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Lynn Matthay

Business Intelligence als Treiber der Geschäftsmodellentwicklung. Analyse und Anwendung am Beispiel des mittelständischen Unternehmens "Textiltrend"

AfricaTECH Book Series

From Data Chaos to Intelligent Value Chains

AI-Driven Data Supply Chain

Was steckt hinter den Algorithmen der künstlichen Intelligenz?

Künstliche Intelligenz und maschinelles Lernen halten zunehmend Einzug, in betrieblichen Anwendungen wie in der Forschung. Nicht alle Anwender und Anwenderinnen verfügen jedoch über einen mathematisch-naturwissenschaftlichen Hintergrund.

Dieses Buch richtet sich an Studierende, Promovierende, Anwenderinnen und Anwender aller Fachrichtungen sowie an alle, die verstehen möchten, was hinter maschinellem Lernen und KI steckt. Es vermittelt ein grundlegendes Verständnis der zentralen Ideen hinter maschinellem Lernen (ML). Die zugrundeliegenden Konzepte stehen im Vordergrund, und diese werden anhand konkreter Verfahren wie Entscheidungsbäumen, linearer und logistischer Regression, neuronalen Netzen sowie Clusteranalyse veranschaulicht. Fallstudien mit Beispielen in der Programmiersprache R zeigen exemplarisch praktische Anwendungsfelder. Ergänzende Übungen laden zur Vertiefung ein.

Das Buch richtet sich ausdrücklich auch an Leser und Leserinnen ohne spezielle mathematische Vorkenntnisse und ist mit Grundwissen aus der Schulmathematik verständlich. Für die praktischen Übungen und Anwendungsbeispiele sind Grundkenntnisse in R erforderlich.

Die Produktfamilie WissensExpress bietet Ihnen Lehr- und Lernbücher in kompakter Form. Die Bücher liefern schnell und verständlich fundiertes Wissen.

Was steckt hinter den Algorithmen der künstlichen Intelligenz?Künstliche Intelligenz und maschinelles Lernen halten zunehmend Einzug, in betrieblichen Anwendungen wie in der Forschung. Nicht alle Anwender und Anwenderinnen verfügen jedoch über einen mathematisch-naturwissenschaftlichen Hintergrund.Dieses Buch richtet sich an Studierende, Promovierende, Anwenderinnen und Anwender aller Fachrichtungen sowie an alle, die verstehen möchten, was hinter maschinellem Lernen und KI steckt.&nbsp;Es vermittelt ein grundlegendes Verständnis der zentralen Ideen hinter maschinellem Lernen (ML). Die zugrundeliegenden Konzepte stehen im Vordergrund, und diese werden anhand konkreter Verfahren wie Entscheidungsbäumen, linearer und logistischer Regression, neuronalen Netzen sowie Clusteranalyse veranschaulicht. Fallstudien mit Beispielen in der Programmiersprache R zeigen exemplarisch praktische Anwendungsfelder. Ergänzende Übungen laden zur Vertiefung ein.Das Buch richtet sich ausdrücklich auch an Leser und Leserinnen ohne spezielle mathematische Vorkenntnisse und ist mit Grundwissen aus der Schulmathematik verständlich. Für die praktischen Übungen und Anwendungsbeispiele sind Grundkenntnisse in&nbsp;R erforderlich.
 Die Produktfamilie 
 WissensExpress
 &nbsp;bietet Ihnen Lehr- und Lernbücher in kompakter Form. Die Bücher liefern schnell und verständlich fundiertes Wissen.

verständlich erklärt - mit Fallstudien in R

Grundprinzipien des maschinellen Lernens

The second edition of Advanced Data Science and Analytics with Python reflects the rapid transformation of artificial intelligence in recent years. While preserving its practical, modular structure, this edition significantly expands coverage of the techniques shaping modern AI practice.The deep learning chapter has been substantially broadened to include reinforcement learning and generative adversarial networks, alongside a fully developed exploration of transformer architectures. Generative AI now takes centre stage, with dedicated coverage of self-attention, BERT, GPT, large language model evaluation and API-based interaction. Emerging agentic systems are introduced as part of the evolving AI landscape. Natural language processing has been enhanced with word embeddings, contextual representations and vector search, while network analysis now includes graph representation learning and embedding techniques. The chapter on data product deployment has been strengthened with modern Core ML workflows and new coverage of on-device Foundation Models, bridging experimentation and production.Fully updated for the contemporary Python ecosystem, this edition equips practitioners with the tools and architectural understanding required to design, build and deploy intelligent systems in today's AI-driven world.Jesús Rogel-Salazar is a lead data scientist, founder of RogueLoop, working for companies such as Ortus Technologies, Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Imperial College Business School, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Advanced Data Science and Analytics with Python

