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Financial Data Science with Python

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Paperback / softback
261 Pages
31 May 2025
$83.20
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In today's finance industry, data-driven decision-making is essential. Financial Data Science with Python: An Integrated Approach to Analysis, Modeling, and Machine Learning bridges the gap between traditional finance and modern data science, offering a comprehensive guide for students, analysts, and professionals. This book equips readers with the tools to analyze complex financial data, build predictive models, and apply machine learning techniques to real-world financial challenges. Beginning with foundational Python concepts, the author covers essential topics like data structures, object-oriented programming, and key libraries such as NumPy and Pandas. The book advances into more complex areas, including financial data processing, time series analysis with ARIMA and GARCH models, and both supervised and unsupervised machine learning methods tailored to finance. Practical techniques like regression, classification, and clustering are explored in a financial context. A key feature is the hands-on approach. Through real-world examples, projects, and exercises, readers will apply Python to tasks like risk assessment, market forecasting, and financial pattern recognition. All code examples are provided in Jupyter Notebooks, enhancing interactivity. Whether you're a student building foundational skills, a financial analyst enhancing technical expertise, or a professional staying competitive in a data-driven industry, this book offers the knowledge and tools to succeed in financial data science.

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    Product Details
    ISBN-13
    9781637428252
    Author
    Dr Haojun Chen
    Publisher
    BUSINESS EXPERT PRESS
    Published
    31 May 2025
    Format
    Paperback / softback
    Pages
    261

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    $83.20
    Ships in 3-5 business days

    Delivery estimate

    Hurry up! Current stock:

    Financial Data Science with Python

    $83.20

    Description

    In today's finance industry, data-driven decision-making is essential. Financial Data Science with Python: An Integrated Approach to Analysis, Modeling, and Machine Learning bridges the gap between traditional finance and modern data science, offering a comprehensive guide for students, analysts, and professionals. This book equips readers with the tools to analyze complex financial data, build predictive models, and apply machine learning techniques to real-world financial challenges. Beginning with foundational Python concepts, the author covers essential topics like data structures, object-oriented programming, and key libraries such as NumPy and Pandas. The book advances into more complex areas, including financial data processing, time series analysis with ARIMA and GARCH models, and both supervised and unsupervised machine learning methods tailored to finance. Practical techniques like regression, classification, and clustering are explored in a financial context. A key feature is the hands-on approach. Through real-world examples, projects, and exercises, readers will apply Python to tasks like risk assessment, market forecasting, and financial pattern recognition. All code examples are provided in Jupyter Notebooks, enhancing interactivity. Whether you're a student building foundational skills, a financial analyst enhancing technical expertise, or a professional staying competitive in a data-driven industry, this book offers the knowledge and tools to succeed in financial data science.

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