Business Analytics and Artificial Intelligence

An Advanced Guide to Data-Driven Decision Making
Erscheinungsjahr 2027
ISBN: 978-3-032-39508-5
Verlag: Palgrave Macmillan

Buch, Englisch, Format (B × H): 155 mm x 235 mm

In today’s AI-driven business environment, the ability to translate data into sound decisions is increasingly important. This textbook introduces the foundations and practical applications of business analytics, machine learning, and artificial intelligence. Readers learn how to explore and visualize business data, build and evaluate predictive models, and apply analytical methods to support business decisions.

The book progresses from core concepts such as data visualization, optimization, model evaluation, supervised learning, and unsupervised learning to advanced topics in deep learning, computer vision, generative AI, text mining, natural language processing, and large language models. It also addresses the limitations and ethical challenges of AI. Step-by-step explanations, business examples, and Python exercises help readers connect analytical concepts with real-world applications.

Designed for students and professionals with little or no prior experience in programming or machine learning, the book develops skills progressively from basic concepts to advanced methods. It prepares readers to select, implement, interpret, and evaluate AI-based analytical approaches for a wide range of business problems.

Zielgruppe

Graduate

Autoren/Hrsg.

Lee, Hyunhwan “Aiden”

Song, Reo

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Marketing
• Wirtschaftswissenschaften Betriebswirtschaft Management
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung

Inhaltsverzeichnis

Part I. Business analytics, machine learning, and artificial intelligence (AI).- AI and machine learning: The foundations of modern business analytics.- Data visualization.- The limitations and ethical issues of AI.- Gradient descent: The foundation of machine learning optimization.- Model evaluation.- Part II. Supervised Learning.- Linear regression.- Regularization.- Logistic regression.- Support vector machines (SVMs).- K-Nearest neighbors (KNNs).- Tree-based methods.- Part III. Unsupervised Learning.- Clustering analysis.- Dimensionality reduction.- Foundations of Deep Learning.- Vision Models.- Sequence and Attention Models.- Autoencoders and Representation Learning.- Classic Generative Models: VAEs and GANs.- Modern Generative Models: Diffusion Models and Foundation Models.- Part V. Text Mining and Natural Language Processing.- Text Pre-processing and Data Quality.- Embeddings and Similarity.- Topic Modeling and Theme Discovery.- Sentiment and Text Classification.- Transformer and Large Language Models.- Retrieval-Augmented Text Mining.- Appendix.- Google Colab.- Python basics.- SQL for Business Analytics.- NoSQL and Document-Oriented Databases.

Über Autor(innen)

Hyunhwan “Aiden” Lee is Assistant Professor of Marketing at California State University, Long Beach, USA. His research focuses on marketing analytics, brand and content analytics, and the application of artificial intelligence to business and marketing problems. His work uses machine learning, natural language processing, computer vision, location-based data analysis, large-scale data analysis, and mathematical modeling.

Reo Song  is Professor of Marketing and Director of the M.S. in Marketing Analytics program at California State University, Long Beach. His research focuses on machine learning, AI, business analytics, and digital marketing, with publications in leading journals including   and  . He has advised organizations on AI and machine learning applications, delivers frequent invited talks on AI and business analytics, and contributes regularly to newspaper columns on AI and digital transformation.