Legal Data, Digital Assets and Smart Contracts for Capital Markets

A step-by-step guide to AI-native analytics systems for BI practitioners Building Business Intelligence Apps with AI provides a step-by-step guide for data analysts, data engineers, and BI developers who need to integrate AI into their analytics workflows. Written by Ryan Dolley, a data industry veteran with fifteen years of experience across BI development, consulting, and data vendor roles, this book shows how to partner with AI to deliver better data models, dashboards, and business queries while building AI-native experiences for end users. The book addresses data foundations, outsourcing repetitive tasks to AI, and scaling autonomous analytics systems that maintain accuracy and data governance. Readers learn practical steps for increasing individual and team effectiveness using AI while avoiding common pitfalls such as hallucination, data inaccuracy, and user abandonment. It delivers operating models that data departments need, covering everything from data blending and dashboard creation to personalization and responsiveness. Readers will also find: <ul> <li>Practical techniques for partnering with LLMs to accelerate data modeling, querying, and dashboard development across BI workflows</li> <li>Strategies to outsource repetitive analytics tasks to AI while maintaining rigorous data governance and accuracy standards</li> <li>Operating models for individuals and data departments to scale AI-native analytics systems that end users adopt</li> <li>Methods for building autonomous, personalized analytics experiences that address user expectations for speed and responsiveness</li> <li>Guidance on avoiding AI pitfalls including hallucination, data inaccuracy, and user abandonment in production analytics environments</li> </ul> Data analysts, data engineers, BI developers, and their managers will find in this book the specific skills and operating models needed to integrate AI into daily analytics work. It equips practitioners to build accurate, autonomous analytics systems while strengthening data governance and accelerating delivery across their teams.

Building Business Intelligence Apps with AI

- 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 the 3rd edition: Chapters on Vibe Coding and AI Agents

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 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.
- AI Agents and Vibe Coding: Using AI in practice and creating new solutions.
- Presenting your Results: Essential presentation techniques for data scientists.

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

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

Generate Value from Data with Machine Learning and Data Analytics

The Handbook of Data Science and AI

SpringerBriefs in Service Science

This book explores the transformative potential of parallel intelligence in enhancing humanitarian services for sustainability, with a focus on leveraging the latest advancements in artificial intelligence. It brings together cutting-edge technologies like agentic intelligence, autonomous intelligence, Retrieval-Augmented Generation (RAG), and Search-Augmented Generation and Extension (SAGE), and along with digital human technologies, to reimagine and re-optimize service systems across a range of sectors. It examines how these technologies can transform service systems across diverse sectors, including education, healthcare, emergency management, agriculture, tourism, and ecology. By integrating real-time data, AI models, and feedback loops, the book outlines how these parallel systems can improve the effectiveness, resilience, and sustainability of services. It also highlights the role of blockchain, smart contracts, and Decentralized Autonomous/Agentic Organizations (DAO) in optimizing distributed humanitarian operations. As AI continues to advance, the author raises the crucial question of what the best model for sustainable and reliable humanitarian services should look like. Through this exploration, he argues that parallel intelligence offers new, innovative solutions to address global challenges, making service systems more adaptive and efficient.This book appeals to a broad audience, including academics, researchers, data scientists, practitioners, students, educators, and general public interested in the intersection of service science and AI. It offers critical insights and practical strategies, making it a valuable resource for those working in scientific research for services, LLMs, and the development of intelligent systems.

New Service with New AI

Parallel Intelligence for Humanitarian Services

Mastering Business Analytics

Data Analytics for Strategic Decisions explores how businesses can harness the power of data, technology, and analytics to make strategic decisions that drive growth and innovation. It provides a comprehensive guide to integrating business strategy with data-driven methodologies, offering practical tools and insights for modern organizations.Readers will gain a deep understanding of how to transform data into actionable insights, improving decision-making across marketing, finance, operations, and strategy. The book combines theoretical foundations with real-world applications, including case studies, datasets, and practical exercises. It also addresses emerging topics like AI, predictive analytics, and data governance, ensuring readers are equipped with cutting-edge knowledge and skills.This book is designed for undergraduate and master's students in Business Administration, Business Analytics, and Digital Transformation, as well as professionals seeking to enhance their data-driven decision-making capabilities. It is ideal for courses in Business Analytics, Data Analytics for Business, Business Intelligence, and Digital Transformation.The book includes supplementary materials such as datasets for practical exercises, case studies, and discussion questions, making it suitable for both classroom and online learning environments.

