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Autor(en): 
  • Allen B. Downey
  • Elements of Data Science 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Vorankündigung
    Veröffentlichung:  ANGEKÜNDIGT  
    Genre:  EDV / Informatik 
    ISBN:  9781718502901 
    EAN-Code: 
    9781718502901 
    Verlag:  Random House N.Y. 
    Einband:  Kartoniert  
    Sprache:  English  
    Seiten:  304 
    Zus. Info:  GLD CRE WHI BLK 
    Bewertung: Keine Bewertung vor Veröffentlichung möglich.
    Inhalt:
    Through practical projects and interesting exercises, learn how to work with data using Python—no prior programming knowledge needed!

    Analyze vaccine efficacy, support for gun control, and more in this exercise-filled, beginner-friendly introduction to data science and Python, the leading programming language in the data science industry.


    This clear, concise introduction to the data-science discipline is for people with no programming experience. Using Python, a beginner-friendly language popular within the industry, the book presents a basic yet powerful set of tools and methods that allow you to do real work in data science as quickly as possible—everything from answering questions and guiding decision-making under uncertainty, to creating effective data visualizations that have a real impact. Concepts are explained in simple terms, and exercises in each chapter demonstrate the practical purposes of various skill sets. Practical and hands-on, the author’s clever organization of content follows the steps of a data-science project: posing and refining questions, cleaning and validating data, exploratory analysis and identifying relationships between variables, generating predictions, and designing visualizations that tell a compelling story. Upon finishing the book, you’ll be able to execute your own data projects from start to finish!

    Learn how to: 
    • Choose questions, data, and methods that go together
    • Find data online or collect it yourself
    • Clean and validate data
    • Explore datasets, visualizing distributions and relationships between variables
    • Model data and generate predictions
    • Communicating results effectively

      
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