Udvidet returret til d. 31. januar 2025

Python Data Science - Chaolemen Borjigin - Bog

Bag om Python Data Science

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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  • Sprog:
  • Engelsk
  • ISBN:
  • 9789811977015
  • Indbinding:
  • Hardback
  • Sideantal:
  • 360
  • Udgivet:
  • 1. juli 2023
  • Udgave:
  • 23001
  • Størrelse:
  • 215x25x285 mm.
  • Vægt:
  • 1131 g.
  • Ukendt - mangler pt..
Forlænget returret til d. 31. januar 2025

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  • BLACK WEEK

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Prøv i 30 dage for 45 kr.
Herefter fra 79 kr./md. Ingen binding.

Beskrivelse af Python Data Science

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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