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  • An Introduction To Python For Quantitative Finance: From Scratch To Productivity

    An Introduction To Python For Quantitative Finance: From Scratch To Productivity by Bilokon, Paul Alexander; Jacquier, Antoine; Mackie, Ewan;

      • GET 8% OFF

      • The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
      • Publisher's listprice GBP 90.00
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        42 997 Ft (40 950 Ft + 5% VAT)
      • Discount 8% (cc. 3 440 Ft off)
      • Discounted price 39 558 Ft (37 674 Ft + 5% VAT)

    42 997 Ft

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    Not yet published.

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Product details:

    • Publisher World Scientific
    • Date of Publication 28 June 2026

    • ISBN 9789811215728
    • Binding Hardback
    • No. of pages300 pp pages
    • Language English
    • 700

    Categories

    Long description:

    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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