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    Data Science and Analytics with Python
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      • Publisher's listprice GBP 48.99
      • 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.

        24 793 Ft (23 613 Ft + 5% VAT)
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    24 793 Ft

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    Product details:

    • Edition number 2
    • Publisher Chapman and Hall
    • Date of Publication 30 April 2025

    • ISBN 9781032772493
    • Binding Paperback
    • No. of pages512 pages
    • Size 235x191 mm
    • Weight 725 g
    • Language English
    • Illustrations 60 Illustrations, black & white; 10 Halftones, black & white; 50 Line drawings, black & white; 19 Tables, black & white
    • 700

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    Short description:

    Since the first edition of ?Data Science and Analytics with Python? we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This has led to book becoming a key textbook among practitioners and students.

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    Long description:

    Since the first edition of ?Data Science and Analytics with Python? we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook. In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI).


    This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy. Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play. This edition also addresses crucial issues of explainability, transparency, and fairness in AI. These topics have rightly gained significant attention in recent years. As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount. This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts.


    Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning. Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook. Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.

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    Table of Contents:

    1. Trials and Tribulations of a Data Scientist  2. Python: For Something Completely Different  3. The Machine that Goes ?Ping?: Machine Learning and Pattern Recognition  4. The Relationship Conundrum: Regression  5. Jackalopes and Hares: Clustering  6. Unicorns and Horses: Classification  7. Decisions, Decisions: Hierarchical Clustering, Decision Trees and Ensemble Techniques  8. Less is More: Dimensionality Reduction  9. Kernel Tricks up the Sleeve: Support Vector Machines  Appendix. Pipelines in Scikit-Learn  Bibliography  Index

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