The Handbook of Data Science and AI

Produktbeschreibung

- 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. - BONUS in print edition: E-Book inside Data Science, Big Data, Artificial Intelligence and Generative AI 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. Using exercises and real-world examples, it will show you how to apply data science methods, build data platforms, and deploy data- and ML-driven projects to production. It will help you understand - and explain to various stakeholders - how to generate value from such endeavors. Along the way, it will bring essential data science concepts to life, including statistics, mathematics, and machine learning fundamentals, and explore crucial topics like critical thinking, legal and ethical considerations, and building high-performing data teams. Readers of all levels of data familiarity - from aspiring data scientists to expert engineers to data leaders - will ultimately learn: how can an organization become more data-driven, what challenges might it face, and how can they as individuals help make that journey a success. The team of authors consists of data professionals from business and academia, including data scientists, engineers, business leaders and legal experts. All are members of the Vienna Data Science Group (VDSG), an NGO that aims to establish a platform for exchanging knowledge on the application of data science, AI and machine learning, and raising awareness of the opportunities and potential risks of these technologies. WHAT'S INSIDE // - Critical Thinking and Data Culture: How evidence driven decision making is the base for effective AI. - Machine Learning Fundamentals: Foundations of mathematics, statistics, and ML algorithms and architectures - Natural Language Processing and Computer Vision: How to extract valuable insights from text, images and video data, for real world applications. - Foundation Models and Generative AI: Understand the strengths and challenges of generative models for text, images, video, and more. - ML and AI in Production: Turning experimentation into a working data science product. - Presenting your Results: Essential presentation techniques for data scientists. 

Kritik

"Die Neuauflage zeichnet sich durch ihre Aktualität aus, indem sie die neuesten Entwicklungen und Trends berücksichtigt. Trotz der Komplexität der Themen ist das Buch verständlich geschrieben und eignet sich sowohl für Einsteiger als auch für erfahrene Praktiker. Es verbindet theoretische Grundlagen mit praktischen Anwendungen und legt Wert auf Interdisziplinarität, indem es Konzepte aus verschiedenen Fachbereichen verknüpft und viele Beispiele aus der realen Welt liefert. Damit ist das Handbuch eine wertvolle Ressource für Leser, die ihr Wissen im Bereich Data Science und KI vertiefen oder erweitern möchten, und ein unverzichtbares Werk für alle, die sich professionell mit Data Science und KI beschäftigen. Für Dezember 2024 plant der Verlag eine erweiterte deutschsprachige Neuauflage des Handbuchs." dotnetrpro, November 2024 

Autoreninfo

The team of authors consists of data experts from business and academia. The spectrum ranges from executives to data engineers who create production systems, to data scientists who generate value from data. All authors are members of the Vienna Data Science Group (VDSG), an NGO that aims to establish a platform for exchanging knowledge on the application of data science, AI and Machine Learning and raising awareness of the opportunities and potential risks of these technologies.