Business Analytics with R

A Tidy Approach
Erscheinungsjahr 2027
ISBN: 978-3-032-32077-3
Verlag: Palgrave Macmillan

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

Machine learning has become an essential component of modern business analytics -- driving insights, optimizing operations, and supporting strategic, data-informed decisions. This textbook offers a hands-on and accessible introduction to applying machine learning in business settings using R, with a strong emphasis on the tidy ecosystem. Key packages such as tidyverse, tidymodels, and tidytext provide a cohesive and efficient framework for data analysis, modeling, and communication.

Balancing theoretical foundations with applied practice, the book introduces mathematical ideas in a clear, intuitive manner, always anchored in realistic business scenarios. Suitable for the classroom and independent research, readers will gain the confidence and tools to apply the tidy ecosystem effectively, from data preparation to modeling to interpretation, enabling reproducible, interpretable, and impactful analytics in dynamic business environments.

Zielgruppe

Graduate

Autoren/Hrsg.

Chen, Bowei

Huang, Tianyuan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Wirtschaftswissenschaften Betriebswirtschaft Management Forschung & Entwicklung (F&E), Innovation

Inhaltsverzeichnis

1. Introduction.- 2. R and Its Basic Syntax.- 3. Data I/O and Management.- 4. Data Operations.- 5. Working with Special Data Types.- 6. Descriptive Statistics.- 7. Data Visualization.- 8. Linear Regression.- 9. Polynomial and Regularized Regression.- 10. Logistic Regression.- 11. Neural Networks.- 12. K-Nearest Neighbors.- 13. Naive Bayes.- 14. Decision Trees.- 15. Tree-based Ensemble Methods.- 16. Support Vector Machines.- 17. Cluster Analysis.- 18. Principal Component Analysis.- 19. Social Network Analysis.- 20. Sentiment Analysis.- 21. Topic Modeling.- 22. Explainable Artificial Intelligence.

Über Autor(innen)

Bowei Chen is a Professor of Business Analytics and Artificial Intelligence at the Adam Smith Business School, University of Glasgow, UK, and the Founding Programme Director of the MSc in Business Analytics programme. He received his PhD in Computer Science from the University College London. His research interests include business analytics, artificial intelligence, machine learning, and data science applications in management, finance, and the social sciences. He has published in leading academic conferences, journals, and books in these fields, and has undertaken international research collaborations and academic appointments across different institutions.

Tianyuan Huang is a Lecturer of Data Science and Big Data Technologies at the School of Data Science, Zhejiang University of Finance & Economics, China. He earned his PhD from Fudan University and has authored three R programming books in China. His current research mainly focuses on quantitative science studies, and he is also dedicated to leveraging data science to rapidly build domain expertise and advance scientific knowledge discovery. In addition to his publications, he supports the R community by maintaining several CRAN packages, such as akc for automated knowledge classification and tidyfst for efficient tidy data manipulation.