Description

Rather than presenting Python as Java or C, this textbook focuses on the essential Python programming skills for data scientists and advanced methods for big data analysts.

Unlike conventional textbooks, it is based on Markdown and uses full-color printing and a code-centric approach to highlight the 3C principles in data science: creative design of data solutions, curiosity about the data lifecycle, and critical thinking regarding data insights. Q&A-based knowledge maps, tips and suggestions, notes, as well as warnings and cautions are employed to explain the key points, difficulties, and common mistakes in Python programming for data science. In addition, it includes suggestions for further reading.

This textbook provides an open-source community via GitHub, and the course materials are licensed for free use under the following license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

More teaching materials including Codes, Datasets, Slides, Syllabus can be found at https://github.com/LemenChao/PythonDataScience

Python Data Science

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£64.99

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Hardback by Chaolemen Borjigin

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Short Description:

Rather than presenting Python as Java or C, this textbook focuses on the essential Python programming skills for data scientists... Read more

    Publisher: Springer Verlag, Singapore
    Publication Date: 01/07/2023
    ISBN13: 9789811977015, 978-9811977015
    ISBN10: 9811977011

    Number of Pages: 345

    Non Fiction , Computing

    Description

    Rather than presenting Python as Java or C, this textbook focuses on the essential Python programming skills for data scientists and advanced methods for big data analysts.

    Unlike conventional textbooks, it is based on Markdown and uses full-color printing and a code-centric approach to highlight the 3C principles in data science: creative design of data solutions, curiosity about the data lifecycle, and critical thinking regarding data insights. Q&A-based knowledge maps, tips and suggestions, notes, as well as warnings and cautions are employed to explain the key points, difficulties, and common mistakes in Python programming for data science. In addition, it includes suggestions for further reading.

    This textbook provides an open-source community via GitHub, and the course materials are licensed for free use under the following license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

    More teaching materials including Codes, Datasets, Slides, Syllabus can be found at https://github.com/LemenChao/PythonDataScience

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