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Autor(en): 
  • Paul Alexander Bilokon
  • Jacquier Antoine
  • Ewan Mackie
  • Aitor Muguruza
  • An Introduction To Python For Quantitative Finance: From Scratch To Productivity 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 7-14 Tagen versandfertig
    Veröffentlichung:  Juni 2026  
    Genre:  EDV / Informatik 
     
    COMPUTERS / Data Science / Data Modeling & Design / COMPUTERS / Languages / Python / COMPUTERS / Machine Theory / Data Capture & Analysis / Data capture and analysis / machine learning
    ISBN:  9789811215728 
    EAN-Code: 
    9789811215728 
    Verlag:  World Scientific Publishing 
    Einband:  Gebunden  
    Sprache:  English  
    Seiten:  300 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book is written for both newcomers and experienced practitioners working at the intersection of data science, machine learning, and finance. It is designed to allow readers with no formal prerequisites to enter these fields with confidence, while also providing sufficient depth to be valuable to professionals. Beginning with a gentle introduction to Python, the book gradually progresses to more advanced language features and the mathematical foundations required to understand key models in quantitative finance. Throughout, the emphasis is on developing both conceptual understanding and practical skills. The material strikes a careful balance between the mathematics underpinning modern financial models and the practical considerations of data science and machine learning. Concepts are introduced and reinforced through hands-on case studies based on real financial datasets, enabling readers to gain experience working with realistic data and workflows. The contents of this book have been refined over many years of teaching to students and practitioners with diverse backgrounds at Imperial College London and the Thalesians Intensive Summer School in Artificial Intelligence, and reflects both academic rigor and real-world relevance.

      



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