo mon0003646161
Quantità: 1 disponibili
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New. Codice articolo 48317180-n
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Da: BargainBookStores, Grand Rapids, MI, U.S.A.
Paperback or Softback. Condizione: New. Scientific Data: A 50 Steps Guide Using Python. Book. Codice articolo BBS-9783111334578
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Da: California Books, Miami, FL, U.S.A.
Condizione: New. Codice articolo I-9783111334578
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Da: PBShop.store US, Wood Dale, IL, U.S.A.
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo DB-9783111334578
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Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition. Codice articolo 48317180
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Da: Rarewaves USA, OSWEGO, IL, U.S.A.
Paperback. Condizione: New. "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. Codice articolo LU-9783111334578
Quantità: Più di 20 disponibili
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo DB-9783111334578
Quantità: 1 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: New. Codice articolo 48317180-n
Quantità: Più di 20 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: As New. Unread book in perfect condition. Codice articolo 48317180
Quantità: Più di 20 disponibili