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The Handbook of Data Science and AI - Stefan Papp, Zoltan Toth, Katherine Munro, Wolfgang Weidinger, Danko Nikolic, Barbora Antosova Vesela, Karin Bruckmüller, Annalisa Cadonna, Jana Eder, Jeannette Gorzala, Gerald A. Hahn, Georg Langs, Roxane Licandro, Christian Mata, Sean McIntyre, Mario Meir-Huber, György Móra, Manuel Pasieska, Victoria Rugli, Rania Wazir, Günther Zauner

The Handbook of Data Science and AI

Generate Value from Data with Machine Learning and Data Analytics
Buch | Hardcover
1000 Seiten
2026 | 3., aktualisierte Auflage
Hanser Publications (Verlag)
978-1-56990-515-9 (ISBN)
CHF 139,95 inkl. MwSt
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A comprehensive overview of the various fields of application of data science and artificial intelligence.
Case studies from practice to make the described concepts tangible.
Practical examples to help you carry out simple data analysis projects.
New in 3rd edition: AI Agents, RAGs, MCP, Vibe Coding
BONUS in print edition: E-Book inside

Data Science, Big Data, and Artificial Intelligence are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them. Featuring:
The book approaches the topic of data science from several sides. Crucially, it will show you how to build data platforms and apply data science tools and methods. Along the way, it will help you understand - and explain to various stakeholders - how to generate value from these techniques, such as applying data science to help organizations make faster decisions, reduce costs, and open up new markets. Furthermore, it will bring fundamental concepts related to data science to life, including statistics, mathematics, and legal considerations. Finally, the book outlines practical case studies that illustrate how knowledge generated from data is changing various industries over the long term.

- Mathematics basics: Mathematics for Machine Learning to help you understand and utilize various ML algorithms.
- Machine Learning: From statistical to neural and from Transformers and GPT-3 to AutoML, we introduce common frameworks for applying ML in practice
- Natural Language Processing: Tools and techniques for gaining insights from text data and developing language technologies
- Computer vision: How can we gain insights from images and videos with data science?
- Modeling and Simulation: Model the behavior of complex systems, such as the spread of COVID-19, and do a What-If analysis covering different scenarios.
- ML and AI in production: How to turn experimentation into a working data science product?
- Presenting your results: Essential presentation techniques for data scientists
Erscheint lt. Verlag 17.4.2026
Verlagsort München
Sprache englisch
Themenwelt Mathematik / Informatik Informatik Datenbanken
Schlagworte AI agents • Business Intelligence • Chatbots • computer vision • data engineering • Data Scientist • Data strategy • Deep learning • machine learning • MLOps • MLSecurity
ISBN-10 1-56990-515-0 / 1569905150
ISBN-13 978-1-56990-515-9 / 9781569905159
Zustand Neuware
Informationen gemäß Produktsicherheitsverordnung (GPSR)
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