Fundamentals of Business Technology and AI Integration

Data Analytics for Strategic Decisions

Essentials

Messen und Beurteilen spielen in den empirischen Sozialwissenschaften, den Gesundheitswissenschaften und in der Informatik eine zentrale Rolle. Dabei sind Hintergründe zu Messtheorien, Messskalen und Gütekriterien sowie zu Messfehlern wesentlich, um diagnostische Maßnahmen beurteilen und Krankheitsbilder interpretieren zu können. Durch die zunehmende Verbreitung datengetriebener Verfahren sind weitergehende Paradigmen von KI-Anwendungen sowie ethischen Limitationen algorithmischer Messverfahren in diesen Kontexten zu berücksichtigen.
Der Inhalt:&nbsp;

<ul>
<li>Messen und Beurteilen in den Sozialwissenschaften, im Gesundheitswesen und bei datengetriebenen Verfahren</li>
<li>Messtheorien und Messfehlerkonzepte</li>
<li>Interpretation medizinischer Daten</li>
<li>Neue Paradigmen bei datengetriebenen Verfahren. Herausforderungen und ethische Limitationen algorithmischer Messverfahren</li>
</ul>
&nbsp;

Messen und Beurteilen spielen in den empirischen Sozialwissenschaften, den Gesundheitswissenschaften und in der Informatik eine zentrale Rolle. Dabei sind Hintergründe zu Messtheorien, Messskalen und Gütekriterien sowie zu Messfehlern wesentlich, um diagnostische Maßnahmen beurteilen und Krankheitsbilder interpretieren zu können. Durch die zunehmende Verbreitung datengetriebener Verfahren sind weitergehende Paradigmen von KI-Anwendungen sowie ethischen Limitationen algorithmischer Messverfahren in diesen Kontexten zu berücksichtigen.Der Inhalt:&nbsp;<ul><li>Messen und Beurteilen in den Sozialwissenschaften, im Gesundheitswesen und bei datengetriebenen Verfahren</li><li>Messtheorien und Messfehlerkonzepte</li><li>Interpretation medizinischer Daten</li><li>Neue Paradigmen bei datengetriebenen Verfahren. Herausforderungen und ethische Limitationen algorithmischer Messverfahren</li></ul>&nbsp;

Einführung in Messtheorie, Messfehler und Beurteilungsaspekte

Messen und Beurteilen in interdisziplinärer Betrachtung

In an era where organizations increasingly rely on data and intelligent technologies to guide strategic decisions, workforce management is undergoing a profound transformation. 
 Data-Driven Sustainable Workforce Analytics: The Triple Intelligence Framework
 presents a comprehensive exploration of how artificial intelligence, advanced analytics, and human-centered management practices are reshaping the future of work. 
 The book introduces the concept of Triple Intelligence, highlighting the strategic integration of human intelligence, artificial intelligence, and collective intelligence to support sustainable organizational transformation and responsible workforce governance. Structured into five thematic parts, the book examines intelligent HR ecosystems, predictive recruitment strategies, machine learning in people analytics, employee engagement, and sectoral applications such as healthcare and smart manufacturing. It also addresses critical ethical and governance issues, including ESG-driven workforce strategies, diversity and inclusion analytics, responsible artificial intelligence, and emerging debates on digital workplace monitoring.Featuring contributions from scholars across 19 countries spanning five continents, this volume provides diverse global perspectives and offers valuable insights for researchers, practitioners, and policymakers seeking to build data-driven, ethical, and sustainable workforce systems in the age of intelligent organizations.&nbsp;

The Triple Intelligence Framework

Data-Driven Sustainable Workforce Analytics

Asset Analytics

This book explores the transformative potential of business analytics in the modern business landscape, offering a practical guide to leveraging data for strategic decision-making. With companies generating vast amounts of data, there is a growing need for professionals who can interpret and analyze this information to derive actionable insights. This book equips readers with essential tools and techniques to become proficient in business analytics, emphasizing the application of data to solve real-world business problems and drive profitability. Encompassing various facets of a business, including supply chain analytics, HR analytics, influencer networks, Impact of machine learning and AR/VR, sensitivity analysis, data story telling etc. and even empowering customers with the help of data driven approach, this book tries to add value in managerial decision making. &nbsp;Focusing on the latest trends and advancements, this book demonstrates how analytics can provide a competitive edge by converting large datasets into meaningful insights. Through real-world examples and case studies, it shows how businesses can enhance market share and deliver greater returns to shareholders. Designed for business professionals, analysts, and decision-makers, this book is an essential resource for anyone looking to improve their company’s performance or advance their career with cutting-edge analytical skills.

Sharpening beyond Data

Business Analytics and Managerial Decision Making

Business Intelligence als Treiber der Geschäftsmodellentwicklung. Analyse und Anwendung am Beispiel des mittelständischen Unternehmens "Textiltrend"

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